DECISION SUPPORT SYSTEM FOR REAL-TIME URBAN FREIGHT MANAGEMENT Hanna Grzybowska...

DECISION SUPPORT SYSTEM FOR

REAL-TIME URBAN FREIGHT MANAGEMENT

LOGISTICS BASE

LOGISTICS BASE

CEDM City Distribution Terminal

CEDM City Distribution Terminal

Hanna Grzybowska [email protected]

andJaume Barceló

[email protected]

Dept. of Statistics and Operations Research

CENIT (Center for Innovation in Transport) www.cenit.es

Universitat Politècnica de Catalunya

New customer Calls at time t

Position of vehicle 1 at

time t

New route for vehicle 2

Position of vehicle 2 at

time t

New route for vehicle 1

mailto:[email protected]

mailto:[email protected]

http://www.cenit.es/

CONCEPTUAL APPROACHES TO REAL TIME FLEET MANAGEMENT AND THE

ROLE IF ICT TECHNOLOGIES

2The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

Supplier

Supplier’s Supplier

Manufacturer

Wholesaler/Distributor

Retailer

CITY AREA- client

CITY LOGISTICS SCENARIO

3

Fleet management in urban areas has to explicitly account for the dynamics of traffic conditions leading to congestions and variability in travel times affecting the distribution of goods and the provision of services

City Logistics activities are impacted by traffic congestion must consider time-varying traffic congestion and operational constraints in routing and logistics optimization models

Last-mile logistics

Decisions must take into account all factors conditioning the problem Decision Support System (DSS)

The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

4

GPS Satellites

Vehicle’s Position

Vehicle

Vehicle’s Data

Updated Route Updated Route

Cellular Antenna

Vehicle’s Data

• Global Positioning System (GPS)• GPS device pickups signal from satellites• GPS device calculates position• Establish communication with network• Vehicle Data is sent to Fleet Management Center.• Fleet Manager updates routes and returns them to vehicle.

Fleet management Centre

ICT TECHNOLOGIES AND REAL-TIME FLEET MANAGEMENT


BARCELONA’S TRAFFIC INFORMATION SYSTEM

5

Current and Short-Term Forecasted Travel Time


6

Initial Demand and Fleet Specifications

Initial Demand and Fleet Specifications

ROUTING AND SCHEDULING

MODULE

ROUTING AND SCHEDULING

MODULE

Initial Operational Plan

DYNAMIC ROUTER AND SCHEDULER


Dynamic Operational Plan

Dynamic Operational Plan

CONCEPTUAL SCHEME FOR REAL-TIME FLEET

MANAGEMENT SYSTEMS

(Adapted from Regan, Jaillet, Mahmassani)

Known/predicted demand for

service

Known/predicted demand for

service

Known/predicted driver/vehicle

availability

Known/predicted driver/vehicle

availability

Load Acceptance Policies

Load Acceptance Policies

Pool of accepted demands

REAL-TIME INFORMATION

• New demandsUnsatisfied demandsTraffic conditions Fleet availability

ASSUMING:

• A given Initial Operational Plan• Fleets equipped with AVL tecnologies (i.e. GPS+GPRS)• A Real-Time Information System

OBJECTIVE:

• Design a DSS for Dynamic Routing and Scheduling


New Customer Request

Cancelation of Service

Arrival of Vehicle at Location

Changes in Travel Times

Delays in Delivery Start Times

Breakdown of Vehicle

Changes in Time Windows Bounds


Insertion Heuristics

Local Search Operators

TABU SEARCH

Reactive Strategies• One-by-one• Pooling

Preventive Strategies• Vehicle Relocation

Waiting Strategies• Drive-First• Wait-First• Combined

DYNAMIC EVENT AND VEHICLE TRACKING

SIMULATOR

NEW ROUTING PLAN

EX

TE

RN

AL

EV

EN

TS

INT

ER

NA

L E

VE

NT

S

INTERNAL EVENTS DYNAMIC MONITORING

SOLVING STRATEGIES

PROPOSED DECISION SUPPORT SYSTEM FOR REAL-TIME

FLEET MANAGEMENT


DYNAMIC ROUTER AND SCHEDULER • Decide where to insert the new customer.• New customer arrives at time t > 0. • Fleet vehicles can be in one of the three status:

– In service at some customer i (SER).– Moving to the next planned customer on the route or waiting at the

customer location to start service within the time window. (MOV).– Idle at the Depot, without a previously assigned route (IDL).– Waiting at the client’s i (WAIT)

• This status determines when a vehicle should be diverted from its current route, be assigned to a new one if is idle or keep the planned trip.

