A stochastic programming approach for supply chain network design under uncertainty

Tjendera Santoso (tjenderaisye.gatech.edu)
Shabbir Ahmed (sahmedisye.gatech.edu)
Marc Goetschalckx (marc.goetschalckxisye.gatech.edu)
Alexander Shapiro (ashapiroisye.gatech.edu)

Abstract: This paper proposes a stochastic programming model and solution algorithm for solving supply chain network design problems of a realistic scale. Existing approaches for these problems are either restricted to deterministic environments or can only address a modest number of scenarios for the uncertain problem parameters. Our solution methodology integrates a recently proposed sampling strategy, the Sample Average Approximation scheme, with an accelerated Benders decomposition algorithm to quickly compute high quality solutions to large-scale stochastic supply chain design problems with a huge (potentially infinite) number of scenarios. A computational study involving two real supply chain networks are presented to highlight the significance of the stochastic model as well as the efficiency of the proposed solution strategy.

Keywords: Facilities planning and design; Supply chain network design; Stochastic programming; Decomposition methods; Sampling.

Category 1: Applications -- OR and Management Sciences

Category 2: Applications -- OR and Management Sciences (Supply Chain Management )

Category 3: Stochastic Programming

Citation: Technical Report, School of Industrial & Systems Engineering, Georgia Institute of Technology, 2003.

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Entry Submitted: 06/16/2003
Entry Accepted: 06/16/2003
Entry Last Modified: 06/16/2003

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