A Peer-to-Peer Matching System for Grocery Home...

I

A Peer-to-Peer Matching System for Grocery Home Delivery

Amin Sazavar

Department of

Civil Engineering and Applied Mechanics

McGill University, Montréal

April 2014

A thesis submitted to McGill University in partial fulfillment of the requirements

of the degree of Master of Engineering – Thesis

© Amin Sazavar, 2014

I

TABLE OF CONTENTS

ABSTRACT………………………………………………………………………. I

RESUMÉ………………………………………………………………….…….… I

DEDICATION……………………………………………………………..…...… I

ACKNOWLEDGEMENTS…………………………………………………....… I

CHAPTER 1. INTRODUCTION …………………………………….……….....1

1.1) Introduction …………………………………………………………………………….....1

1.2) Review of e-grocery operations ………………………………………………………......2

1.3) Research objectives …………………………………………………………………….....3

1.4) Delivery concept ……………………………………………………………………….....4

1.4.1) Definitions ………………………………………………………………………………..…4

1.4.2) Delivery system environment ……………………………………………………………..5

1.5) Importance of the research …………………………………………………………….….8

1.6) Outline of the research ……………………………………………………………………9

CHAPTER 2. LITERATURE REVIEW ……………………………………....11

2.1) Logistics and supply chain management ………………………………………………..11

2.2) Traditional grocery supply chain ……………………………………………………..…11

2.3) E-Grocery supply chain ………………………………………………………………... 13

2.3.1) E-Grocery supply chain structure ……………………………………………………...13

2.3.2) E-grocery operations and motivations ………………………………………………...14

II

2.4) Background for modelling home delivery operations ………………………………..…16

2.4.1) General ………………………………………………………………………………….…16

2.4.2) Vehicle Routing Problem (VRP) …………………………………………………….…16

2.4.3) Solutions to the VRP ……………………………………………………………………..20

CHAPTER 3. METHODOLOGY …………………….…………………..……22

3.1) Introduction ……………………………………………………………………………...22

3.2) Delivery scenarios …………………………………………………………………….....22

3.3) Modelling of delivery methods ……………………………………………………….…25

3.3.1) Regular shopping ……………………………………..……………………………….....25

3.3.2) Matching system ……………………………………………...…………………………..27

3.3.3) Truck delivery System …………………………………………………….……….........32

CHAPTER 4. CASE STUDY AND SENSITIVITY ANALYSIS …………….36

4.1) Study network (Sioux Falls network) ………………………………………………...…36

4.2) Carrier’s location …………………………………………………………………..…....37

4.3) Client’s location ……………………………………………………………………...….37

4.4) Sensitivity analysis ………………………………………………………………………38

4.4.1) Number of stores in the network ……………………………………………………....38

4.4.2) Client/carrier ratio …………………………………………………………………...…38

4.4.3) Truck capacity constraints ………………………………………………………….….39

CHAPTER 5. RESULTS …………………………………………………...…. 40

III

5.1) Introduction ………………………………………………………………….……..……40

5.2) Sensitivity analysis: client/carrier ratio ……………………………………………….…40

5.2.1) Scenario 1: Regular shopping for all clients and carriers (R) ………………..……40

5.2.2) Scenario 2 : Truck delivery fleet, regular shopping for all carriers (TR) …..……42

5.2.3) Scenario 3: Matching system, regular shopping for un-matched (non-delivery)

carriers and clients (MR) …………………………………………………………………..……45

5.2.4) Scenario 4: Matching system, truck delivery, regular shopping for un-matched

carriers (MTR) ………………………………………………………………………..…48

5.3) Comparison of scenario performance ………………………………………………......50

5.4) Analysis of the impact of the changes in the number of stores ………………………...54

5.4.1) Scenario 1: Regular shopping for all clients and carriers (R) …………………......54

5.4.2) Scenario 2: Truck delivery fleet, regular shopping for all carriers (TR)……..…...56


carriers and clients (MR) ………………………………………………………..……...58


carriers (MTR) ……………………………………………………………………………63

5.5) Analysis of the impact of the changes in trucks load capacity on scenarios with truck

delivery fleet …………………………………………………………………………….67

CHAPTER 6. CONCLUSION ……………………………………………..…...71

6.1) Introduction …………………………………………………………………...………....71

6.2) How the research objectives are satisfied ………………………………………..……...72

6.3) Research significance…………………………………………………………………….72

IV

6.4) Limitations and recommendations for future research .....................................................73

REFERENCES ……………………………………………………………….…75

APPENDICES …………………………...………………………………………82

V

Abstract

This thesis introduces a new delivery method for electronic grocery shopping. This method

involves a peer-to-peer matching system in which customers (carriers) who shop at a grocery

store are assigned other customers (clients) who have ordered groceries to be delivered to their

home. Carriers deliver groceries to the clients’ homes, in return for an incentive. The matching

system matches carriers to clients to minimize the total travel time on the network.

In order to examine how this new method of delivery performs, we compare it with the current

methods of grocery delivery: regular grocery shopping, where every customer visits the grocery

store, and truck delivery service, where groceries are delivered to customers who have ordered

them. Four different scenarios are designed and simulated. A set of performance measures is then

developed in order to evaluate the different scenarios.

The matching system is simulated by employing a mixed-integer optimization method and

solved by the IBM-CPLEX add-on in Matlab. The truck delivery service is presumed to be a

multi-depot split-delivery truck delivery service and is simulated by using a partial inference of a

multi-depot split-delivery vehicle routing problem heuristic. The scenarios are simulated and

examined on a hypothetical network –The Sioux Falls traffic network.

The most important finding from the results is that the new delivery system – the matching

system – performs well in terms of total travel time in the network and has the potential to be

implemented alongside the two other current methods of grocery shopping. The matching system

reduces the travel time in the network compared to regular grocery shopping, but does not

VI

outperform truck home delivery service. The performance of the matching system is found to be

the greatest when the number of carriers is equal to the number of clients.

The matching system produced acceptable results in the study presented in this thesis; however,

the need for a more detailed investigation is obvious to be able to judge its privileges to the

regular truck delivery service.

VII

RESUMÉ

Cette thèse propose une nouvelle méthode de livraison pour l'épicerie en ligne. Cette méthode

implique un système d'appariement de pairs dans laquelle les consommateurs (livreurs) qui

achètent à l'épicerie sont liés à d'autres consommateurs (clients) qui ont commandé en ligne une

épicerie à être livrée à leur domicile. Les livreurs livrent l'épicerie au domicile des clients, en

échange d'une rétribution. Le système de recherche assigne les livreurs aux clients dans le but de

minimiser le temps total du voyage sur le réseau.

Afin d'évaluer comment cette nouvelle méthode de livraison performe, on le compare avec les

méthodes actuelles de livraison d'épicerie: achats réguliers en épicerie, où chaque client se rend à

l'épicerie et où la livraison est effectuée par camion et où l'épicerie est livrée à des clients qui les

ont commandé. Quatre scénarios différents sont conçus et simulés. Un ensemble de mesures de

la performance est ensuite développé afin d'évaluer les différents scénarios.

Le système d'appariement est simulé en utilisant un procédé d'optimisation en nombres entiers et

résolu par l' IBM - CPLEX module dans Matlab. Le service de livraison par camion est présumé

être une fraction de la prestation de services de livraison de camion multi - dépôt et est simulé en

utilisant une déduction partielle d'un multi- dépôt véhicule split- livraison problème de routage

heuristique. Les scénarios sont simulés et examinés sur un réseau hypothétique - Le réseau de

circulation Sioux Falls.

Le point le plus important dans l'ensemble de ces résultats est que le nouveau système de

livraison - le système de correspondance - se comporte bien en termes de temps total de voyage

VIII

dans le réseau et a le potentiel pour être mis en œuvre aux côtés des deux autres méthodes

actuelles de l'épicerie. Le système d'appariement réduit le temps de voyage dans le réseau par

rapport à l'épicerie régulière, mais ne surpasse pas un service de livraison par camion. La

performance du système d'appariement se trouve être la plus efficace lorsque le nombre de

livreurs est égal au nombre de clients.

Le système d'appariement a produit des résultats acceptables dans l'étude présentée dans cette

thèse, mais la nécessité d'une enquête plus approfondie est évident pour être en mesure de juger

de ses privilèges pour le service régulier de livraison par camion.

IX

DEDICATION

To

Saba, Hamed, Maman and Baba

X

ACKNOWLEDGEMENTS

I am deeply indebted to my supervisors Dr. Marianne Hatzopoulou from McGill University and

Dr. Matthew Roorda from University of Toronto for their advice, support, encouragement and

friendship during my graduate studies. I wish all the best to them and their families.

1

CHAPTER 1

INTRODUCTION

1.1) Introduction

Groceries are the most universal commodity and there is intense competition between

supermarkets, driving them to seek new technologies and methods of streamlining both their

supply chains and their marketing strategies. The internet is the element that can help link

customers with grocery stores from their homes and integrate the logistics and supply chains

with sales (Boyer et al. 2005). The developments that are brought by the internet and information

technology have been helpful in the initiation of new businesses and service concepts. The

grocery business is an important sector with a considerable market penetration in today’s society.

One of the important aspects of the grocery business is the way consumers gain access to

groceries. Basically, there are two different methods for consumers to access groceries; regular

grocery shopping and truck delivery following online shopping (e-grocery). E-grocery and the

truck delivery system is the newer method and is making its way into the households shopping

market: however, it has some drawbacks. Selling groceries online means incurring additional

costs and fees which may be higher than what customers are willing to pay for the delivery

service (Galante et al. 2013), and also significant investments are needed for the distribution

infrastructure (Kempiak and Fox 2002). The retail food industry has been forced to adapt and to

use new technologies in order to increase its efficiency, thus it is developing new business

practices and relationships with suppliers and customers (Kinsey and Ashman 2000). It can be

concluded that innovation in this business, especially in delivery operations, is essential. As the

need is felt for new delivery systems, we witness a range of innovations. For example, some

2

French retailers have launched a service where customers can order their groceries online and

pick them up at a store (Galante et al. 2013).

In this thesis, a new method of delivery for e-grocery is proposed and simulated. The

performance of the new delivery method is compared against the regular shopping and the truck

delivery system. These three methods of delivery are simulated and run on a hypothetical traffic

network and their performances are judged according to their travel times. This new service

offers progress and greater capacity in the grocery delivery and grocery business in general.

Currently e-grocery is an important part of the grocery business and it seems that the next

developments in the grocery business will be in this field (e-grocery).