• Whenever a new customer arrives, the status of a vehicle must be known to compute travel times for this new customer.

• If the vehicle has a MOV status, the travel time is computed from the current position of the vehicle to the location of the new customer.

• If the vehicle has IDL status, the travel time is just the travel time from the depot to the new customer.

• If the vehicle is has SER status, the amount of time needed to arrive to the customer is the remaining service time at the current customer plus the travel time between the current customer and the new customer.

• If the vehicle has WAIT status it can be send to other client.The bi-weekly logistics and routing research group meeting, NICTA, 29th July 2011

1. Initial fleet scheduling

2. Start services while tracking vehicles

3. New client call is received at time t

4. Check vehicle positions and current travel times

5. Reject unfeasible routes (insertion / diversion)

6. Recalculate routes.7. Execute new plan.

In Transit

In Transit

In Service

In Service

New Client

DYNAMIC ROUTING AND SCHEDULING: Example of Dynamic Insertion Heuristic


section of the original route

section removed from the original route

new section of modified original route

new client request registered at time t

initially known client already visited by the assigned vehicle

P2

P1

D2

D1

P4

P3

D3

D4

D5

P5

P6

D6

DEPOT

1

2

3

DYNAMIC REROUTING WITH REAL-TIME INFORMATION (ADJUSTMENT SOF ROUTE 2 - GREEN AND 3 - BLUE )

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section removed from the original route

new section of modified original route

new client request registered at time t

initially known client already visited by the assigned vehicle (before time instant t)

initially known client still not visited by the assigned vehicle (before time instant t)

on of the fleet vehicle serving route 2 at time t


AVERAGE LINK TRAVEL TIMES AND VARIANCES (Barcelona “Eixample”-CBD)

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Working exclusively with average travel times may lead to significant deviations in city logistics problems where temporality is an

important factor such as the VRPTW.

A report on urban distribution in Barcelona (Robusté, 2005) found that:There were more than 62,000 commercial outlets more than 60,000 daily unloading operationsService time between 13 and 16 minutes50% of deliveries were made by 11:00 hrs


DEALING WITH TIME-DEPENDENT TRAVEL TIMES

• di = departure time from client i

• si = sevice time for client i

• Tij(di) = travel time from i to j when departing at time di from client i

• Tij(di) Tij(di’)

si

Sj

Sk

i

j

k

di

dj

dk )

i(d

ijT

)j

S)i

(dij

Ti

(djk

T)j

(djk

T

12

Ad Hoc version of the algorithm of Ziliaskopoulos and Mahmassani

Calculates the time-dependent shortest paths from all nodes of to one, specified as destination point.


REAL-TIME TRAFFIC INFORMATION SYSTEM

REAL-TIME FLEET MONITORING SYSTEM


FRAMEWORK


TIME-DEPENDENT TRAVEL TIMES DATA FORECASTING MODULE

MULTIPLE SIMULATION ITERATIONS

“future”travel times

actualtravel times

Ajtcttctc Hj

Pjj ),())(1()()((t)F

historical travel times data base

SINGLE SIMULATION ITERATION

t0

TRAFFIC SIMULATOR

cjH(t0) cj

H(t0+1) cjH(t0+T)…cj

P(t0) cjP(t0+1) cj

P(t0+T)…

t0

Historical Data BaseActual Travel Times

+Expected Travel Times

ATIS

1

2)(

Tt


Time-Dependent Routing Plan:

sequences of clients to visit

Time-Dependent Shortest Paths: sequences of nodes between the clients for the

vehicle to go through

Present Travel Times Data Base

Vehicle Fleet Performance Simulator

Vehicles’ current:• status• position• load

List of clients:• served• omitted

INPUT

OUTPUT

e.g.: instant of appearance of

new event

Depending on the current vehicle’s status, the value of time left to complete:• service• waiting• trip on the current arc

