Ivanov 2015

ScienceDirectIFAC-PapersOnLine 48-3 (2015) 1700–1707

Available online at www.sciencedirect.com

2405-8963 © 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.Peer review under responsibility of International Federation of Automatic Control.10.1016/j.ifacol.2015.06.331

Dmitry Ivanov et al. / IFAC-PapersOnLine 48-3 (2015) 1700–1707

© 2015, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

Supply Chain Design With Disruption Considerations:

Review of Research Streams on the Ripple Effect in the Supply Chain

Dmitry Ivanov1, Alexandre Dolgui

2, Boris Sokolov

3,4

1Berlin School of Economics and Law, Department of Business Administration

Chair of International Supply Chain Management, 10825 Berlin, Germany

Phone: +49 3085789155; E-Mail: [email protected] 2Ecole Nationale Supérieure des Mines, UMR CNRS 6158, LIMOS,

158, Cours Fauriel, 42023 Saint-Etienne cedex 2, France E-Mail: [email protected] 3St. Petersburg Institute for Informatics and Automation of the RAS (SPIIRAS)

V.O. 14 line, 39 199178 St. Petersburg, Russia; E-Mail: [email protected] 4University ITMO, St. Petersburg, Russia; E-Mail: [email protected]

Abstract: This study aims at analysing recent research on supply chain design with disruption considera-

tions in terms of the ripple effect in the supply chain. It develops different dimensions of the ripple-effect

and summarizes recent developments in the field of supply chain disruption management from a multi-

disciplinary perspective. We observe that the analysis of how to achieve planned economic performance in

a real-time, uncertain and perturbed execution environment is a vital and up-to-date issue in many supply

chains. Te ripple effect can be the phenomenon that is able to consolidate research in supply chain

tion management and recovery similar to the bullwhip effect regarding demand and lead time fluctuations.

This may build the agenda for future research on supply chain dynamics, control, continuity, and disrup-

tion management, making supply chains more robust, adaptable, and profitable. Copyright © 2015 IFAC

Keywords: supply chain, ripple effect, dynamics, control, resilience, robustness, disruption management,

event management, quantitative analysis, information technology.

1. INTRODUCTION

Supply chain design (SCD) has been a visible and influential

topic in the field of production, operations, and supply chain

management (SCM) over the past two decades. A large num-

ber of approaches have been proposed for the design of sup-

ply chains (SC) (Drezner 1995, Daskin 1995, Amiri 2006,

Melo et al. 2009, Georgiadis et al. 2011, Constantino et al.

2012). Typically, cost or service level optimization has been

included in the objective functions. In many cases, inventory,

lead-time, and demand fluctuations have been integrated into

those models (Sourirajan et al., 2009, Sadjady and Davoud-

pour 2012, Kumar and Tiwari 2013, Askin et al. 2014).

The risks of demand and supply uncertainty are related to the

random uncertainty and business-as-usual situation. Such

risks are also known as recurrent or operational risks (Cho-

pra et al. 2007, Wilson 2007, Singh et al. 2012). SC managers

achieved significant improvements at managing the SCs and

mitigating recurrent SC in risks through improved planning

and execution (Chopra and Sodhi 2014). Disruptive risks

represent now a new challenge for SC managers who face the

ripple effect that arises from vulnerability, instability, and

disruptions in SCs (Ivanov et al. 2014a).

The ripple effect describes the impact of a disruption on SC

performance and disruption-based scope of changes in the SC

structures and parameters (Ivanov et al. 2014a). Following a

disruption, its effect ripples through the SC. The scope of the

rippling and its impact on economic performance depends

both on robustness reserves (e.g., redundancies like inventory

or capacity buffers) and speed and scale of recovery measures

(Sheffi and Rice 2005, Tomlin 2006, Bode et al. 2011,

Ivanov and Sokolov 2013, Kim and Tomlin, 2013). Since the

research community distinguishes between operational and

disruption risks, the ripple-effect can be considered for dis-

ruption risks while for operational risks the bullwhip-effect is

typically studies. The differences are presented in Table 1.

Table 1: Ripple effect and bullwhip effect

Feature Ripple-Effect Bullwhip-Effect

Risks Disruptions (e.g., a plant

explosion)

Operational (e.g., de-

mand fluctuation)

Affected

areas

Structures and critical

parameters (like lead-time

and inventory)

Critical parameters like

lost sales

Recov-

ery

Middle- and long-term;

coordination efforts and

investments

Short-term coordination

to balance demand and

supply

Affected

Perfor-

mance

Output performance like

annual revenues

Current performance

like daily stock-

out/overage costs

Proceedigs of the 15th IFAC Symposium onInformation Control Problems in ManufacturingMay 11-13, 2015. Ottawa, Canada

Copyright © 2015 IFAC 1770




2, Boris Sokolov

3,4

















1. INTRODUCTION






































explosion)


mand fluctuation)

Affected

areas



and inventory)


lost sales

Recov-

ery



investments



supply

Affected

Perfor-

mance


annual revenues

Current performance

like daily stock-

out/overage costs






2, Boris Sokolov

3,4

















1. INTRODUCTION






































explosion)


mand fluctuation)

Affected

areas



and inventory)


lost sales

Recov-

ery



investments



supply

Affected

Perfor-

mance


annual revenues

Current performance

like daily stock-

out/overage costs



Dmitry Ivanov et al. / IFAC-PapersOnLine 48-3 (2015) 1700–1707 1701

At the design stage (the so called proactive stage), contin-

gency plans or backup planning (e.g., alternative suppliers or

shipping routes) are developed (Knemeyer et al. 2009, Cui et

al. 2010, Benyoucef et al. 2013, Li et al. 2013). During the

execution (the so called reactive stage), the recovery must

happen quickly to expedite stabilization and adaptation in

order to ensure SC continuity and avoid long-term impact

(Sheffi and Rice 2005, Simchi-Levi et al. 2014, Chopra and

Sodhi 2014).