1.2) Review of e-grocery operations

With the emergence of e-commerce and the expansion of access to the internet, many consumers

are attracted to the practicality of e-grocery, to the extent that it could erode the traditional

methods of shopping for groceries (Hean Tat Keh and Shieh 2001).The growth in electronic

retailing business is fast. Annual growth rates in consumer electronic retailing businesses were

over 100% from 1995 to 2006 (Cullinane et al. 2008). E-grocery includes the ordering of

commodities via the internet and the delivery of the ordered commodities to the customer.

According to Statistics Canada, in 2010, 8 out of 10 Canadian households (79%) had access to

the Internet. Grocery shopping is one of the largest categories of electronic retailing business.

Implementation of the new services requires a desire to change the current scenarios and models

among various stakeholders; namely the customers, retailers, wholesalers, manufacturers and the

supply chain service providers.

3

In the regular grocery supply chain, goods are delivered to the store by the supply chain service

providers and customers do the shopping and delivery on their own. Hence, there are no costs for

the store associated with the delivery operations. In the e-grocery service, however, significant

costs are incurred in picking and packing the goods ordered, and transportation for home

delivery (@Your Home 2001).

Picking and packing operations are done in two ways. The first method is called a channel

model, in which these operations take place in a dedicated distribution centre. The second

method is the intermediary method, in which these operations take place using the current store

facilities and equipment (Bartolotta, 1998; Dagher et al., 1998; Heikkilä et al., 1998; Holmström

et al., 1999; Kämäräinen et al., 2001).

The other major cost is the home delivery transportation, which is the Achilles heel of the e-

grocery business. Improving the logistical efficiency is one of the most important steps toward

profitability. Grocery products, in particular, need to be handled under appropriate conditions to

preserve their quality; hence vehicles with temperature controlled storage are needed and this

imposes high costs on the business.

1.3) Research objectives

As just stated, home delivery is a major problem in the e-grocery business and this problem is

not going to diminish as this business is expected to expand in future. Studies show that there is

latent demand for e-grocery shopping. In France, 33% of consumers who have never used e-

grocery services would begin to do so within the next six months if the service was available in

4

their area. That is why the need for a new delivery concept which does not impose extra delivery

fees on consumers, requires little infrastructure, and has the potential to expand quickly, is

needed (Galante et al. 2013).

Therefore the first objective of this thesis is to introduce a new method of delivery which has the

potential to improve the delivery system in this business. The second objective is to analyze and

compare the efficiency of different e-grocery delivery methods in terms of travel time. This

thesis seeks to answer the following major questions:

What are the levels of different e-grocery delivery methods in terms of travel time?

In which conditions is each of the e-grocery methods more feasible?

1.4) Delivery concept

As discussed in section 1.2, home delivery operations play a crucial role in an e-grocery business

and the need for proper delivery systems is acute.

1.4.1) Definitions

The delivery concept rests on a number of definitions presented herein: Client: The customer

who places an order for groceries via the internet and expects to receive the ordered groceries,

delivered to his or her specified address.

Carrier: The customer who does his or her shopping, on his/her own effort, by visiting the

grocery store and is willing to also deliver groceries to other clients.

Customers: The group of all of the clients and carriers in a network.

5

Shopping tour: A series of sequential trips that lead to shopping and/or delivery of groceries for a

customer in the network.

Best store for Customer/Carrier/Client: The store that provides the shortest overall distance for a

customer while traveling on a shopping tour.

Initial location: Every shopping tour starts from an initial location.

Final location: Every shopping tour ends at a final location.

Client/Carrier Ratio:

in a network is called the client/carrier ratio.

1.4.2) Delivery system environment

The new delivery system that we propose here operates in a network composed of numerous

zones (nodes). Customers are scattered in the network and are able to travel from one zone to any

other zone. There are several stores of a particular grocery business company stationed in the

zones; however there is not necessarily a store in every single zone of the network. The customer

who is willing to enroll in the delivery system creates an account on the grocery company’s

website which includes the customer’s address. The grocery company takes online orders

through its website. All of the stores in the network are connected and synchronized with each

other through a central communication unit which includes all of the current pending online

orders. Once an order is received by the website, it conveys the information to the central

communication unit. All of the stores in the network can see the current pending orders.

At the same time, there are “carriers”, customers who visit their preferred store to do their daily

grocery shopping. As mentioned before, all of the customers (clients and carriers) are registered

on the website with their addresses. There are mechanisms installed in all of the stores which can

6

detect that a specific carrier is present in the store, e.g. the customer would scan or tap their

membership card when entering the store. Once the carrier has done his/her own shopping, the

cashier at the store would inform the carrier that the central communication unit has matched

them with a client. Upon the carrier’s willingness, he/she is expected to carry the client’s sealed

package containing their orders in it and deliver it to the client in a timely manner.

There are criteria for matching carriers and clients in a network which are applied by the central

communication unit. These match a carrier with a client in a way that imposes a reasonable extra

distance/travel time to the carrier’s routine shopping tour, so that the carrier would be willing to

take the opportunity. If the carrier picks up the client’s package from the store, he or she is

expected to deliver it within a specified time window. Once the sealed package is delivered to

the client, the client would pass a confirmation code to the carrier. This code is generated when

the client makes the online order. The carrier has to send this confirmation code to the central

communication unit in order to inform it of the successful delivery of the package. The carrier

would receive bonus points in his/her account on the store website that can be used when doing

grocery shopping later. The procedure for assignment of points is organized in such a way that

the new delivery system appeals to potential carriers. Besides the attractions of the matching

system for the carriers, this system could also have some benefits to the clients who are the

instigators of the whole process. For example, they would be charged less for the delivery than

for the regular truck delivery system, or they could be granted some points, or even the delivery

fee could be waived and they would be charged only for packing of their orders.

7

It has to be taken into consideration that every matched carrier is only matched with one client

and also every matched client is only serviced once with a single carrier. The central

communication unit does not match carriers and clients who have exactly the same address in

their profile, in order to prevent any system abuse. It is noteworthy that once the carrier indicates

his or her willingness, the store’s staff is responsible for gathering all of the ordered groceries

and packing them in a sealed package. This delivery system is named the “carrier/client

matching system.” Figure 1 illustrates how the system works.

1. Client places order via grocery store website and receives a confirmation code

2. Order is transferred from the website to the central communication unit

3. Carrier leaves the initial location to visit the grocery store

4. Grocery store informs the central communication unit that the carrier has entered the store

5. Central communication unit matches the carrier with an appropriate client and informs the

grocery store

6. Grocery store staff pass the packaged order to the carrier and the carrier leaves the grocery store

for the client’s location

7. Order is delivered to the client by the carrier, and client provides the carrier with the confirmation

code to be sent by the carrier to the central communication unit

8. The carrier leaves the client’s location for his/her final location.

Figure1. Flowchart of matching system environment

Grocery

store

Client

location

Carrier final

location

Carrier initial

location

Central

communication

unit

Grocery store

website 1 2

5

3

6

8

4

7

8

1.5) Importance of the research

In order to answer the two major questions, four different scenarios are defined, simulated and

compared with each other. These four scenarios are feasible shopping methods which are

different mixes of all available shopping modes, namely: regular shopping, truck delivery and

the matching system.

The reason that the main focus of this dissertation is the grocery business is that groceries have

greater demand than other products, e.g. clothing. Groceries are bought regularly and frequently

and they are the greatest portion of the shopping basket of a household at any time of the year.

The food and agricultural industry in US make up 9% of the gross domestic product; 60% of it in

sales activities. Retail food stores and restaurants sell over $890 billion of food and drink each

year, half of this amount is spent in grocery stores, which is why there is fierce competition

between companies in this business (Kinsey and Ashman 2000). In addition, groceries are a

commodity which is not always necessary to test or see before purchasing.

Another reason that this research focuses on e-grocery delivery is that the market growth is

expected to be high. E-grocery business has moved from the infancy stage to the growth stage

and its sales are expected to rise dramatically in coming years. E-grocery businesses forecast a

growth in their business. Although the profits are currently low, more profit is expected if the

business keeps growing at the expected rates. More frequent access to the internet by consumers

and less expensive computer prices are the major elements that would help this business to

further develop (Hean Tat Keh and Shieh 2001).

9

In general we can classify shopping tours into two types: home-based shopping and non-home-

based shopping. Home-based shopping tours are generated from home and are terminated at

home as well. Non-home-based shopping tours are done by individuals who are on their way

home from some other location, e.g. their workplace. The proposed matching system offers an

alternative to home-based shopping and decreases some of its consequences, e.g. emissions,

traffic load. The reason for this claim is the fact that the carrier travels almost the same route

after purchasing his or her groceries as would the customer who would engage in home-based

shopping and lives close to that carrier. Now, according to the matching concept, the carrier has

done his/her own shopping and is heading home, it would be a productive endeavor to organize a

system in which the client’s order is carried in the carrier’s vehicle.

The agents in this system include clients, carriers and the grocery company. All of the agents

seek their benefit in any system and this system should be economically beneficial to all of them.

Clients would receive their groceries for a lower charge than if they were delivered by the store’s

delivery trucks. Carriers are attracted by rewards for travelling a reasonable extra distance and

taking a small detour. The grocery company is also a beneficiary, because the truck fleet imposes

a large initial cost (purchase of trucks) and continuing high maintenance costs. When the

matching system starts working well and becomes the first choice of the clients, the store can

eliminate the fleet of delivery trucks and the costs associated with it.

1.6) Outline of the research

This thesis is presented in six chapters as follows. Chapter 1 defines the problem and the new

delivery concept. It also presents the objectives of the research and the structure of the thesis.

10

Chapter 2 contains a review of literature on electronic grocery retailing business with emphasis

on vehicle routing problems. Chapter 3 presents the different grocery shopping-delivery

scenarios and the methodology that is employed in modelling them. Chapter 4 introduces the

study network and the set-up of the sensitivity analysis. In Chapter 5 the results for four

scenarios are presented. In this chapter results are discussed based on the different set ups that

are produced in chapter 4. Chapter 6 presents the conclusions and limitations of the thesis and

makes recommendations for future research.

11

CHAPTER 2

LITERATURE REVIEW

2.1) Logistics and supply chain management

Companies started to explore the management of logistics during the 1950s (Ballou 1999;

Bowersox et al. 1999). Logistics is that part of the supply chain process that plans, implements,

and controls the efficient, effective flow and storage of goods, services, and related information

from the point of origin to the point of consumption in order to meet costumer’s requirements

(Bowersox et al. 1999).Generally, a company is not able to control its entire supply chain

because a typical supply chain consists of several elements such as raw material suppliers,

production facilities, warehouses, distribution centers, transportation services, retailers and

customers. Thus, companies normally take a narrower view and control the immediate supplies

and distributions (Ballou 1999). Nowadays, the development of an economically efficient

operation is the main goal of both logistics and supply chain management. In these operations

several other goals are also taken into consideration such as flexibility and customer satisfaction.