EXTERNAL TRIGGER

INTERNAL EVENT e.g.: • arrival to a client whose TW is closed• end of providing a client with service

VEHICLE FLEET PERFORMANCE SIMULATOR


MATHEMATICAL FORMULATION OF THE VEHICLE ROUTING PROBLEM WITH PICKUP AND DELIVERIES AND TIME WINDOWS

Kk Aji

ijkijk

k

xc),(

Min

kijk

kijk

kko

kikkin

kkiki

kjkjikijk

kkinkiniik

kiiki

kjkijkiikijk

koDjkkdi

kdNiijk

koNiijk

kdPjkjko

Nik

Njkinjijk

Kk kdNjijk

AjiKkx

AjiKkx

KkL

DinKkqCL

PiKkCLq

AjiKkLqLx

PiKkTtT

ViKkbTa

AjiKkTtSTx

Kkx

Kkxx

Kkx

PiKkxx

Pix

k

kk

k

k k

k

),( , }1,0{

),( , 0

0

, 0

,

),( , 0

,

,

),( , 0)(

1

0

1

, 0

1

),(

,

,,,

),(,

,,

,,

Subject to:

Each customer is served by the same vehicle

Time windows feasibility

Precedence requirement

Route for vehicle k, from o(k) to d(k)

Route and vehicle loads requirements


PICK UP & DELIVERY PROBLEMS WITH TIME WINDOWS

18

• The route of each vehicle starts and ends at the depot., • Each customer must be served within a time interval

[ai,bi] during Si units of time. • Each customer must be visited exactly once by

exactly one vehicle. Pairs of associated clients are serviced by the same

vehicle (pairing constraint), Cargo’s sender is always visited before its recipient

(precedence constraint), • For each customer, the pickup location i+ must be served

in the same route and before delivery location i- • The sum of demands of customers on a route cannot

exceed vehicle capacity. The entire routing cost is minimized.


COMPOSITE HEURISTICS TO CONTRUCT THE INITIAL AND THE DYNAMIC ROUTING AND SCHEDULING

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ALGORITHM PROVIDING THE INITIAL SOLUTION

Based on the Simple Pairing Approach

Based on the Sweep Algorithm

Based on Customers’ Aggregation Areas

PARALLEL TABU SEARCH PROCEDUREUSING SIMULTANEOUSLY TWO LOCAL SEARCH HEURISTICS

Pickup and Delivery Pair Shift Operator

Pickup and Delivery Pair Exchange Operator

POST-OPTIMIZATION

Pickup and Delivery Pair Rearrange Operator

2-opt procedure


P2

D1

P6

D6

DEPOT

D7P7

P3 D3

P5

D5

D4

D2

P4

P1

NEIGHBOURHOOD 1 NEIGHBOURHOOD 2

NEIGHBOURHOOD 3

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Algorithm Initial Solution

1. Order known clients (Client’s Sorting Pre-Processing Algorithm using the Definitions of Customers’ Aggregation Areas

2. Create initial solution • Select the farthest client in the listing • Find its PD partner and delete them both from the listing • IF it is the first iteration OR it is not possible to insert the pair into existing route THEN create route: depot-pickup customer-delivery customer-depot • ELSE insert the pair in location causing minimal increment of the cost of the existing route


PARALLEL TABU SEARCH

Works with two concurrent different search processes:

•The Pickup and Delivery Pair Shift Operator (NPDPSO)

•The Pickup and Delivery Pair Exchange Operator (NPDPEO).


POST-OPTIMIZATION PROCESS

Post-optimization is realised by: Pickup and Delivery Pair Shift Operator


COMPUTATIONAL EXPERIMENTS AND RESULTS


THE SELECTED SCENARIO: Barcelona’s CBD


KTS seminar at Linkoping University, Norrkoping 16.03.2011

Clients

Depot

• Downtown area of Barcelona.• Commercial activities and tourism. • It covers 747 hectares• 1,570 links y 721 nodes.