In this setting, it is vital to extend existing SCD models by

integrating objectives like flexibility, robustness, stability,

resilience into multi-criteria SCD selection procedures

(Snyder and Daskin 2005, Wilson 2007, Klibi et al. 2010,

Peng et al. 2011, Baghalian et al. 2013). Such research can

provide professionals with useful tools to analyse perfor-

mance and resilience of SCs simultaneously.

2. STATE-OF-THE-ART

2.1 Pro-active approach

Studies by Snyder and Daskin (2005), Wilson (2007), Qi and

Chen (2010), Cui et al. (2010), Klibi et al. (2010), Schmitt

and Singh (2012), Lim et al. (2010, 2013), Peng et al. (2011),

Li et al. (2013), Ivanov and Sokolov (2013), Kim and Tomlin

(2013), and Ivanov et al. (2014a) indicated that understanding

and finding SCD with effective and efficient constellations of

economic performance, complexity, robustness, flexibility,

adaptability and resilience is a promising research area with

high practical applicability.

2.1.1 Mixed-integer programming

Mixed-integer programming (MIP) with application to relia-

ble SCD has been a broad research avenue over the past ten

years. More precisely, incapacitated fixed charge location

model – UFL and P-median problem have been mostly stu-

died. The reliable location model was first introduced by

Snyder and Daskin (2005). UFL model aims at finding op-

timal SCD with assignments of the customers to the locations

with the objective to minimize the sum of fixed and transpor-

tation costs in the SC. The study by Snyder and Daskin

(2005) assumed equal estimations of probability failures for

all the SC nodes and considered a case with 49 cities in U.S.

This model has been extended by Shen et al. (2009) and Cui

et al. (2010) by relaxing the assumption on homogenous fail-

ure probability. In addition, Cui et al. (2010) paid attention to

the fact that total transportation costs in the SC should not

increase after a disruption. They model provides the solution

without an increase in transportation costs for both normal

and disruptive modes. For medium-size problems, as docu-

mented in Li et al. (2013) Lagrangian relaxation also allows

finding optimal solution in reasonable time.

Next development of MIP models in the ripple-effect context

can be considered regarding the facility fortification. Lim et

al. (2010) incorporated a totally reliable bаck-up supplier that

is used if a primary supplier is destroyed. The related costs

are incorporated into the objective function but the fortifica-

tion budget remained incapacitated. Li et al. (2013) extended

this model by introducing the limits on the fortification budg-

et in a single-product case with eight distributors and up to

150 customers.

In addition, inventory considerations have also been included.

Chen et al. (2011) presented a joint inventory-location model

under the risk of probabilistic facility disruptions. Benyoucef

et al. (2013) considers SCD with unreliable suppliers. The

objective is to minimise fixed location costs, inventory and

safety stock costs at the distribution centres and ordering

costs and transportation costs across the network. The non-

linear MIP model is solved with the help of Lagrangian re-

laxation. Inventory management under SC disruption in-

volves nonlinear cost components. Therefore heuristic solu-

tion methods are typically used. In addition, lead-time uncer-

tainty constraints may be included into consideration (Mo-

hebbi et al. 2003, Aсar et al., 2010).

Rafiei et al. (2013) developed a comprehensive model for a

problem statement with multiple products and many periods.

They consider the levels of inventory, back-ordering, the

available machine capacity and labor levels for each source,

transportation capacity at each transshipment node and avail-

able warehouse space at each destination. The problem also

considers the facility fortification by taking into account

backup supplier with reserved capacity and backup trans-

shipment node that satisfies demands at higher price without

disruption facility. The solution to the model is based on a

priority-based genetic algorithm.

2.1.2 Stochastic programming

It is to distinguish between classical stochastic programming

models (Tsiakis et al. 2001, Santoso 2005, Goh et al. 2007)

where demand is uncertain parameter and robust stochastic

programming models (Azaron et al. 2008) where also facility

disruptions and capacity expansion costs are considered to be

uncertain. Recently, Schütz et al. (2009), Iakovou et al.

(2010), Li and Wang (2011), and Baghalian et al. (2013) ex-

tended the existing models by considering demand-side and

supply-side uncertainties simultaneously.

Benyoucef et al. (2013) considers two-period SCD model in

which selected suppliers are reliable in the first period and

can fail in the second period. The corresponding facility loca-

tion/supplier reliability problem is formulated as a non-linear

stochastic programming problem. The authors use Monte

Carlo optimisation approach in combination with the Lagran-

gian relaxation. Sawik (2013) developed a stochastic pro-

gramming model to integrated supplier selection, order quan-

tity allocation and customer order scheduling in the presence

of SC disruption risks.

2.1.3 Fuzzy, robust, and goal optimization

The study by Petrovic et al. (1998) was among first papers on

fuzzy optimization application to SCD with the uncertainty of

demands and external supply. The objective was to determine

the stock levels and order quantities to obtain an acceptable

delivery performance at a reasonable total cost for the SC.

Aliev et al. (2007) applied fuzzy mathematical programming

with a fuzzy objective function solved by genetic algorithm.

INCOM 2015May 11-13, 2015. Ottawa, Canada

1771

1702 Dmitry Ivanov et al. / IFAC-PapersOnLine 48-3 (2015) 1700–1707

Similar to Petrovic et al. (1998), this study considered cus-

tomer demand and production capacity as uncertainties. The

objective was to provide a sound trade-off between maximi-

zation of profit and service level. Constantino et al. (2011)

presented a fuzzy programming approach for the strategic

design of distribution networks.