It is expected that by developing new logistical models and by designing logistic networks with

new structures, companies may be able to offer more efficient and cost-effective services.

2.2) Traditional grocery supply chain

The elements of a supply chain include functions such as warehousing, transportation, customer

service and transportation. The consumers are the final part of the grocery supply chain, being

responsible for the picking and transporting of the goods. Figure 2 illustrates a simplified

structure of the traditional grocery supply chain.

12

Figure 2. Simplified structure of supply chain for traditional grocery retail business (Yrjölä 2001)

Retail businesses may make their purchases from a wide range of suppliers, ranging from

farmers to wholesalers. The finished goods are transported from the suppliers’ plants to the

warehouse. Warehouses supply the retail stores and in the case of grocery products the

transportation from warehouse to retail store is performed by trucks. In traditional grocery

business the final transaction happens in the grocery store where the customer picks and buys the

groceries. After picking and buying, customers carry the groceries away on their own.

13

2.3) E-Grocery supply chain

2.3.1) E-Grocery supply chain structure

In e-grocery business, the supply chain structure is similar to the traditional grocery supply chain

structure (Figure 2), except that the direction of the last arrow is downward, showing the

operation of delivery of goods to customers. In order to be practical and attractive to customers,

the e-grocery supply chain structure has to be more efficient than the traditional grocery supply

chain. In addition to this, customers can calculate the cost of doing their traditional grocery

shopping. Generally, customers consider the cost of the grocery shopping trip as the cost of gas

and parking, however they should note that the maintenance cost of the car and the opportunity

cost of their time play crucial roles in their choice of shopping mode.

Figure 3. Simplified structure of the e-grocery supply chain (Yrjölä 2001)

14

The goal for e-grocery business is to provide cost-efficient picking, packing and delivery

services with no inconvenience for the customers. The picking operation is generally done in one

of three ways. The first strategy is when the operation is done in a retail store. The second option

is the use of a separate dedicated distribution centre: this is most popular with new companies. A

third strategy is that a traditional grocery store could be partly converted to a distribution centre

and all the picking operations could be managed there.

2.3.2) E-grocery operations and motivations

The first online grocer in the USA was Grocery Express which was founded in San Francisco in

1981. It offered home delivery of groceries with a simple user interface via phone and fax.

Grocery Express had 5,000 customers at its peak, but logistical challenges and the inability to

build to scale eventually doomed it to failure (Mendelson 2001). The US e-grocery market

experienced rapid market growth from the mid-1990s to the end of that decade and new

companies such as Webvan, Streamline, Homegrocer, Peapod, and Groceryworks were

established. Most of these companies have either gone out of business or converted to traditional

grocery retailing. However, successful businesses such as Tesco started their e-grocery

operations later and have evolved to the point where today Tesco.com is the leading e-grocery

business in the UK.

In the late 1990s e-grocery businesses in particular invested in dedicated distribution centers to

make the picking and packing process more efficient, however, because of high investments and

low customer demand, these businesses did not prosper financially. Currently, e-grocery

businesses are generally based on the traditional in-store picking and packing process; for

15

example, Tesco.com uses 250 out of its 690 stores for e-grocery picking operations, covering

91% of the population in the UK (Jones 2001; Knichel 2001; Reinhardt 2001). It provides e-

grocery services through one third of its 690 stores which means that it is within a half-hour

reaching time to 91% of the British population (Kempiak and Fox 2002).

E-grocery shopping is attractive to people who lack the time or willingness to shop themselves.

These customer characteristics were also detected in earlier research on the typical e-grocery

customer (Lardner 1998; Ingram 1999). The customers’ main motivations for using e-grocery

services are, namely, the greater convenience and savings in time from not visiting the store, and

avoiding the picking operation while shopping in the store (Raijas 2000). Retailers believe that e-

grocery business will improve their market penetration and will play a crucial role in households’

grocery shopping preferences. Generally, the customers who are using e-grocery shopping

services are willing to pay more than the cost they would have to pay when shopping for

groceries traditionally, or alternatively the regular delivery fees for truck delivery service, in

order to enjoy the convenience they desire.

E-grocery business is also attractive to the grocery retailer by expanding the geographic area

covered by offering e-grocery services (Anckar et al. 2002). Considering the fact that the grocery

business is highly local because of transportation costs, e-grocery operations is a reasonable step

forward for grocery businesses to invest in. From society’s point of view also e-grocery business

is acceptable since it supports better services to elderly and disabled people.

16

2.4) Background for modelling home delivery operations

2.4.1) General

Home delivery is the provision of groceries by a retailer to a customer who has placed an order.

Typically home delivery operations are done by the truck fleet of the retailer. The specified

trucks are dispatched and scheduled using the vehicle routing problem. The following section

presents the vehicle routing problem (VRP).

2.4.2) Vehicle Routing Problem (VRP)

The basic VRP is composed of one depot, vehicles of the same type and numerous customers

(demands) in a network. Generally the objective is to minimize the total cost in the network.

VRP is an extension of the Travelling Salesman Problem (TSP) and is an NP-hard problem. As a

more general definition VRP is defined as selecting the best possible sequence of routes in the

network while minimizing a cost function and satisfying any existing constraints like fleet

capacity, delivery time windows, etc. There is not a single universally accepted definition for

VRP (Laporte 2007). There are different types of VRP, and numerous assumptions and

constraint combinations can be applied to them. The major types of VRP are as follows:

Stochastic vs. deterministic

When one or more variables of the problem, e.g. travel time, are not fixed (deterministic) they

would be treated as a random (stochastic) variable, hence the stochastic VRP. This was first

introduced in 1982 by Stewart and Golden. The computational difficulty of solving the problem

increases enormously, no matter what is considered random in the network (Larson and Odoni

2007).

17

Dynamic vs. static

When a company has certain known demands (demand that is known before the service day),

plus some online demands that are unknown before the service day, the case is a dynamic VRP,

whereas in the static VRP, all of the demands are known in advance and no demands are made

during the trucks’ service. In dynamic VRP, the decision in each stage is dependent on the

decision made in previous stages. A stage is defined as the point when the decision is made.

(Figliozzi 2010; Powell et al. 2000).

VRP with time window

In this type of VRP there is a timeline and the vehicles have to set their arrival time at the

customer’s address at a certain time in order to deliver orders in a timely manner and also reduce

the vehicle waiting time. It means that the vehicle has to wait if it arrives sooner than the

specified time (Repoussis and Tarantilis 2009).

Open VRP

Generally in truck services, trucks are scheduled to return to their initial location, from where

they have been dispatched, after completing the deliveries. In open VRP, trucks do not

necessarily return to their initial location and their final location is not the same as their initial

location (Laporte 2009).

Time dependent VRP

In this type, the travel times in the network vary during the day, which happens mostly in urban

networks (Figliozzi 2009). This kind of VRP is introduced by Malandraki and Daskin in 1992. A

18

common way to solve this type of VRP is to divide the time into N time steps and solve the VRP

for each time step (Malandraki and Daskin, 1992, Figliozzi 2009).

There are some extensions to the VRP problem that could fit most of the different types of the

VRP that were presented above. These extensions are as follows:

Single depot vs. multi depot

Trucks are dispatched from a single depot in the basic form of the truck delivery services. In

some cases the company may have multiple depots in the network and trucks are dispatched

from these scattered depots. This extension to the VRP is called multi depot VRP (Laporte

2009).

Split delivery vs. single source

In some delivery services a single customer may receive orders from more than one vehicle and

his/her orders may be distributed among vehicles; these cases are split delivery VRP. In the basic

case all of the orders of a customer are delivered by a single vehicle and is called single source

VRP. The reason this extension is specifically mentioned is the fact that the VRP that is used in

this thesis is a single source VRP, which is actually a simplified derivation of a multi depot VRP

(Laporte 2009).

Homogeneous vs. heterogeneous fleet

In some cases the delivery fleet is composed of more than one vehicle type and forms a VRP

with heterogeneous fleet. In these cases a notation of vehicle type is used when formulating the

19

VRP for vehicle type used. Each type can have different capacity or other characteristics. In

simple cases the fleet is uniformly made up of the same type of vehicles and the fleet is

homogeneous (Laporte 2009).

VRP with pickup and delivery

In some cases the vehicle is assigned with delivery duties in some parts of the route and pickup

duties and operations in other parts of the route. In these cases usually there are delivery time

windows and the problem is mostly solved by heuristic methods (Luo and Schonfeld 2007).

Consolidated delivery

In these cases different supplies have been sent to a warehouse and these supplies have been

bundled together. Vehicles visit these warehouses to pick up the bundles and deliver them to

customers. Such packaging and bundling operations can bring efficiency to the delivery services

in terms of time and cost (Laporte 2009).

2.4.3) Solutions to the VRP

VRP algorithms are divided into 3 major categories:

I. Exact algorithms

These algorithms are generally considered for classical VRP which has low complications, i.e. a

symmetric cost matrix, homogeneous fleet, limited number of depots. Dynamic Programming

(DP), branch-and-bound, and Integer Linear Programming are the three general formulation

techniques used when exact answers are being sought. Exact algorithms have poor success in

20

solving realistic problems. This algorithm may face difficulty in solving cases with more than

100 deliveries (Laporte 2009).

II. Classical heuristics

These solutions have more capacity for solving complicated VRP. Classical heuristics have two

parts; first part is the constructive heuristic and the second part is the improvement heuristic. The

first part proposes a fast, feasible solution to the problem and the second part tries to improve the

proposed solution (Laporte 2007). Some of the constructive heuristics are savings algorithms and

set partitioning heuristics. Clark-Wright is the best known savings algorithm which has been

used in this thesis as well. This algorithm is not the most accurate algorithm, but is the most

popular because it is fast and simple to implement (Cordeau et al. 2002).

III. Metaheuristic

These solutions are the most efficient way to solve large networks with noticeable complications,

which have been used in the past 15 years. Some of the best metaheuristic methods can solve

problems with more than 100 vertices and arrive to a solution much faster than any other

solution. The difference between these solutions and classical heuristics is the fact that the

objective function changes from one iteration to the next and that is why it is more efficient in

complicated VRP cases with numerous vertices (Laporte 2009).

21

CHAPTER 3

METHODOLOGY

3.1) Introduction

The new delivery concept proposed in this thesis – the matching system – is defined and

introduced in the previous section. In order to make this method of delivery more practical, it is

necessary to combine this new method with the other methods of delivery. In the next section,

four scenarios of delivery for grocery business are defined. All of these scenarios are practical

and are composed of the three different delivery methods.