THE SIMULATION MODEL


• 100 Customers:• Constant demand and service time.• Time windows

• 1 Depot; Opening hours 08:00 – 16:00• Fleet: 8 homogeneous vehicles with large capacity. • Vehicles are equipped with GPS and real-time

communication system with fleet manager.• Real-time traffic information system.• Simulation time: 10 hours (07:00 – 17:00)

MODELING ASSUMPTIONS


Table 1. Collection of the DSS performance testing scenarios

TESTING SCENARIOS

Scenario No Initially Known Clients TDSP Calculator Triggered Traffic Information Used Module

1 100 once at the start average static IRSM

2 100 at the start and after multiple times time-dependant IRSM+DRSM with CRRM

3 80 at the start and after multiple times time-dependant IRSM+DRSM with CRRM

Initial solution Final solution

ScenarioIniNor

Total TravelTime [s]

TotalWaiting Time [s]

SolutionCost [s]

FinNor

Total Travel Time [s]

Total WaitingTime [s]

SolutionCost [s]

ServiceLevel

1 5 206022 16695 222717.0 5 213207 17189 230396 100%

2 3 149144 10372 159516.0 3 155825 10449 166274 100%

3 3 136028 10393 146420.0 4 188248 10254 198501 100%

Table 2. Results on fleet performance depending on the traffic information input


TESTING SCENARIOS, USING SPI

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No

scenario TW No of

static clients Method Travel

time data TDSP Calculator

used Forecaster

used 1 narrow 100 SPI perfect once - 2 wide 100 SPI perfect once - 3 narrow 100 SPI AS once - 4 wide 100 SPI AS once - 5 narrow 100 SPI TD multiple times multiple times 6 wide 100 SPI TD multiple times multiple times 7 narrow 80 SPI TD multiple times multiple times 8 wide 80 SPI TD multiple times multiple times 9 narrow 60 SPI TD multiple times multiple times

10 wide 60 SPI TD multiple times multiple times 11 narrow 50 SPI TD multiple times multiple times 12 wide 50 SPI TD multiple times multiple times 13 narrow 40 SPI TD multiple times multiple times 14 wide 40 SPI TD multiple times multiple times 15 narrow 20 SPI TD multiple times multiple times 16 wide 20 SPI TD multiple times multiple times


TESTING SCENARIOS USING CSR

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No

scenario TW No of

static clients Method Travel

time data TDSP Calculator

used Forecaster

used 17 narrow 100 CSR perfect once - 18 wide 100 CSR perfect once - 19 narrow 100 CSR AS once - 20 wide 100 CSR AS once - 21 narrow 100 CSR TD multiple times multiple times 22 wide 100 CSR TD multiple times multiple times 23 narrow 80 CSR TD multiple times multiple times 24 wide 80 CSR TD multiple times multiple times 25 narrow 60 CSR TD multiple times multiple times 26 wide 60 CSR TD multiple times multiple times 27 narrow 50 CSR TD multiple times multiple times 28 wide 50 CSR TD multiple times multiple times 29 narrow 40 CSR TD multiple times multiple times 30 wide 40 CSR TD multiple times multiple times 31 narrow 20 CSR TD multiple times multiple times 32 wide 20 CSR TD multiple times multiple times


SERVICE LEVELS IN SCENARIOS WITH DYNAMIC CLIENTS

30

80

85

90

95

100

105

SPI narrow TW

SPI w ide TW

CSR narrow TW

CSR w ide TW

No stat. 100 80 60 50 40 20cust. (%)

service level


TOTAL TRAVEL TIMES IN SCENARIOS WITH DYNAMIC CLIENTS

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0

20000

40000

60000

80000

100000

120000

SPI narrow TW

SPI w ide TW

CSR narrow TW

CSR w ide TW

total travel time [s]

No stat. 100 80 60 50 40 20cust. (%)


CONCLUSIONS• The obtained results prove that the performance of the fleet

strongly depends on the traffic information used to create and update the routing and scheduling plan.

• The usage of the time-dependent shortest paths, computed whenever a new even occurs, brings better results than when the average travel times’ estimates are employed, even in the case when not all the information on the clients to be served is initially available.

• The comparison of the initially planned and performed routing and scheduling plan indicates that the total travel time, total waiting time and the solution cost are always higher when executed. It is due to the fact that the plan does not take into consideration the most recent and forecasted changes in the traffic flow.

• No customer is left unserved.


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THANK YOU VERY MUCH FOR YOUR ATTENTION

DECISION SUPPORT SYSTEM FOR REAL-TIME URBAN FREIGHT MANAGEMENT Hanna Grzybowska...

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