Gulpınar et al. (2012) formulates a stochastic model for mul-

tiple capacitated facilities that serve customers with a single

product, and a stockout probabilistic requirement as a chance

constraint. Based on robust optimization, they present numer-

ical experiments to illustrate the performance of the different

robust formulations. Pishvaee et al. (2012) presented a robust

possibilistic programming approach to socially responsible

SCD.

2.1.5 Simulation, system science and control theory

Simulation approaches have been proved to be a suitable tool

for analysis of SCD in terms of the ripple effect. Wu et al.

(2007) presented a Petri net-based modeling approach to

model how disruptions propagate through a SC and evaluate

the impact of the disruption on the SC performance. Another

application of Petri net-based simulation to SCs is presented

by Tuncel and Alpan (2010) in order to evaluate the impact

of multiple disruption scenarios (disruptions in demand,

transportation and quality) and possible mitigation actions on

the SC performance.

Monte Carlo approach based on a generalized semi-Markov

process is taken to assess the disruptions caused by a specific

type of hazard on an SC (Deleris and Erhun, 2011). This

model estimates the probability distribution of the loss in the

SC output caused by the occurrence of hazards within the SC.

Zegordi and Davarzani (2012) present an SC disruption anal-

ysis model based on colored Petri nets for better visual repre-

sentation.

Lewis et al. (2013) analyse the disruptions risks at ports-of-

entry with the help of closure likelihood and duration which

are modeled using a completely observed, exogenous Markov

chain. They developed a periodic-review inventory control

model that indicates for studied scenarios that operating mar-

gins may decrease 10% for reasonable-length port-of-entry

closures or eliminated completely without contingency plans,

and that expected holding and penalty costs may increase

20% for anticipated increases in port-of-entry utilization.

Swaminathan et al. (1998) and Surana et al. (2005) applied

agent systems and adaptive principles to SC dynamics analy-

sis. Kamath and Roy (2007) analysed capacity augmentation

decisions for products with short life-cycles and unpredicta-

bly high demand with the help of the system dynamics ap-

proach. Xu et al. (2014) used AnyLogic software and mod-

elled SC as an agent system to study the disruption at suppli-

ers and recovery policies on SC service level.

Schönlein et al. (2013) apply stability analysis based on mul-

ticlass queuing network. The authors study different destabi-

lization inputs and formulate a mathematical program that

minimizes the required network capacity, while ensuring a

desired level of robustness.

To study the impact of transportation disruptions on SC per-

formance, Ivanov et al. (2010) developed a structure dynam-

ics control approach to SCD with the simultaneous considera-

tion of multiple SC structures (i.e., material, information,

product, technology, and finance) and their dynamics. They

presented solution methods based on a combination of optim-

al control and mathematical programming.

Teimoury and Fathi (2013) along with Zhou et al. (2013)

faced the issues of SC resilience through flexibility increase

and applied queuing models to analyse the impacts of post-

ponement on SC performance. Meisel and Bierwirth (2014)

also applied simulation and optimization method to analyse

performance of a make-to-order strategy in presence of un-

certainties.

Recently Ivanov et al. (2013) and (2014b) developed a model

for multi-period and multi-commodity SCD with structure

dynamics considerations. The original idea of these studies is

SC description as a non-stationary dynamic control system

along with a linear programming (LP) model. In contrast to

MIP formulation, they distribute static and dynamic parame-

ters between the LP and control models.

2.2. Re-active approach

Investment in SC protection can help to avoid many prob-

lems with disruptive events. However, it is impossible to

avoid disruption. Therefore, adaptation is needed to change

SC plans, schedules or inventory policies in order to achieve

the desired output performance (Ivanov and Sokolov, 2013).

Modelling SCs and operations using system dynamics and

control theory using differential equations holds great appeal

for the scientific community (Riddals et al. 2000, Sarimveis

et al. 2008, Ivanov et al. 2012, Harjunkoski et al. 2014). This

is because many of the influential characteristics of the prob-

lem can be succinctly expressed in dynamic form. Then, a

vast array of tools and methodologies can be invoked to gain

insight into the system dynamics. Dynamic methods also

have the advantage of being conduits into the adapta-

tion/recovery domain.

Wilson (2007) presented a system dynamics model for a mul-

ti-stage SC. Different transportation disruptions are modeled

and their impact on customer orders fulfillment rate and in-

ventory fluctuations are evaluated. The greatest impact oc-

curs when transportation is disrupted between the Tier-1 sup-

plier and the warehouse.

Schmitt and Singh (2012) presented a quantitative estimation

of the disruption risk in a multi-echelon SC using simulation.

The disruption risk is measured by “weeks of recovery” as

the amplification of the disruption. Carvalho et al. (2012)

analysed impacts of disruptions on lead-time and overall SC

costs using ARENA-based simulation model.

Bensoussan et al. (2007) considered disruptions in informa-

tion structure as possible information delay and incomplete-

ness in the ordering policies for inventory decisions with the

help of linear control theory. Hwarng and Xie (2008) ana-

lysed SC dynamics from chaos theory perspective. Schmitt

(2011) modelled strategies that include satisfying demand


1772


from an alternate location in the network; procuring material

or transportation from an alternative source or route; and

holding strategic inventory reserves throughout the network.

The very extensive area of research on SC adaptation is mod-

el predictive control (MPC) (Wang et al., 2007). In MPC, a

system model and current and historical measurements of the

process are used to predict system behaviour at future pre-

determined times. A control-relevant objective function is

then optimized to calculate a control sequence that must satis-

fy the system constraints.

Applications of MPC to multi-echelon production–inventory

problems and SCs have been examined previously in the lite-

rature. Perea et al. (2000) modelled multi-plant, multi-

product polymer processes through difference equations, and

schedule optimization in an MPC framework. Braun et al.

(2003) developed a decentralized MPC implementation for a

six-node, two-product, and three-echelon demand network

problem. In the study by Puigjaner and Lainez (2008), a mul-

ti-stage stochastic model has been employed. Mastragostino

et al. (2014) analysed SC performance in the presence of un-

certainty in the model parameter and demand considering

service level in the SC.