3.2) Delivery scenarios

As briefly stated in the Introduction chapter, four different scenarios are considered for grocery

shopping and delivery. These four scenarios are intended to be feasible and practical mixes of the

three methods of shopping and delivery. The regular shopping method is the only method of

grocery shopping/delivery which does not essentially need to be combined with any other

methods to be deemed as a complete delivery system and a system can rely solely on it, however,

the two other methods of grocery delivery/shopping need to be combined with other methods to

form a complete and reliable delivery system for the network. That is why scenarios which are

mixtures of methods are defined and used in the network. The four proposed scenarios are as

follows:

Scenario 1: In this scenario all of the shopping and delivery operations are done by

customers on their own for their own use; thus this scenario is composed of one delivery

method which is the regular shopping mode.

22

Scenario 2: In this scenario all of the clients in the network are being serviced by the truck

delivery fleet of the store and the entire carriers do their own shopping and delivery

operations; thus this scenario is composed of two different delivery methods; the truck

delivery system and regular shopping.

Scenario 3: In this scenario initially carriers and clients are matched to the greatest extent

possible. Unmatched clients and carriers do their shopping and delivery operations on their

own; thus this scenario is composed of two different delivery methods which are the

matching system and regular shopping. Figure 4 illustrates how this scenario works in

detail.

Scenario 4: In this scenario initially carriers and clients are matched to the greatest extent

possible. Unmatched clients remain in the network and receive their orders by the truck

delivery fleet of the store and all unmatched carriers do their shopping and delivery

operations on their own; thus this scenario is composed of three different delivery modes;

the matching system, truck delivery system and regular shopping. Figure 5 illustrates how

this scenario works in detail.

23

Clients

(order)

Client & Carrier pool

(Matching system)

Carriers

Willingness to

do a delivery

Unmatched carriers

do their own

shopping (regular

shopping)

Clients and carriers are

matched

Unmatched clients do

their own shopping

(regular shopping)

SUCCESSFUL

MATCHING

UNSUCCESSFUL

MATCHING

YES

NO

Clients (order)

Client & Carrier pool

(Matching system)

Clients and carriers are matched

Carriers

Willingness to

do a delivery

Delivery by the truck delivery

fleet (truck delivery system)

SUCCESSFUL MATCHING

Unmatched carriers do

their own shopping

(regular shopping)

UNSUCCESSFUL MATCHING

NO

YES

Figure 5. Scenario 4

Figure 4. Scenario 3

24

3.3) Modelling of delivery methods

In order to model these four delivery scenarios, it is necessary to simulate the three delivery

methods respectively.

3.3.1) Regular shopping

As stated above, in this method of grocery shopping/delivery all customers (including both

clients and carriers) are assumed to visit their “best store” when shopping for groceries and

transport their groceries to their final destination themselves. In this mode, customers (either

client or carrier) leave their initial location and visit their “best store” and finally go to their final

destination. It is assumed that for clients the initial and final location of the shopping tour are the

same location (most likely home) and for carriers they are different locations (e.g. initial location

is the work office and final location is home). The total travel time for each customer is the sum

of the travel time of the stages of their shopping tours.

Total travel time for a carrier (regular shopping) =

(Equation 1)

i

K’’

i’

Figure 6. Carrier’s regular shopping tour

25

Total travel time for a client (regular shopping) =

(Equation 2)

Subscripts:

i = the carrier’s initial location zone in the shopping tour

i’= the carrier’s final location zone in the shopping tour

j= the client’s zone

k’’= the best store zone for the carrier

k’ = the best store zone for the client

Variables:

= travel time from carrier’s origin i to his best store k’’

= travel time from client’s zone j to his best store k’

= travel time from carrier’s origin i to his best store k’’ to carrier

destination i’

= travel time from client’s origin j to his best store k’ to client

destination j

j

K’

Figure 7. Client’s regular shopping tour

26

3.3.2) Matching system

In this method of grocery delivery each client is being matched with a carrier in the system as

long as there is an unmatched carrier to provide service such that the minimum possible travel

time is being spent by the matched carrier. Clients and customers are either matched or

unmatched in the network depending on the ratio. It is clear that in this method of grocery

delivery, matched clients do not travel in the network. Carriers (either matched or un-matched)

collectively comprise the total travel time spent in the network. The unmatched clients are

serviced by other methods of delivery, depending on the delivery scenario that is providing

delivery service in the system.

The shopping tour of a matched carrier starts from his initial location, from where he moves to

his best store, and then he travels from his best store to the location of the client who is matched

with him to deliver the groceries. Once the groceries are delivered to the client, the carrier travels

to his final location (Figure 8).

The shopping tour for an unmatched carrier starts from his initial location, followed by a visit to

his best store and ended by the movement from his best store to his final location.

The pattern of the movements of the carriers and clients in the network in this method of delivery

is as follows:

27

The total travel time for each customer is the sum of the travel time of the stages of their

shopping tours.

Total travel time by a matched carrier =

(Equation 3)

Subscripts:

i = the carrier’s initial location zone in the shopping tour

i’= the carrier’s final location zone in the shopping tour

j= the client’s zone

k’’= the best store zone for the carrier

Variables:

n = number of zones in the network

A = number of carriers in the network

B = number of clients

= number of clients in zone j

C= number of carriers’ shopping tours in the network

Number of carriers’ shopping tours in the network with initial location of and

final location of

i

K’’

j

i’

Figure 8. Matched carrier shopping tour (Matching system)

28

= number of carriers’ shopping tours in the network with initial location of .

= number of carriers’ shopping tours in the network with final location of .

= travel time from a carrier’s initial location i to his best store k’’

= travel time from client j to his matched carrier’s final location i’

= travel time from a carrier’s initial location i to his best store k’’ to

his matched client j

= travel time from a carrier’s initial location i, to his best store

k’’, to client j, to the carrier’s final location i’

= Total travel time by matched carriers in the network

A mixed integer optimization approach is employed to match the proper carrier and client in the

network. The optimization formulation is coded in Matlab and the optimization is solved using

CPLEX 12.5 on a dual core 2.20 GHZ laptop computer. The formulation and the optimization

details are presented in the following section.

Optimization objective

Objective is to minimize the total travel time by matched carriers in the network ( ) by

matching the carriers and clients as efficiently as possible.

Total travel time by matched carriers who are doing delivery to their matched clients is

calculated as follows:

∑

∑

(Equation 4)

29

Optimization method

In the first step, the best store matrix (BSM) for carriers is created. As defined above, the best

store for a carrier is the store which brings the carrier the shortest travel time possible, when he

does regular grocery shopping. BSM is a matrix that represents the store which provides

the shortest shopping tour for every possible pair of and . In this matrix the element (a,b) is

the store which provides the shortest regular shopping travel time for a carrier with the initial

location of a and final location of b.

The 3D matrix of T is created in the next step. This is a matrix which includes the

expected travel time for any possible combination of i,j and i’ in the network. For example, the

element in this matrix is the travel time of the shopping tour in which a carrier starts

from his initial location , proceeds to his/her best store , next to the matched client location

and at last to his/her final location .

(Equation 5)

Once the T matrix is formed, the X matrix is calculated. X is a 3D matrix with the same

dimensions as T in which cell (i,i’,j) shows the optimized number of delivery tours by matched

carriers which are started in initial location of i, and completed in final location of i’ while a

client located in j is serviced on the way after the best store is visited by the matched carrier.

The objective of this optimization is to find the optimum X. The optimum X matrix has to be

found considering these logical constraints:

1) Sum of all of the elements of X equals the smaller number between the number of

clients in the network (B) and number of carriers’ shopping tours in the network (C).

30

∑∑∑

2) Number of delivery tours by matched carriers that provide service to any zone of the

clients (any j) is less than or equal to the number of orders in that specific zone.

∑∑

3) Number of delivery tours by matched carriers that are originated from each zone (any

i) is less than or equal to the number of carriers’ shopping tours with the initial

location in that specific zone ( ).

∑∑

4) Number of delivery tours by matched carriers that are completed in each zone is less

than or equal to the number of carriers’ shopping tours completed in that specific

zone ( .

∑∑

5) Number of delivery tours by matched carriers that are associated with any

combination of and is less than or equal to the number of carriers’ shopping tours

associated with and .

∑

31

By taking these constraints into consideration a mixed integer optimization is conducted to

calculate the optimized matrix.

Once the matrix is calculated, a third 3D matrix named is formed. is the 3D matrix with the

same dimensions as and . This matrix is the multiplication of the two corresponding elements

of and . (Element-by-element multiplication rather than regular matrix multiplication).

Thus:

(Equation 6)

Summation of the elements of is the minimum possible travel time of all delivery tours by

matched carriers in the network.

3.3.3) Truck delivery system

It is assumed in the present scenarios that the truck delivery fleet is delivering groceries based on

a specific type of vehicle routing problem (VRP). Our VRP is multi-depot split-delivery vehicle

routing problem (MDSDVRP) in which vehicles are dispatched from several depots scattered in

the network. In the proposed scenarios with truck delivery fleet, each zone in the network has

different number of clients. It is assumed that more than one delivery truck can visit each zone to

service the clients located in that specific zone.

32

The heuristic that is being used to solve the MDSDVRP is a derivation of the heuristic

introduced by Gulczynski, Golden and Wasil (2011) who were the first to solve a multi depot

split-delivery VRP.

Vehicle routing problem solution:

Step 1: Assigning nodes to depots

In this step each node in the network is assigned to a depot. For each node , is the travel cost

between node and the closest depot to in terms of travel time, and is the travel cost

between and the second closest depot to in terms of travel time. If

⁄ is less than a tolerance

then node is assigned to its closest depot in terms of travel time. If

⁄ , then is left

unassigned temporarily. Thus, a node that is much closer to one depot than other depots will be

immediately assigned to its closest depot. A node that is almost equidistant from multiple depots

will be assigned using the cheapest insertion. For each unassigned node and each depot , the

cost of inserting between each pair of nodes that is already assigned to is calculated. Node

is assigned to the same depot as nodes and where is the smallest number

among other possible pairs of nodes that are already assigned to the same depot.

Now, all nodes are assigned to a depot, however the sequentiality of nodes in tours is not

considered and is not yet optimized.

Step 2: Saving algorithm

The Clark-Wright algorithm is used to find out which nodes can be linked to each other and be

serviced with the same vehicle. Capacity constraints are taken into consideration as well. In this

33

saving algorithm, at first it is assumed that each truck is servicing only one zone; however it may

not be filled to capacity (Figure 9). Then the nodes are considered to be serviced by the same

truck when their pairing can save travel time for the truck and the truck’s capacity constraint is

not violated (Figure 10). However, it may be possible that a truck is able to service a portion of

the clients in a zone before reaching capacity. In such cases split delivery occurs and the truck

will service the zone to the greatest extent possible, and the zone is paired with its next best

possible saving pair and is serviced with another vehicle. The travel time saving after the

connection for the two nodes of and which are both assigned to the depot is calculated as

follows:

(Equation 7)

w

i j

i j

w

Figure 9. Truck delivery operation before two zones are connected

Figure 10. Truck delivery operation after two zones are connected

34

(Equation 8)

(Equation 9)

The savings for all possible pairs of nodes with the same depot are calculated and sorted from

largest to smallest and then nodes are paired considering the savings and the trucks’ capacity

constraints. At the end of this step, nodes are linked to each other to be serviced sequentially in a

tour with maximum savings in total cost (total travel time).

