Supply Chain Network Design under Uncertainty and Risk

Thesis

Supply Chain Network Design under Uncertainty and Risk

Submitted for the Degree of Doctor of Philosophy

by Dominik Hollmann

Department of Mathematics, Brunel University September 2011

Abstract

We consider the research problem of quantitative support for decision making in supply chain network design (SCND). We first identify the requirements for a comprehensive SCND as (i) a methodology to select uncertainties, (ii) a stochastic optimisation model, and (iii) an appropriate solution algorithm. We propose a process to select a manageable number of uncertainties to be included in a stochastic program for SCND. We develop a comprehensive two-stage stochastic program for SCND that includes uncertainty in demand, currency exchange rates, labour costs, productivity, supplier costs, and transport costs. Also, we consider conditional value at risk (CV@R) to explore the trade-off between risk and return. We use a scenario generator based on moment matching to represent the multivariate uncertainty. The resulting stochastic integer program is computationally challenging and we propose a novel iterative solution algorithm called adaptive scenario refinement (ASR) to process the problem. We describe the rationale underlying ASR, validate it for a set of benchmark problems, and discuss the benefits of the algorithm applied to our SCND problem. Finally, we demonstrate the benefits of the proposed model in a case study and show that multiple sources of uncertainty and risk are important to consider in the SCND. Whereas in the literature most research is on demand uncertainty, our study suggests that exchange rate uncertainty is more important for the choice of optimal supply chain strategies in international production networks. The SCND model and the use of the coherent downside risk measure in the stochastic program are innovative and novel; these and the ASR solution algorithm taken together make contributions to knowledge.

iii

Contents

Abstract

iii

Acknowledgements

vii

List of Tables

ix

List of Figures

xi

List of Algorithms

xiii

List of Abbreviations

xv

1. Introduction

1

1.1. Problem Context and Motivation . . . . . . . . . . . . . . . . . . . . . . 1

1.2. Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

1.3. Outline of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2

2. Methodological Requirements for Supply Chain Network Design (SCND)

5

2.1. SCND ? Scope and Definition . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2. Decision Making under Uncertainty . . . . . . . . . . . . . . . . . . . . . 8

2.3. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17

2.4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

3. Uncertainty and Risk Identification for SCND

21

3.1. Risk Management . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2. Literature Review: Analysis of Uncertainties Impacting SCND . . . . . . 22

3.3. An Uncertainty Selection Process for SCND . . . . . . . . . . . . . . . . 26

3.4. Uncertainty Identification for the Case Study . . . . . . . . . . . . . . . 29

3.5. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

4. A Stochastic Optimisation Model for SCND under Uncertainty and Risk

31

4.1. Literature Review: Quantitative Decision Support for SCND . . . . . . . 31

4.2. Model Description . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

4.3. Random Parameters: Scenario Generation . . . . . . . . . . . . . . . . . 61

4.4. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65

v

 ................
................

In order to avoid copyright disputes, this page is only a partial summary.

Google Online Preview   Download