Apart from control approaches, other techniques have also

been applied in this domain. Unnikrishnan and Figliozzi

(2011) developed a scenario-based model with an adaptive

routing policy. Vahdani et al. (2011) applied fuzzy program

evaluation and review technique to calculate the completion

time of SC operations in the case of a severe disruption. Shao

and Dong (2012) considered supply disruption and reactive

strategies in an assemble-to-order SC with time-sensitive

demand. Ivanov et al. (2013) included transportation reconfi-

guration in the case of SC disruptions into the SCD in a mul-

ti-period model based on a combination of LP and optimal

control. Xu et al. (2014) developed an approach to predict SC

resilience by including recovery measures that uses the anal-

ogy to biological cells with the abilities to self-adaptation and

self-recovery. Paul et al. (2014) analysed series of disruptions

over time and presented an inventory control-based model to

develop optimal recovery policies real time disruption man-

agement for a two-stage batch production–inventory system

with reliability considerations. They consider multiple dis-

ruptions and cases where new disruption may or may not

affect the recovery plan of earlier disruptions.

3. ANALYIS AND OBSERVATIONS

3.1. Types of risks

The analysed literature suggests four basic types of disruptive

risks that should be considered by SC managers:

• Production and transportation disruptions, especially

in global SCs

• Product-related disruption risks due to high degree

of supplier specialization

• Information flow disruptions

• Disruptions in financial flows

First, globalization and outsourcing trends make SC more

complex and less observable and controllable. According to

complexity theory, such systems become more sensitive to

disruptions. Special focus in this area is directed to disrup-

tions in transportation channels. Second, the efficiency para-

digms of lean processes, single sourcing, etc. have failed in

disruption situations. As a consequence, SC became more

vulnerable even to minor perturbations. Any disruption in a

global SC, especially in its supply base, does immediately

affect the entire SC. Third, with the increased specialization

and geographical concentration of manufacturing, disruptions

in one or several nodes affect almost all the nodes and links

in the SC. Fourth, IT became the crucial element of global

SCs, since disruptions in IT may have significant impacts on

disruptions in material flows (Soroor et al. 2009, Tang and

Musa 2011). Fifth, contracting, costs, and profit coordination

issues are important for analyzing disruptive risks in the SCs.

3.2 Pro-active SC protection

Next, recent literature discussed different risk mitigation

strategies. Six elements of pro-active SC protection can be

classified:

• Back-up suppliers, depots and transportation channels

• Inventory and capacity buffers

• SC localization and segmentation

• Product and process flexibility

• Coordination and contracting

• Back-up IT

3.3 Re-active SC recovery policies

Reaction to disruptive events can be performed in five basic

ways depending on the severity of disruptions:

• Parametrical adaptation

• Process and product adaptation

• Structure adaptation

• System adaptation

Parametrical adaptation represents the simplest case where

stabilization and recovery are possible through tuning of

some critical parameters like lead-time or inventory. Process

and product adaptation refer to flexibility reserves. Structure

adaptation considers back-up supplier on contingency trans-

portation plans. System adaption is the highest level of adap-

tation where strategy and organization have to be recovered.

Re-active approaches can be based either on purely recovery

policies without any SC pro-active protection or integrated in

or with the pro-active approaches. We focus on the second

case. Many pro-active techniques actually include the re-

active elements. MIP formulations with facility fortification

consider product shift to back-up suppliers if primary suppli-

ers are disrupted. MPC models implement rolling planning

policy and include the re-planning elements explicitly. Inven-

tory control models also suggest policies for recovery. Simu-

lation techniques consider “what-if” scenarios which can be

used by SC managers in the case of disruption occurrence to

quickly estimate the recovery policies and impacts on opera-

tional and financial performance.


1773


3.4. Method overview

MIP models provide interesting managerial insights and can

be successfully used in the cases where disruption probabili-

ties can be fairly estimated. Second, most of the MIP solu-

tions suggest opening new facilities. That increases total costs

even if transportation costs are not increased.

However, as pointed out in recent articles by Chopra and

Sodhi (2014) and Simchi-Levi et al. (2014) it is almost im-

possible to determine probability of factory fires, natural dis-

asters, or piracy in a certain region. That is why one has to

concentrate mostly on mitigation strategies and identification

of the impact of disruption on financial and operational per-

formance regardless of what caused the disruption.

In addition, a general shortcoming of the existing studies, as

pointed out by Cui et al. (2010) and Li et al. (2013) is that the

dynamics of the SC execution is not considered. The disrup-

tions are mostly considered as static events, without taking

into account their duration, stabilization/ recovery policies.

Similar to MIP, the assumptions on known reliability of the

suppliers and parametric probabilities make the stochastic

programming models generally difficult to handle and im-

plement. In addition, scenario-based approach exponentially

increases the number of variables and constraints in stochas-

tic formulations. For some practical challenges and solutions

in this direction, we refer to van Delft and Vial (2004).

Generally the application of fuzzy and robust optimization is

related mostly to operational risks (e.g., demand fluctuations)

and tactical planning level with some episodical interfaces to

SCD. The same can be stated for MPC models. In addition,

as general shortcoming of robust optimization, the tendency

to quite pessimistic solution has to be named. In practice, it is

hardly to assume that managers will accept SCDs with low

efficiency and high fixed-costs just in the anticipation of the

worst-case.

Summarizing, management science and operations research

along with system dynamics and control theory contain a

number of useful methods that can be used for analysis and

for mitigating the ripple effect. Different methods are suited

to different problems. No single technique is likely to prove a

panacea in this field. While mathematical and stochastic op-

timization have their place at the strategic and tactical level at

SC design and planning stages, they fail to throw much light

on the dynamic behaviour of the SC as a whole. The implica-

tions of strategic SC design and tactical plans on SC perfor-

mance at the execution and recovery stage can be enhanced

by using models based on the dynamics of the execution

processes.