35

CHAPTER 4

STUDY NETWORK AND SENSITIVITY ANALYSIS

4.1) Study network (Sioux Falls network)

The study uses the Sioux Falls network which is composed of 24 nodes and 76 transportation

links. The properties of this hypothetical network are provided in two matrices; the Before-after

matrix which represents the frequency of tours (either shopping tour or otherwise) initiated in a

node and ending in another node. The Cost matrix is the other matrix which represents the travel

time between nodes in the network. Although the internal travel time for each node is zero in the

cost matrix, a constant travel time is considered for all of internal trips in order to simulate the

delivery trucks’ movements inside a node more precisely. These matrices are provided in the

appendix A.

Figure 11. Sioux Falls network

36

4.2) Carrier’s location

Locations of carriers in the network are randomized in order to reach a result independent of the

carrier’s location. As discussed above, the before-after matrix of carriers’ shopping tours is

derived from the before-after matrix of the Sioux Falls network. A probability can be calculated

for each cell in the Sioux Falls before-after matrix.

∑ ∑

(Equation 10)

With as the before-after matrix and its size is .

Considering that ∑ ∑

, a random number between 0 and 1 is generated by

Matlab. This random number defines the location of a carrier in the network. While doing this

randomization, it is noted that the number of carriers assigned to a zone in the Sioux Falls

network does not exceed its associated element in the Sioux Falls before-after matrix and the

total number of carriers in a carrier’s before-after matrix is confined to the total number of

carriers in that specific client/carrier ratio (Table 1).

4.3) Client’s location

Locations of clients are randomized in order to reach a result which is independent of the client’s

location. Considering that there are 24 nodes in the Sioux Falls network and also there are fixed

number of clients in the network for each client/carrier ratio (Table 1), a random integer

between 1 and 24 is generated which defines the node to which a client belongs. This generation

continues until the desired number of clients are randomly scattered.

37

4.4) Sensitivity analysis

In order to capture the differences between the four scenarios described in Chapter 3, it is

necessary to conduct a sensitivity analysis in order to gain a comprehensive insight into the

scenarios’ functions and behavior. Each scenario in every set-up has been run 30 times and its

results recorded. The average of the 30 runs is reported as the result for every scenario.

4.4.1) Number of stores in the network

Accessibility of the grocery stores plays an important role in the travel time of shopping tours. In

order to capture the effect of store availability in the network over different scenarios, the

modeling for scenarios is done for four different cases where there are 4, 8, 12 and 16 stores in

the network. Therefore, the performance of the four scenarios in these four different cases and

the effect of number of stores in the network are evaluated.

4.4.2) Client/carrier ratio

A constant number of 1500 customers are considered to be scattered in the network. These 1500

customers are either carriers or clients. In order to capture the effect of different client/carrier

ratios, the 1500 customers are divided into either clients or carriers in six different ratios. The

client/carrier ratios are set out in Table 1:

38

Client/Carrier

Ratio 0.25 0.5 1 1.5 2 4

No. of Clients 300 500 750 900 1000 1200

No. of Carriers 1200 1000 750 600 500 300

No. of customers 1500 1500 1500 1500 1500 1500

Table 1. Client/carrier ratios

4.4.3) Truck capacity constraints

The capacity constraint plays a crucial role in the number of truck delivery tours. The capacity

that is considered as the base case is 50 units of delivery per truck. In order to capture the effect

of this constraint and the way that the scenarios react to changes, three other cases with 10, 25

and 100 units of delivery are also examined.

39

CHAPTER 5

RESULTS

5.1) Introduction

The four shopping-delivery scenarios are simulated as presented in the methodology chapter.

Next the sensitivity analyses are taken into consideration and the performance of each scenario is

investigated and examined. This chapter reports the scenarios performance in two ways in

general; each scenario’s performance is investigated under different conditions, e.g. different

client/carrier ratio, or scenarios’ performances are compared to one another.

5.2) Sensitivity analysis: client/carrier ratio

In order to assess the impact of the changes of the client/carrier ratio in each scenario’s

performance, it is assumed that there are only four stores in the network. The performance of

each of the scenarios is investigated under this assumption.

5.2.1) Scenario 1: Regular shopping for all clients and carriers (R)

Figure 12 shows that carrier tours are longer than client tours, which explains why travel time

decreases with a greater proportion of clients. In addition, Figure 12 shows that for a

client/carrier ratio of 1.0 where there are equal numbers of clients and carriers in the network,

carriers’ regular shopping travel time is higher than the clients’ regular shopping travel time.

This happens because of the nature of home-based and non-home-based shopping trips. Carriers

are assumed to make non-home-based regular shopping trips and, on the other hand, clients are

assumed to make home-based shopping trips. Non-home-based shopping trips are generally

40

longer than home-based shopping trips, thus carriers’ regular shopping trips are longer than

clients’ regular shopping trips when client/carrier ratio is 1.0.

Figure 12. Travel times in scenario 1

Figure 13 demonstrates that, as expected, regular shopping travel time per carrier and regular

shopping travel time per client are almost constant and the changes in the client/carrier ratio do

not affect them. Regular shopping travel time per carrier is larger than the regular shopping

travel time per client because, as explained in Figure 12, carriers generally make non-home

based shopping trips as opposed to clients who make home-based shopping trips, and non-home-

41

based shopping trips are longer than home-based shopping trips. As the client/carrier ratio

increases, the network relies more on the clients’ regular shopping rather than the carriers’

regular shopping and this causes the decrease in the total travel time of the network.

Figure 13. Travel times per client/carrier/customer in scenario 1

5.2.2) Scenario 2 : Truck delivery fleet, regular shopping for all carriers (TR)

Figure 14 shows that, for scenario 2, delivery trucks’ travel time increases as the client/carrier

ratio increases because of the availability of more clients in the network and the greater demand

42

for the truck delivery service to serve the clients. The changes in delivery trucks’ travel time can

be seen with more precision in the next figure (Figure 15). Moreover, the carriers’ regular

shopping travel time decreases as the client/carrier ratio increases because of the decreasing

number of carriers in the network. The total travel time in the network decreases as the

client/carrier ratio increases. The reason is that carriers’ regular shopping decreases more

sharply than the increase in the trucks’ travel time, and also trucks’ travel times are considerably

lower than the carriers’ regular shopping travel times.


43

Figure 15. Delivery truck travel time in scenario 2

Figure 16 shows that as the client/carrier ratio increases and the number of clients in the

network grow, there would be a higher demand for the delivery truck service and consequently

the number of delivery trips would increase.

Figure 16. Number of delivery truck tours in scenario 2

44


carriers and clients (MR)

Figure 17 shows that un-matched carrier travel time decreases as the client/carrier ratio

increases and finally it becomes zero at a client/carrier ratio of 1.0 and higher. The reason for

this is that with a client/carrier ratio of 1.0 and higher, the number of clients exceeds the number

of carriers and all of the carriers are matched with a client. Unmatched clients’ travel times are

zero for client/carrier ratios of 0.25 to 1.0 because there is a matched carrier giving service to

them and clients do not have to shop for themselves. For client/carrier ratios greater than 1.0,

unmatched clients travel in the network and the sum of their travel time increases as the

client/carrier ratio increases because of the higher number of unmatched clients in the network.

Matched carriers’ travel time increases from the client/carrier ratio of 0.25 to 1.0, because as the

number of clients increases and there are enough carriers available, higher numbers of clients

and carriers are matched and there would be a higher number of matched carriers in the network.

The travel time for matched carriers reaches its maximum at a client/carrier ratio of 1.0 where

there are equal numbers of carriers and clients. Above a client/carrier ratio of 1.0, matched

carriers’ travel time decreases because the number of clients in the network dominates the

number of carriers and few are paired. In addition, the total travel time of all of the customers

(either clients or carriers) in the network decreases from the client/carrier ratio of 0.25 to 1.0.

This happens due to the fact that as the client/carrier ratio increases the network moves toward

equilibrium and consequently the matching system is utilized more and there are fewer regular

shopping tours. Beyond a client/carrier ratio of 1.0, total travel time in the network increases

due to the fact that the increase in the unmatched clients’ regular shopping travel time is sharper

than the decrease in matched carriers’ travel time. The total travel time for the client/carrier ratio

45

of 4.0 is higher than the total travel time for the client/carrier ratio of 1.0, however the number

of matched shopping (300) and the number of regular shopping (1200) is the same in both. This

could be explained by the fact that normally home-based shopping travel times (which clients are

more likely to do) are shorter than non-home based shopping travel times.


Figure 18 shows that the travel time per carrier for the regular shopping trips of unmatched

carriers increases gradually for client/carrier ratios of 0.25 to 0.5 and then it drops to zero. In

46

addition, the travel time per client for regular shopping trip of unmatched clients experiences a

gradual decrease from the client/carrier ratio of 1.5 to 4.0. The travel time per carrier for

matched carriers increases from the client/carrier ratio of 0.25 to 1.0 because of fewer matches

and it stays even for the client/carrier ratio of 1.0 and greater. Moreover, total travel time per

customer in the network has its minimum and the most efficient condition occurs when clients

and carriers are balanced with the highest possible number of matches in the network.

Figure 18. Travel time per client/carrier/customer in scenario 3

47


carriers (MTR)

According to Figure 19, the trend in the travel time for regular shopping of un-matched carriers

and the travel time of the matched carriers in scenario 4 are the same as in scenario 3. Figure 19

shows that as the client/carrier ratio increases and consequently more clients are distributed in

the network, trucks offer service to more clients. Figure 20 illustrates this with more precision.

Moreover, the total travel time in scenario 4 decreases as the client/carrier ratio increases from

1.5 to 4.0. This can be explained by the fact that as the client/carrier ratio increases, fewer

customers are matched and delivery trucks are relied upon. The travel time of the trucks is

considerably lower than the matched carriers’ travel times.


48

Figure 20. Delivery truck travel times in scenario 4

Figure 21, illustrates that as the client/carrier ratio increases from 1.5 to 4.0 and more clients

place orders, delivery trucks get involved more frequently in the network and considering their

limited capacity, more trucks (tours) are needed to serve clients. The changes in travel times per

carrier for the regular shopping of un-matched carriers and the travel time per carrier of matched

carriers in scenario 4 is the same as their changes in scenario 3.

