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Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/6407

Title: Supply chain network design under uncertainty and risk
Authors: Hollmann, Dominik
Advisors: Mitra, G
Lucas, CA
Keywords: Stochatic integra programming
Conditional value-at-risk
Scenario generation
Solution heuristic
Ex-ante decision making
Publication Date: 2011
Publisher: Brunel University, School of Information Systems, Computing and Mathematics
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.
Description: This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.
URI: http://bura.brunel.ac.uk/handle/2438/6407
Appears in Collections:Mathematics
School of Information Systems, Computing and Mathematics Theses

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