The research described in this paper is partially supported by

the Russian Foundation for Basic Research (grants 13-07-

00279, 13-08-00702, 13-08-01250, 13-06-00877, 13-07-

12120-офи-м, 15-29-01294-ofi-m, 15-07-08391, 15-08-

08459), grant 074-U01 supported by Government of Russian

Federation), Program “5-100-2020” supported by Govern-

ment of Russian Federation, Department of nanotechnologies

and information technologies of the RAS (project 2.11), by

ESTLATRUS projects 1.2./ELRI-121/2011/13 «Baltic ICT

Platform» and 2.1/ELRI-184/2011/14 «Integrated Intelligent

Platform for Monitoring the Cross-Border Natural-

Technological Systems»

REFERENCES

Acar, Y., Kadipasaoglu, S., & Schipperijn, P. (2010). A decision

support framework for global supply chain modelling: An as-

sessment of the impact of demand, supply and lead-time uncer-

tainties on performance. International Journal of Production

Research, 48(11), 3245–3268.

Aliev, R. A., Fazlollahi, B., Guirimov, B. G., & Aliev, R. R. (2007).

Fuzzy-genetic approach to aggregate production-distribution

planning in supply chain management. Information Sciences,

177(20), 4241–4255.

Amiri, A. (2006). Designing a distribution network in a supply chain

system: Formulation and efficient solution procedure. European

Journal of Operational Research 171(2): 567–76.

Askin, R.G., Ilaria Baffo & Mingjun Xia (2014). Multi-commodity

warehouse location and distribution planning with inventory

consideration. International Journal of Production Research,

52(7), 1897-1910

Azaron, A., Brown, K.N., Tarim, S.A. & Modarres, M., 2008. A

multi-objective stochastic programming approach for supply

chain design considering risk. International Journal of Produc-

tion Economics, 116 (1), 129-138.

Baghalian, A., Rezapour, S., & Farahani, R.Z. (2013). Robust sup-

ply chain network design with service level against disruptions

and demand uncertainties: A real-life case. European Journal of

Operational Research, 227 (1), 199-215.

Barlas Y. 2000. “Formal aspects of model validity and validation in

system dynamics.” System Dynamics Review, 12 (3): 183–210

Baron, O., Milner, J., Naseraldin, H., 2011. Facility location: a ro-

bust optimization approach. Production and Operations Man-

agement 20 (5), 772–785.

Bensoussan, A., Çakanyildirim, M., & Sethi, S. (2007). Optimal

ordering policies for inventory problems with dynamic informa-

tion delays. Production & Operations Management, 16(2), 241–

256.

Benyoucef, L., Xie, X. & Tanonkou, G.A., 2013. Supply chain net-

work design with unreliable suppliers: A lagrangian relaxation-

based approach. International Journal of Production Research,

51 (21), 6435-6454.

Bode, C., Wagner, S.M., Petersen, K.J., & Ellram, L.M. (2011).

Understanding responses to supply chain disruptions: Insights

from information processing and resource dependence perspec-

tives. Academy of Management Journal, 54(4), 833–856.

Braun, M. W., Rivera, D. E., Flores, M. E., Carlyle, W. M., &

Kempf, K. G. (2003). A model predictive control framework for

robust management of multi-product, multi-echelon demand

networks. Annual Reviews in Control, 27, 229–245.

Carvalho H, Barroso AP, Machado VH, Azevedo S, Cruz-Machado

V. (2012). Supply chain redesign for resilience using simulation.

Computers & Industrial Engineering, 62(1), 329–341

Chen Q., Li, X., Ouyang Y. (2011). Joint inventory-location prob-

lem under the risk of probabilistic facility disruptions. Transpor-

tation Research Part B: Methodological, 45(7), 991–1003


1774


Chen, J., A. S. Sohal, and D. I. Prajogo. 2013. “Supply chain opera-

tional risk mitigation: a collaborative approach.” International

Journal of Production Research 57 (1): 2186–2199

Chopra, S., Reinhardt, G., & Mohan, U. (2007). The importance of

decoupling recurrent and disruption risks in a supply chain. Na-

val Research Logistics, 54(5), 544–555

Chopra, S., Sodhi, M.S. (2014) Reducing the risk of supply chain

disruptions. MIT Sloan Management Review, 55(3), 73-80.

Costantino, A., Dotoli, M., Falagario, M., Fanti, M.P., Mangini,

A.M. (2012). A model for supply management of agile manu-

facturing supply chains. Int. J. Production Economics 135, 451–

457.

Costantino, A., Dotoli, M., Falagario, M., Fanti, M.P., Mangini,

A.M., Sciancalepore, F., Ukovich, W. (2011). A Fuzzy Pro-

gramming Approach for the Strategic Design of Distribution

Networks. Prodeedings of the IEEE International Conference on

Automation Science and Engineering, pp. 66-71.

Cui, T., Ouyang, Y. & Shen, Z.-J.M., 2010. Reliable facility loca-

tion design under the risk of disruptions. Operations Research,

58 (4-part-1), 998-1011.

Daganzo, C. F. (2004). On the stability of supply chains. Operations

Research, 52(6), 909–921.

Daskin, M. S. 1995. Network and Discrete Location: Models, Algo-

rithms,and Applications. John Wiley, New York.

Deleris, L.A., &Erhun, F. (2011). Quantitative risk assessment in

supply chains: A case study based on engineering risk analysis

concepts. In: Kempf, K.G., Keskinocak, P., &Uzsoy, R. (eds)

Planning Production and Inventories in the Extended Enter-

prise, International Series in Operations Research & Manage-

ment Science 152. New York: Springer, pp. 105–131.