Figure 21. Number of truck delivery tours in scenario 4

49

This section has shown the changes in travel times of the four scenarios with four stores in the

network. The trends in travel times and the justifications are similar for the networks with 8, 12

and 16 stores, however, the absolute numbers change as the number of stores changes. The

related figures for cases with 8, 12 and 16 stores are available in appendix B.

5.3) Comparison of scenario performance

In this section, the four scenarios are compared with each other in terms of travel time. In these

comparisons we assume that there are four stores in the network.

The performance of scenarios 3 and 4 are identical for a client/carrier ratio ranging from 0.25 to

1.0. For the client/carrier ratio above 1.0, clients start regular shopping in scenario 3 and trucks

start delivery service in scenario 4. For client/carrier ratios higher than 1.0, the total travel time

in scenario 3 increases because of the increasing number of unmatched clients. However, in

scenario 4 the total travel time keeps decreasing because of the sharp decrease in matched

carriers’ travel time. The performance of scenarios 3 and 2 are close for client/carrier ratios

ranging from 0.25 to 1.0. Scenario 2 has a better performance than scenario 3 for client/carrier

ratios higher than 1.0, because fewer regular shopping tours are made. Scenario 1 has the longest

travel time for all client/carrier ratios, because it relies completely on regular shopping which

tends to have larger travel time in comparison with either the matching system or the truck

delivery service. Scenarios 2 and 4 have almost the same total travel time in the network for all

client/carrier ratios (scenario 4 performs slightly better than scenario 2). This proves that the

matching system is economical because although it performs almost the same as the truck

delivery system, it is less reliant on the truck delivery system and the high costs of operating a

50

truck fleet can be avoided. Moreover, the figure shows that scenario 4, which is the mix of the

new matched system and the truck delivery system, has the potential to perform better than all

other scenarios and its minimum occurs at a client/carrier ratio of 4.

51

Figure 22. Travel times for all scenarios

52

In Figure 23 the changes in truck delivery fleet travel time for scenario 2 and scenario 4 are

compared. Scenario 2 relies more on the truck delivery system. In scenario 2 the truck delivery

system is engaged in all ratios, however in scenario 4 it is launched for client/carrier ratios of

greater than 1.0 where there are not enough carriers in the matching system. Also, the travel time

for a client/carrier ratio ranging from 1 to 4 is always higher in scenario 2, which seems logical

because in scenario 4 the delivery fleet is serving the unmatched clients, while in scenario 2

trucks serve all clients.

Figure 23. Delivery truck travel time in scenarios 2 and 4

53

Now that this study is performed for the case with four stores, the other cases with 8, 12 and 16

stores are also investigated and their figures show that the comparisons between different

scenarios described above are applicable for all cases. The figures related to these comparisons

are available in appendix C.

5.4) Analysis of the impact of changes in the number of stores

5.4.1) Scenario 1: Regular shopping for all clients and carriers (R)

Figure 24 shows that as the number of stores in the network increases, travel times for carriers

and clients decrease. This decrease is sharper for clients than it is for carriers. Clients’ regular

shopping tours have two legs, the first leg is towards the store and the second leg is the return to

the initial location (presumably their home). It can be interpreted that the clients’ regular

shopping travel times are dependent on these two distances and these two distances are

dependent on the nearest store location. However, within carriers’ regular shopping trips there

are two other legs that are not as heavily dependent on the store’s availability. The first leg is

store-bound and the second leg is their destination (presumably their home). What makes the

store’s accessibility less of an important element in carriers’ regular shopping travel times is the

fact that store’s accessibility is only a factor that defines how far the carrier has to deviate from

his/her ideal route when going from his/her initial location (presumably his/her work location) to

his/her final destination (presumably his/her home) in order to do shopping. This deviation can

be an important factor in carriers’ regular shopping travel times, however it is not the only factor

in play because carriers are obliged to travel the long distances from their initial location to their

final destination anyway and store accessibility is only a portion of this.

54


Figure 25 shows that as the number of stores in the network increases, travel time per carrier,

travel time per client and total travel time per customer decreases. This is due to higher

accessibility to the store for clients and carriers.

55

Figure 25. Travel time per client/carrier/customer in scenario 2

5.4.2) Scenario 2: Truck delivery fleet, regular shopping for all carriers (TR)

Figure 26 shows that a higher number of stores in the network lead to less shopping travel time

for either of the shopping methods in scenario 2. Figure 27, brings the delivery trucks travel time

of scenario 2 into focus. We observe that as the number of stores in the network grows delivery

truck travel times decrease because of the greater accessibility in the network. Finally, Figure 28

illustrates that as the number of stores in the network increases, the number of delivery truck

tours in scenario 2 increases. This increase is more severe in lower client/carrier ratios and as the

ratio grows, this difference is more graded.

56

Figure26. Travel times in scenario 2

Figure 27. Delivery truck travel times in scenario 2

57

Figure 28, illustrates that as the number of stores in the network increases, number of delivery

truck tours in scenario 2 increases. This increase is more severe in lower client/carrier ratios and

as the ratio grows, this difference is more graded.

Figure 28. Number of truck delivery tours in scenario 2


carriers and clients (MR)

The travel times of the different methods of shopping and delivery (either regular shopping or

matching system) for each client/carrier ratio decrease as the number of stores in the network

increases because there would be more stores available and the travel time from each point to its

best store would be smaller. It is noteworthy to mention that the rate of the change in travel time

savings is decreasing as the number of stores increases; this means that the difference between

58

the case with four stores and eight stores is higher than when comparing the cases with eight

stores and 12 stores and consequently the comparison between 12 stores and 16 stores. This

means that it becomes less economical to increase the number of stores to reduce travel times. As

the number of stores increases, from client/carrier ratio of 0.25 to 1.0, the total travel time in the

network decreases more gradually. In the case with 4 stores, we observe an increase in travel

times between client/carrier ratio of 1.5 and client/carrier ratio of 4; this increase gets less acute

as the number of stores increases until the case with 12 stores. For 12 stores, the total travel time

decreases for client/carrier ratios above 1. This decrease is even sharper in case with 16 stores.

59


60

As illustrated in Figure 30 the trend of the travel time per client/carrier/customer for different

numbers of stores is similar to the changes in Figure 29, meaning that as the number of stores

increases, a shorter travel time per client/carrier/customer is needed to complete a delivery and

shopping tour. In addition, the results confirm in Figure 29, that it is not necessarily economical

to invest in a larger number of stores in order to increase accessibility. The differences between

the cases with 12 stores and 16 stores are small.

61

Figure 30. Scenario 3 travel times per client/carrier/customer

62


carriers (MTR)

As shown in Figure 31, the travel time for each shopping method (truck delivery, regular or

matching) decreases as number of stores increases in the network. Also, it is clear that for

client/carrier ratios above 3 as the number of stores increases, the “total travel times” line is

getting closer to the “matched carriers travel time” line. The reason can be found by looking at

delivery trucks. As the number of stores grow, trucks travel for shorter travel times, thus they

play a smaller role in total travel time. Figure 32 shows the changes in trucks travel time on a

better scale.

63


64

Figure 32. Delivery trucks travel time in scenario 4

As the number of stores grows, the range of the number of tours is shrinking. This can be

interpreted by considering two facts; the first is that in our solution for the vehicle routing

problem, the only thing that is optimized is the cumulative travel times of the trucks in the

network and the optimization is not related to any other kinds of savings (e.g. savings in number

of used trucks). The second fact is that in our vehicle routing problem optimization there are no

constraints limiting the number of trucks used. Thus, we are allowed to use as many trucks as

required to reach the lowest overall trucks’ travel times in the network. In Figure 33, the

65

maximum number of tours for any number of stores occurs for the client/carrier ratio of 4, and

this is almost the same for cases with different number of stores. However, the number of tours

for client/carrier ratio of 2.0 and client/carrier ratio of 1.5 are approaching the number of tours

for client/carrier ratio of 4.0 as the number of stores grows. This is because with a higher

number of available stores and considering the fact that there are no limits on the number of

trucks and also because in our cost matrix (travel times between zones) the cost of inter-zonal

trips is much higher than the cost of intra-zonal trips, more trucks are dispatched from stores to

reach the clients in surrounding zones so that the inter-zonal movements are avoided.

Figure 33. Number of delivery truck tours in scenario 4

66

5.5) Analysis of the impact of the changes in trucks load capacity on

scenarios with truck delivery fleet

Load capacity of the delivery trucks is one of the constraints of the VRP in scenario 2 and

scenario 4. It is important to note that all the results presented in previous sections are based on

the assumption of having 50 units of delivery capacity in the trucks. In order to capture the

effects of different load capacities, three other cases with 10, 25 and 100 units are tested in

addition to the base case with 50 units of load capacity.

Figures 34 and 35 prove that as the truck capacity increases, the trucks’ travel times decrease.

This is because as the trucks carry more orders they would return to the store (depot) to reload

less often especially that intra-zonal travel times are lower than inter-zonal travel times. That is

why as the truck enters a zone to deliver the orders to clients of that zone, it is traveling short

distances with low travel times and after delivering to all the clients it drives the relatively longer

distance to the store to be reloaded and dispatched again. In addition, it is important to note that

the slope of the line decreases as the capacity increases. This is because with high capacity in the

truck, it can reach more clients with fewer legs of empty backhauling to the store or dispatching

from the store, thus the travel times of different client/carrier ratios stay close to each other.

67



0

200

400

600

800

1000

1200

1400

1600

1800

trav

el t

ime

client/carrier ratio

capacity 10 capacity 25 capacity 50 capacity 100

0.25 0.5 1.0 1.5 2.0 4.0

68

Figures 36 and 37 show that the changes in the number of delivery tours behaves the same as the

trucks’ travel times change. The explanations that are provided for Figures 34 and 35 are valid

here as well. Furthermore, the comparison of the four capacities and the behavior of each of the

capacities across the six client/carrier ratios is similar to what was observed in figures 34 and

35.

In order to study the effect of stores’ locations on the results, we assume that there are only four

stores in the network and stores are distributed in three different patterns. The reason to select the

case with four stores is that in this case travel times are higher, thus greater differences in results

are expected. We observe that the location of the stores does not play an important role in the

overall performance of the scenarios and their results. This is due to the fact that carriers and

clients locations are already randomized. Thus, there is no need to implement a third

randomization for the stores locations in the network.

Figure 36. Number of delivery tours in scenario 2

69

Figure 37. Number of delivery tours in scenario 4

The figures and tables related to this part are available in appendix D.

70

CHAPTER 6.