Dolgui, A. and Proth, J.-M. (2008) ‘RFID Technology in Supply

Chain Management: State of the Art and Perspectives’, Proceed-

ings of the 17th IFAC World Congress, Seoul, Korea

Drezner, Z., ed. 1995. Facility Location: A Survey of Applications

and Methods. Springer, New York.

Georgiadis, M.C., Tsiakis, P., Longinidis, P., & Sofioglou, M.K.

(2011). Optimal design of supply chain networks under uncer-

tain transient demand variations. Omega, 39 (3), 254-272.

Goh, M., Lim, J.Y.S., Meng, F., 2007. A stochastic model for risk

management in global chain networks. European Journal of Op-

erational Research 182 (1), 164–173.

Graves, S.C., Tomlin, B., 2003. Process flexibility in supply chains.

Management Science 49, 907–919.

Gulpınar, N., Pachamanova, D., Canakoglu, E., 2012. Robust strate-

gies for facility location under uncertainty. European Journal of

Operational Research 225 (1), 21–35

Hammami, R. & Frein, Y., 2013. An optimisation model for the

design of global multi-echelon supply chains under lead time

constraints. International Journal of Production Research, 51

(9), 2760-2775

Harjunkoski, I., Maravelias C.T., Bongers P., Castro P.M., Engell,

S., Grossmann, I.E., Hooker J., Méndez, C., Sand G., Wassick J.

(2014). Scope for industrial applications of production schedul-

ing models and solution methods. Computers and Chemical En-

gineering, 62, 161-193.

Hsu, C.I., & Li, H.C. (2011). Reliability evaluation and adjustment

of supply chain network design with demand fluctuations. Inter-

national Journal of Production Economics, 132(1), 131-145.

Hu, X, Gurnani, H, & Wang L. (2013). Managing risk of supply

disruptions: Incentives for capacity restoration. Production and

Operations Management, 22(1),137–150.

Hwarng, H. B., & Xie, N. (2008). Understanding supply chain dy-

namics: A chaos perspective. European Journal of Operational

Research, 184(3), 1163–1178.

Iakovou, E., Vlachos, D. and Xanthopoulos, A. (2010). A stochastic

inventory management model for a dual sourcing supply chain

with disruptions, International Journal of Systems Science,

41(3): 315-324.

Ivanov D, Sokolov B, Kaeschel J (2010) A multi-structural frame-

work for adaptive supply chain planning and operations with

structure dynamics considerations. European Journal of Opera-

tional Research 200: 409-420

Ivanov D., Sokolov B. (2012a) Structure dynamics control approach

to supply chain planning and adaptation, International Journal of

Production Research, 50(21), 6133-6149.

Ivanov D., Sokolov B. (2013) Control and system-theoretic identifi-

cation of the supply chain dynamics domain for planning, analy-

sis, and adaptation of performance under uncertainty, European

Journal of Operational Research, 224(2); 313-323.

Ivanov D., Sokolov B., Pavlov A. (2014) Optimal distribution

(re)planning in a centralized multi-stage network under condi-

tions of ripple effect and structure dynamics, European Journal

of Operational Research, 237(2), 758-770.

Ivanov D., Sokolov B., Pavlov, A. (2013) Dual problem formulation

and its application to optimal re-design of an integrated produc-

tion-distribution network with structure dynamics and ripple ef-

fect considerations: International Journal of Production Re-

search, 51(18), 5386-5403.

Ivanov, D., Sokolov, B. (2012b). Dynamic supply chain scheduling,

Journal of Scheduling 15(2); 201-216.

Ivanov, D., Sokolov, B. 2010. Adaptive Supply Chain Management.

London: Springer

Ivanov, D., Sokolov, B., Dolgui, A. (2014) The Ripple effect in

supply chains: trade-off ‘efficiency-flexibility-resilience’ in dis-

ruption management, International Journal of Production Re-

search, 52:7, 2154-2172.

Kamath, N. B., & Roy, R. (2007). Capacity augmentation of a sup-

ply chain for a short lifecycle product: A system dynamics

framework. European Journal of Operational Research, 179(2),

334–351.

Kim, S.H., & Tomlin, B. (2013). Guilt by Association: Strategic

Failure Prevention and Recovery Capacity Investments. Man-

agement Science, 59(7), 1631-1649

Klibi, W., Martel, A., & Guitouni, A. (2010). The design of robust

value-creating supply chain networks: A critical review. Euro-

pean Journal of Operational Research, 203(2), 283–293.

Knemeyer, A.M., Zinn, W., & Eroglu, C. (2009). Proactive planning

for catastrophic events in supply chains. Journal of Operations

Management. 27 (2), 141-153.

Kumar S.K., Tiwari M.K. (2013): Supply chain system design inte-

grated with risk pooling, Computers & Industrial Engineering,

64, pp. 580-588.

Lewis, B.M, Erera, A.L., Nowak, M.A., & White, III C.C. (2013).

Managing Inventory in Global Supply Chains Facing Port-of-

Entry Disruption Risks. Transportation Science, 47(2), 162-180.

Li, Q., Zeng, B. & Savachkin, A., 2013. Reliable facility location

design under disruptions. Computers & Operations Research,

40(4), 901-909.

Lim, M., M. S. Daskin, A. Bassamboo, and S. Chopra, 2010, “A

Facility Reliability Problem: Formulation, Properties and Algo-

rithm,” Naval Research Logistics, 57:1, 58-70

Lim, M.K., Bassamboo, A., Chopra, S., & Daskin, M.S. (2013).

Facility location decisions with random disruptions and imper-


1775


fect estimation. Manufacturing and Service Operations Man-

agement, 15(2), 239-249.

Lin, YK., Huang CF & Yeh, CT (2014). Network reliability with

deteriorating product and production capacity through a multi-

state delivery network. International Journal of Production Re-

search 52 (22): 6681-6694-

Lu, M., Huang, S., & Shen, Z.M. (2011). Product substitution and

dual sourcing under random supply failures. Transportation Re-

search Part B, 45, 1251–1265.

Mastragostino R, Patel S, and Swartz C. (2014). Robust decision

making for hybrid process supply chain systems via model pre-

dictive control. Computers and Chemical Engineering, 62, 37-

55.

Max Shen, Z.-J., & Qi, L. (2007). Incorporating inventory and

routing costs in strategic location models. European Journal of

Operational Research, 179, 372-389.

Meepetchdee, Y., & Shah, N. (2007). Logistical network design

with robustness and complexity considerations. International

Journal of Operations and Production Management, 37(3),

201–222.

Meisel F., Bierwirth C. (2014) The design of Make-to-Order supply

networks under uncertainties using simulation and optimization.

International Journal of Production Research, 52(22), 6590-

6607.

Melo, M.T., Nickel, S., & Saldanha-da-Gama, F. (2009). Facility

location and supply chain management - A review. European

Journal of Operational Research, 196 (2), 401-412.

Mohammadi B.H., Yusuff, R.M., Megat Ahmad, M.M.H., &Abu

Bakar, M. R. (2009). Development of a new approach for de-

terministic supply chain network design. European Journal of

Operational Research, 198(1), 121–128.

Mohebbi, E. (2003). Supply interruptions in a lost-sales inventory

system with random lead time: Computers & Operations Re-

search, 30, 411–426.

Nair, A. & Vidal, J.M., 2010. Supply network topology and robust-

ness against disruptions – an investigation using multi-agent

model. International Journal of Production Research, 49 (5),

1391-1404

Paul, S.K., Sarker, R., & Essam, D. (2014). Real time disruption

management for a two-stage batch production–inventory system

with reliability considerations. European Journal of Operational

Research 237 (2014) 113–128

Peng, P., Snyder, L. V., Lim, A., & Liu, Z. (2011). Reliable logistics

networks design with facility disruptions. Transportation Re-

search Part B: Methodological, 45(8), 1190–1211.

Perea, E., Grossmann, I., Ydstie, E., & Tahmassebi, T. (2000). Dy-

namic modeling and classical control theory for supply chain

management. Computers and Chemical Engineering, 24, 1143–

1149

Petrovic, D., Roy, R., & Petrovic, R. (1998). Modelling and simula-

tion of a supply chain in an uncertain environment. European

Journal of Operational Research, 109(2), 299–309

Pishvaee, M.S., Razmi, J., Torabi, S.A. (2012). Robust possibilistic

programming for socially responsible supply chain network de-

sign; a new approach. Fuzzy sets and systems, 206, 1-20.

Puigjaner, L., & Lainez, J. M. (2008). Capturing dynamics in inte-

grated supply chain management. Computers and Chemical En-

gineering, 32,:2582–2605.

Qi, L., Shen, Z.-J.M. & Snyder, L.V., 2010. The effect of supply

disruptions on supply chain design decisions. Transportation

Science, 44 (2), 274-289.

Qiang, Q., Nagurney, A., & Dong, J. (2009). Modelling of supply

chain risk under disruptions with performance measurement and

robustness analysis. In: Wu, T., & Blackhurst, J.V. (eds) Man-

aging supply chain risk and vulnerability: Tools and methods

for supply chain decision makers (1st ed.). New York: Springer,

91–111.

Rafiei, M., Mohammadi, M. & Torabi, S., 2013. Reliable multi pe-

riod multi product supply chain design with facility disruption.

Decision Science Letters, 2 (2), 81-94

Riddalls, C.E., Bennett, S., & Tipi, N.S. (2000). Modelling the dy-

namics of supply chains. International Journal of Systems

Science, 31(8), 969-976.

Rosenhead, J., Elton, M. & Gupta, S.K., 1972. Robustness and op-

timality as criteria for strategic decisions. Operational Research

Quarterly (1970-1977), 23 (4), 413-431

Ross, A.M., Rong, Y., Snyder, L.V., 2008. Supply disruptions with

time-dependent parameters. Computers and Operations Re-

search 35 (11), 3504–3529.

Sadjady H., Davoudpour H. (2012): Two-echelon, multi-commodity

supply chain network design with mode selection, lead-times

and inventory costs, Computers & Operations Research, 39, pp.

1345-1354.

Santoso, T., Ahmed, S., Goetschalckx, G. & Shapiro, A. (2005). A

stochastic programming approach for supply chain network de-

sign under uncertainty. European Journal of Operational Re-

search, 167, 96–115.

Sawik, T. (2013) “Integrated selection of suppliers and scheduling

of customer orders in the presence of supply chain disruption

risks.” International Journal of Production Research 51(23-24):

7006-7022.

Schmitt, A.J., & Singh, M. (2012). A quantitative analysis of disrup-

tion risk in a multi-echelon supply chain. International Journal

of Production Economics, 139 (1), 23-32.

Schoenlein, M., Makuschewitz, T., Wirth, F., Scholz-Reiter, B

(2013). Measurement and optimization of robust stability of

multiclass queuing networks: Applications in dynamic supply

chains. European Journal of Operational Research, 229:179-189.

Schütz, P., Tomasgard, A., & Ahmed, S. (2009). Supply chain de-

sign under uncertainty using sample average approximation and

dual decomposition. European Journal of Operational Re-

search, 199 (2), 409-419.

Serel, D.A., 2008. Inventory and pricing decisions in a single-period

problem involving risky supply. International Journal of Produc-

tion Economics 116 (1), 115-128.

Shao X.F., Dong M. (2012). Supply disruption and reactive strate-

gies in an assemble-to-order supply chain with time-sensitive

demand. IEEE Transactions on engineering management, 59(2),

201-212.