CONCLUSION

6.1) Introduction

Grocery retail is the largest sector of retailing business. This has unique aspects and several

players are seeking their own benefit including, business owners, grocery customers and society

in general. Delivery operations are a major part of this business which has a great impact on the

quality of the services and the customers’ satisfaction. Internet and information technology has

brought novel ideas especially in streamlining delivery operations and setting up more efficient

commodity circulation between customers. This thesis focuses on the delivery operations of

grocery businesses. In this thesis, a new delivery concept for groceries developed and simulated

in order to evaluate its performance. This delivery concept has been analyzed through a number

of scenarios. These scenarios include combinations of the current shopping-delivery modes;

truck delivery and regular shopping. This concept seems to be a potential step forward for e-

grocery businesses, because it is economically beneficial to all of the agents involved with this

delivery method. Carriers are rewarded with points for making a delivery to a client who lives a

reasonable distance from the carrier’s final location. Clients benefit because they would have

their groceries delivered to their address with a lower or no delivery fee. The grocery business

would benefit as well. A delivery system requires large distribution facilities and infrastructure;

by employing this method of delivery for customers, the store would be able to reduce its costs

of delivery.

71

6.2) How the research objectives are satisfied

The thesis set out to answer the two major questions that are presented in the introduction

chapter. The results provide important insight into how the new delivery concept works and the

benefits that it brings when combined with other delivery modes.

In general, the results prove that the ideal conditions for the matching system happens when

there are equal numbers of carriers and customers in the network (balanced network). In the

balanced network, the matching system brings its largest benefit to the total travel time in the

network. Scenario 4, which employs both truck delivery and the matching system, performs

better than the other three scenarios in terms of total travel time. Thus it can be concluded that,

on the basis of travel time estimates, the matching system seems to be a promising method of

shopping-delivery which can be combined with other methods of delivery to produce efficient

shopping-delivery scenarios for grocery shopping.

The results also prove the fact that as the number of stores in the network increase, travel times

in the network would decrease, however this change would be less intensive as more stores are

introduced.

6.3) Research significance

This research introduces and evaluates a new method of grocery delivery. The new method (the

matching system) is simulated and combined with other methods of delivery to form four

scenarios of shopping-delivery. The developed models of the four scenarios are capable of

72

quantifying the travel time of the delivery methods. Sensitivity analysis is conducted in order to

be able to compare the scenarios in a broader extent.

6.4) Limitations and recommendations for future research

This research is based on the Sioux Falls network which is a hypothetical network. Although we

consider the results to be robust, it would be a reasonable next step to model the shopping

scenarios based on a real traffic network and shopping data.

The psychological aspect of the matching system is very important. It is necessary to have an

insight into how the players of the matching system – client, carrier and the grocery business –

would react to this concept. In addition, the role of the government and communities in the

development of this delivery-shopping concept remains to be addressed.

In this thesis the truck delivery system is assumed to obey the multi-depot split-delivery vehicle

routing problem where every traffic zone is serviced by a truck unless the truck exceeds its

capacity. Each traffic zone can be serviced by several trucks depending on the entire set of orders

and their quantity in other traffic zones. More complicated algorithms can be employed to

maximize the savings from splitting deliveries to certain zones by reallocating some (or all) of

their demands to new routes (Gulczynski 2011). Although it adds considerable complexity to the

planning for the truck delivery system, it makes truck delivery systems more efficient.

Delivery time-windows are a logical add-on to the matching system where clients could order

their groceries to be delivered within a desired time-window. This would add more complexity to

73

simulate the matching system; however it would make the matching system more realistic in

satisfying customer demands.

The cost matrix of the Sioux-Falls network is a symmetric matrix which only includes travel

time between zones. A detailed asymmetric cost matrix that considers other factors such as the

costs associated with fuel, parking fees and customers’ value of time is recommended for future

research.

74

REFERENCES

Archetti, C., Savelsbergh, M.W.P. and Grazia Speranza, M. (2008) To Split or Not to Split? That

Is the Question, Transportation Research Part E 44 (2008) 114-123

Ballou, R.H. (1999) Business Logistics Management – Planning, Organizing and Controlling the

Supply Chain, 4th edition, Prentice-Hall, New Jersey.

Bartolotta, S. (1998) The Consumer Direct Channel: How the Virtual Retailer Is Winning

New Customers, in Strategic Supply Chain Alignment. Best Practice in Supply Chain

Management, Ed. J. Gattorna. Gower Publishing, Brookfield.

Bowersox, D.J., Closs, D.J. and Stank, T.P. (1999) Twenty-first Century Logistics: Making

Supply Chain Integration a Reality, Council of Logistics Management, Oak Brook.

Boyer, K. K., Tomas, G., Hult, M. and Broad, E. (2005) Extending the Supply Chain: Integrating

Operations and Marketing in the Online Grocery Industry, Journal of Operations Management 23

(2005) 642-661

Cairns, S. (1996) Successes and Failures of Home Delivery Services for Food Shopping,

Transport Policy, 3(4): 155–176.

Cordeau, J.F., Gendreau, M., Laporte, G., Potvin, J.Y. and Semet, F. (2002) A Guide to Vehicle

Routing Heuristics. Journal of the Operational Research Society 53(5): 512–522.

75

Cullinane, S., Edwards, J. and McKinnon, A. (2008) Clicks versus Bricks on Campus: Assessing

the Environmental Impact of Online Food Shopping, Logistics Research Centre, Heriot-Watt

University.

Dagher, N., Soumitra, D. and De Meyer, A. (1998) Online Grocery Shopping, INSEAD,

Fontainebleau, France.

Farag, S., Schwanen, T., Dijst, M. and Faber, J. (2007) Shopping Online and/or In-Store? A

Structural Equation Model of the Relationships between e-Shopping and In-Store Shopping,

Transportation Research Part A 41 (2007) 125-141.

Figliozzi, M.A. (2009a) Planning Approximations to the Average Length of Vehicle Routing

Problems with Time Window Constraints. Transportation Research, Part B 43(4), 438–447.

Figliozzi, M.A. (2009b) A Route Improvement Algorithm for the Vehicle Routing Problem with

Time Dependent Travel Times. TRB 88th

Annual Meeting

Figliozzi, M.A. (2010) Emissions and Energy Minimization Vehicle Routing Problem. TRB 89th

Annual Meeting

Galante, N., Garcia Lopez, E. and Monroe, S. (2013) The Future of Online Grocery in Europe:

Perspectives on Retail and Consumer Goods, Spring 2013, McKinsey & Company.

76

Gulczynski, D., Golden, B. and Wasil, E. (2011) The Multi-Depot Split Delivery Vehicle

Routing Problem: An Integer Programming-Based Heuristic: New Test Problems and

Computational Results. Computers & Industrial Engineering 61 (2011) 794 - 804.

Hean Tat Keh and Shieh, E. (2001) Online Grocery Retailing: Success Factors and Potential

Pitfalls, Business Horizons (July-August).

Heikkilä, J., Kallio, J., Laine, J., Saarinen, L., Tinnilä, M., Tuunainen, V. and Vepsäläinen A.

(1998) First Steps in Electronic Business

Hensher, D. and Figliozzi, M.A. (2007) Behavioral Insights into the Modelling of Freight

Transportation and Distribution Systems, Transportation Research Part B 41 (2007) 921–923.

Holmström, J., Tanskanen, K. and Kämäräinen, V. (1999),Redesigning the Supply Chain for

Internet Shopping – Bringing ECR to the Households, Logistics Research Network Conference

Proceedings, Newcastle, UK.

Ingram, B. (1999), Boston Bears Watching, Supermarket Business, March, 41–45.

Jones, D. (2001) Tesco.Com: Delivering Home Shopping, ECR Journal, 1: 137–143.

Kempiak, M. and Fox, M.A. (2002) Online Grocery Shopping: Consumer Motives, Concerns

and Business Models, First Monday, Volume 7, Number 9.

77

Kinsey, J. and Ashman, S. (2000) Information Technology in the Retail Food Industry,

Technology in Society 22: 83–96.

Knichel, B. (2001) The Intelligent Supply Chain – Tesco Home Shopping, CIES

Conference handout, October 2001, Amsterdam, The Netherlands.

Kämäräinen, V., Saranen, J. and Holmström, J. (2001) The Reception Box Impact on Home

Delivery Efficiency in the E-Grocery Business, International Journal of Physical Distribution and

Logistics Management, 31(6): 414–426.

Laporte, G. (1992) The Vehicle Routing Problem: An Overview of Exact and Approximate

Algorithms, European Journal of Operational Research 59 (1992) 345 – 358.

Laporte, G. (2007) What You Should Know about the Vehicle Routing Problem. Naval Research

Logistics 54(8): 811.

Laporte, G. (2009) Fifty Years of Vehicle Routing. Transportation Science 43(4): 408–416.

Lardner, J. (1998) “Please Don’t Squeeze the Tomatoes Online”: Supermarkets of the

Future Have No Aisles, U.S. News & World Report, November 9, 51–52.

Larson, R.C. and Odoni, A.R. (2007) Applications of Network Models in Anonymous Urban

Operations Research Dynamic Ideas, Belmont MA.

78

Luo, Y. and Schonfeld, P. (2007) A Rejected-Reinsertion Heuristic for the Static Dial-A-Ride

Problem. Transportation Research Part B 41(7), 736–755.

Malandraki, C. and Daskin, M.S. (1992) Time Dependent Vehicle Routing Problems:

Formulations, Properties and Heuristic Algorithms. Transportation Science 26(3): 185–200.

Mendelson, H. (2001) Webvan: The New and Improved Milkman Ec-31, Graduate School of

Business, Stanford University, California, March 2001.

Mintel (2007), Home Shopping UK, summary available from www.mintel.co.uk

Murphy, A.J. (2007) Grounding the Virtual: The Material Effects Of Electronic Grocery

Shopping, Geo-forum 38: 941–953.

Powell, W.B., Snow, W. and Cheung, R.K. (2000) Adaptive Labeling Algorithms for the

Dynamic Assignment Problem. Transportation Science 34(1): 50–66.

Raijas, A. (2000), The Consumer Benefits and Problems in Electronic Grocery Store, Working

Paper, Helsinki School of Economics, Electronic Commerce Institute

Reinhardt, A. (2001) Tesco Bets Small – and Wins Big, Business Week Online, October 1,

http://www.Businessweek.com/Magazine/Content/01_40/B3751622.html (accessed on

12/10/2013)

http://www.businessweek.com/Magazine/Content/01_40/B3751622.html

79

Repoussis, P.P. and Tarantilis, C.D. Solving the Fleet Size and Mix Vehicle Routing Problem

with Time Windows via Adaptive Memory Programming. Transportation Research Part C, 18

(5), 695 - 712

Soeanu, Andrei, Sujoy Ray, Mourad Debbabi, Jean Berger, Abdeslem Boukhtouta, Ahmed

Ghanmi (2011) A Decentralized Heuristic for Multi-Depot Split-Delivery Vehicle Routing

Problem, Proceedings of the IEEE International Conference on Automation and Logistics

Chongqing, China, August 2011.