Sheffi, Y., & Rice, J.B. (2005). A supply chain view of the resilient

enterprise. MIT Sloan Management Review, 47 (1), 41-48.

Shen, Z.M. (2007). Integrated supply chain design models: a survey

and feature research directions. Journal of Industrial and Man-

agement Optimization, 3(1), 1-27.

Simchi-Levi, D., Schmidt, W., and Wei, Y. (2014). From supers-

torms to factory fires: Managing unpredictable supply chain dis-

ruptions. Havard Business Review, February 2014.

Singh, A.R., Mishra, P.K., Jain, R., & Khurana, M.K. (2012). De-

sign of global supply chain network with operational risks. In-

ternational Journal of Advanced Manufacturing Technology, 60

(1-4), 273-290.

Snyder, L.V. and Daskin M. S. (2005). Reliability Models for Facili-

ty Location: The Expected Failure Cost Case. Transportation

Science, 39, 400-416.


1776


Sodhi, M.S. and Lee, S. (2007). An analysis of sources of risk in the

consumer electronics industry, Journal of the Operational Re-

search Society 58(11): 1430-1439

Sodhi, M.S., Son, B.-G., Tang, C. 2012. Researchers’ perspectives

on supply chain risk management. Production and Operations

Management 21 (1), 1-13.

Soni U., Jain V., Kumar S. (2014). Measuring supply chain resi-

lience using a deterministic modeling approach. Computers &

Industrial Engineering 74 (2014) 11–25.

Soroor, J., Tarokh, M.J., Keshtgary, M. (2009). Preventing failure in

IT-enabled systems for supply chain management. International

Journal of Production Research 47 (23), 6543-6557.

Sourirajan, K., L. Ozsen, and R. Uzsoy. 2009. “A Genetic Algo-

rithm for a Single Product Network Design Model with Lead

Time and Safety Stock Considerations.” European Journal of

Operational Research 197: 599–608.

Surana A., Kumara S., Greaves M. & Usha (2005). Supply-chain

networks: a complex adaptive systems perspective. International

Journal of Production Research 43(20), 4235-4265.

Swaminathan, J.M, Smith, S.F, and Sadeh, N.M. Modeling Supply

Chain Dynamics: A Multiagent Approach, in Decision Science,

1998, 29(3), 607–632.

Teimoury, E., Fathi, M. (2013). An integrated operations-marketing

perspective for making decisions about order penetration point

in multi-product supply chain: A queuing approach. Internation-

al Journal of Production Research, 51(18), 5776-5796.

Tomlin, B. (2006). On the Value of Mitigation and Contingency

Strategies for Managing Supply Chain Disruption Risks, Man-

agement Science, 52, 639-657.

Tsiakis, P., Shah, N. & Pantelides, C.C. (2001). Design of multi-

echelon supply chain networks under demand uncertainty. In-

dustrial & Engineering Chemistry Research, 40, 3585–3604.

Tuncel, G. and Alpan, G. (2010). Risk assessment and management

for supply chain networks- A case study, Computers in Industry

61(3): 250-259.

Unnikrishnan, A., & Figliozzi, M. (2011). Online Freight Network

Assignment Model with Transportation Disruptions and Re-

course. Transportation Research Record: Journal of the Trans-

portation Research Board, 2224(1), 17–25.

Vahdani, B., Zandieh, M., & Roshanaei, V. (2011). A hybrid multi-

stage predictive model for supply chain network collapse recov-

ery analysis: A practical framework for effective supply chain

network continuity management. International Journal of Pro-

duction Research, 49(7), 2035–2060.

van Delft, Ch., Vial, J.-Ph., 2004. A practical implementation of

stochastic programming: An application to the evaluation of op-

tion contracts in supply chains. Automatica 40, 743–756.

Wang, D., & Ip, W.H. (2009). Evaluation and analysis of logistic

network resilience with application to aircraft servicing. IEEE

Systems Journal, 3, 166–173.

Wilson, M.C. (2007). The impact of transportation disruptions on

supply chain performance. Transportation Research Part E: Lo-

gistics and Transportation Review, 43, 295-320.

Wu, T., Blackhurst, J., and O’grady, P. (2007). Methodology for

supply chain disruption analysis. International Journal of Pro-

duction Research, 45(7), 1665- 1682.

Xia, Y., Yang, M.-H., Golany, B., Gilbert, S.M. & Yu, G., 2004.

Real-time disruption management in a two-stage production and

inventory system. IIE Transactions, 36 (2), 111-125.

Xiao T., and Yu G. (2006). Supply Chain Disruption Management

and Evolutionarily Stable Strategies of Retailers in the Quantity-

setting Duopoly Situation with Homogeneous Goods, European

Journal of Operational Research 173(2) 648-668.

Xu, M., Wang X. & Zhao L. (2014). Predicted supply chain resi-

lience based on structural evolution against random supply dis-

ruptions. International Journal of Systems Science: Operations

& Logistics, 1(2), 105-117

Yu, H., Zeng, A.Z. & Zhao, L., 2009. Single or dual sourcing: Deci-

sion-making in the presence of supply chain disruption risks.

Omega, 37 (4), 788-800

Zegordi, S.H., & Davarzani, H. (2012). Developing a supply chain

disruption analysis model: Application of coloured petri-nets.

Expert Systems with Applications, 39 (2), 2102-2111.

Zhou, W., Zhang, R., Zhou, Y. (2013) A queuing model on supply

chain with the form postponement strategy. Computers and In-

dustrial Engineering, 66(4), 643-652.

Zobel, C.W., & Khansa, L. (2014). Characterizing multi-event dis-

aster resilience. Computers & Operations Research, 42, 83–94.


1777

Ivanov 2015

Documents

Transcript of Ivanov 2015