Thompson, R.G. and Hassall, K.P. (2012) A Collaborative Urban Distribution Network, Procedia

– Social and Behavioral Sciences 39: The Seventh International Conference on City Logistics.

Xianzhong Mark Xu and Roberts, M. (2004) Internet Shopping Model and Customer

Perceptions: A Study of UK Supermarkets, Idea Group Publishing.

Yrjölä, H. (2001) Physical Distribution Considerations for Electronic Grocery Shopping,

International Journal of Physical Distribution and Logistics Management, 31(10): 746–761.

@ Your Home (2001) @ Your Home – New Markets for Customer Service and Delivery, Report

to Retail Logistics Task Force of Foresight Programme, UK,

http://www.foresight.gov.uk/servlet/docviewer/doc=2857/ (accessed on 20/11/2013)

http://www.foresight.gov.uk/servlet/DocViewer/doc=2857/

80

APPENDICES

Appendix A

TT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 2.00 17.40 3.90 12.94 17.16 21.96 30.21 25.47 20.67 24.27 18.15 13.20 27.30 24.15 30.27 29.70 30.76 34.28 35.30 42.22 38.71 35.68 29.55 32.11

2 17.40 2.00 21.30 13.86 9.65 4.84 13.38 8.63 13.16 16.76 22.19 27.14 41.24 28.19 22.77 12.87 15.87 17.45 20.41 27.34 32.37 28.17 33.59 37.79

3 3.90 21.30 2.00 9.04 13.26 18.06 26.31 21.57 16.77 20.37 14.25 9.30 23.40 20.25 26.37 25.80 26.86 30.38 31.40 38.32 34.81 31.78 25.65 28.21

4 12.94 13.86 9.04 2.00 4.21 9.02 17.27 12.52 7.72 11.32 8.34 13.28 27.38 14.34 17.33 16.76 17.81 21.33 22.35 29.28 26.94 22.74 19.74 23.94

5 17.16 9.65 13.26 4.21 2.00 4.81 13.05 8.31 3.51 7.11 12.55 17.49 31.59 18.55 13.12 12.54 13.60 17.12 18.14 25.07 22.73 18.53 23.95 28.15

6 21.96 4.84 18.06 9.02 4.81 2.00 8.54 3.79 8.32 11.92 17.36 22.30 36.40 23.36 17.93 8.04 11.04 12.62 15.58 22.50 27.54 23.34 28.76 32.96

7 30.21 13.38 26.31 17.27 13.05 8.54 2.00 4.74 9.54 13.14 20.34 25.29 39.03 26.05 19.15 8.99 11.99 8.10 16.53 20.40 27.62 24.56 31.16 34.22

8 25.47 8.63 21.57 12.52 8.31 3.79 4.74 2.00 4.80 8.40 15.60 20.55 34.65 21.31 14.41 4.24 7.24 8.82 11.78 18.71 24.02 19.82 26.42 30.62

9 20.67 13.16 16.77 7.72 3.51 8.32 9.54 4.80 2.00 3.60 10.80 15.75 29.85 16.51 9.61 9.03 10.09 13.61 14.63 21.56 19.22 15.02 21.62 25.82

10 24.27 16.76 20.37 11.32 7.11 11.92 13.14 8.40 3.60 2.00 7.20 12.15 26.25 12.91 6.01 5.43 6.49 10.01 11.03 17.96 15.62 11.42 18.02 22.22

11 18.15 22.19 14.25 8.34 12.55 17.36 20.34 15.60 10.80 7.20 2.00 4.95 19.05 6.00 12.90 12.63 13.69 17.21 18.23 25.16 22.20 18.00 11.40 15.60

12 13.20 27.14 9.30 13.28 17.49 22.30 25.29 20.55 15.75 12.15 4.95 2.00 14.10 10.95 17.85 17.58 18.64 22.16 23.18 30.10 25.51 22.95 16.35 18.91

13 27.30 41.24 23.40 27.38 31.59 36.40 39.03 34.65 29.85 26.25 19.05 14.10 2.00 14.41 21.02 31.68 30.10 30.93 25.56 18.63 11.41 15.61 9.01 4.81

14 24.15 28.19 20.25 14.34 18.55 23.36 26.05 21.31 16.51 12.91 6.00 10.95 14.41 2.00 6.90 18.34 18.08 22.92 13.54 20.05 16.20 12.00 5.40 9.60

15 30.27 22.77 26.37 17.33 13.12 17.93 19.15 14.41 9.61 6.01 12.90 17.85 21.02 6.90 2.00 11.44 11.18 16.02 6.64 13.46 9.61 5.41 12.01 16.21

16 29.70 12.87 25.80 16.76 12.54 8.04 8.99 4.24 9.03 5.43 12.63 17.58 31.68 18.34 11.44 2.00 3.00 4.58 7.54 14.47 21.05 16.85 23.45 27.65

17 30.76 15.87 26.86 17.81 13.60 11.04 11.99 7.24 10.09 6.49 13.69 18.64 30.10 18.08 11.18 3.00 2.00 7.58 4.54 11.47 18.69 16.59 23.19 25.29

18 34.28 17.45 30.38 21.33 17.12 12.62 8.10 8.82 13.61 10.01 17.21 22.16 30.93 22.92 16.02 4.58 7.58 2.00 12.12 12.30 19.52 20.35 26.95 26.12

19 35.30 20.41 31.40 22.35 18.14 15.58 16.53 11.78 14.63 11.03 18.23 23.18 25.56 13.54 6.64 7.54 4.54 12.12 2.00 6.93 14.15 12.05 18.65 20.75

20 42.22 27.34 38.32 29.28 25.07 22.50 20.40 18.71 21.56 17.96 25.16 30.10 18.63 20.05 13.46 14.47 11.47 12.30 6.93 2.00 7.22 8.05 14.65 13.82

21 38.71 32.37 34.81 26.94 22.73 27.54 27.62 24.02 19.22 15.62 22.20 25.51 11.41 16.20 9.61 21.05 18.69 19.52 14.15 7.22 2.00 4.20 10.80 6.60

22 35.68 28.17 31.78 22.74 18.53 23.34 24.56 19.82 15.02 11.42 18.00 22.95 15.61 12.00 5.41 16.85 16.59 20.35 12.05 8.05 4.20 2.00 6.60 10.80

23 29.55 33.59 25.65 19.74 23.95 28.76 31.16 26.42 21.62 18.02 11.40 16.35 9.01 5.40 12.01 23.45 23.19 26.95 18.65 14.65 10.80 6.60 2.00 4.20

24 32.11 37.79 28.21 23.94 28.15 32.96 34.22 30.62 25.82 22.22 15.60 18.91 4.81 9.60 16.21 27.65 25.29 26.12 20.75 13.82 6.60 10.80 4.20 0.00

Sioux Falls network cost (travel time) matrix

81

Before - After 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

1 0 1 1 5 2 3 5 8 5 13 5 2 5 3 5 5 4 1 3 3 1 4 3 1

2 1 0 1 2 1 4 2 4 2 6 2 1 3 1 1 4 2 0 1 1 0 1 0 0

3 1 1 0 2 1 3 1 2 1 3 3 2 1 1 1 2 1 0 0 0 0 1 1 0

4 5 2 2 0 5 4 4 7 7 12 14 6 6 5 5 8 5 1 2 3 2 4 5 2

5 2 1 1 5 0 2 2 5 8 10 5 2 2 1 2 5 2 0 1 1 1 2 1 0

6 3 4 3 4 2 0 4 8 4 8 4 2 2 1 2 9 5 1 2 3 1 2 1 1

7 5 2 1 4 2 4 0 10 6 19 5 7 4 2 5 14 10 2 4 5 2 5 2 1

8 8 4 2 7 5 8 10 0 8 16 8 6 6 4 6 22 14 3 7 9 4 5 3 2

9 5 2 1 7 8 4 6 8 0 28 14 6 6 6 9 14 9 2 4 6 3 7 5 2

10 13 6 3 12 10 8 19 16 28 0 40 20 19 21 40 44 39 7 18 25 12 26 18 8

11 5 2 3 15 5 4 5 8 14 39 0 14 10 16 14 14 10 1 4 6 4 11 13 6

12 2 1 2 6 2 2 7 6 6 20 14 0 13 7 7 7 6 2 3 4 3 7 7 5

13 5 3 1 6 2 2 4 6 6 19 10 13 0 6 7 6 5 1 3 6 6 13 8 8

14 3 1 1 5 1 1 2 4 6 21 16 7 6 0 13 7 7 1 3 5 4 12 11 4

15 5 1 1 5 2 2 5 6 10 40 14 7 7 13 0 12 15 2 8 11 8 26 10 4

16 5 4 2 8 5 9 14 22 14 44 14 7 6 7 12 0 28 5 13 16 6 12 5 3

17 4 2 1 5 2 5 10 14 9 39 10 6 5 7 15 28 0 6 17 17 6 17 6 3

18 1 0 0 1 0 1 2 3 2 7 2 2 1 1 2 5 6 0 3 4 1 3 1 0

19 3 1 0 2 1 2 4 7 4 18 4 3 3 3 8 13 17 3 0 12 4 12 3 1

20 3 1 0 3 1 3 5 9 6 25 6 5 6 5 11 16 17 4 12 0 12 24 7 4

21 1 0 0 2 1 1 2 4 3 12 4 3 6 4 8 6 6 1 4 12 0 18 7 5

22 4 1 1 4 2 2 5 5 7 26 11 7 13 12 26 12 17 3 12 24 18 0 21 11

23 3 0 1 5 1 1 2 3 5 18 13 7 8 11 10 5 6 1 3 7 7 21 0 7

24 1 0 0 2 0 1 1 2 2 8 6 5 7 4 4 3 3 0 1 4 5 11 7 0

Sioux – Falls before after matrix

82

Appendix B

8 stores:

Travel times in scenario 1

Travel times per client/carrier/customer in scenario 1

83


Delivery truck travel time in scenario 2

84

Number of VRP trips in scenario 2


85

Travel time per client/carrier/customer in scenario 3


86

Delivery truck travel times in scenario 4

Number of truck delivery tours in scenario 4

87

12 stores:



88



89



90



91



92

16 stores:



93



94



95



96



97

Appendix C

8 stores:

Travel times of all scenarios

98

Delivery truck travel time in scenarios 2 and 4

99

12 stores:


100


101

16 stores:


102


A Peer-to-Peer Matching System for Grocery Home...

Documents

Transcript of A Peer-to-Peer Matching System for Grocery Home...