A stochastic programming approach for supply chain network design under uncertainty

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.

Simulation-optimization model for production planning in the blood supply chain

“…This approach can be implemented in various ways, depending on the simulation paradigms chosen. Examples can be found in Nikolopoulou and Ierapetritou [38], Santoso et al [39] and Almeder et al [40] for agent-based simulation, Monte Carlo simulation and DES, respectively. In the blood supply chain, optimization models have been used less frequently than in industrial supply chains.…”

Hybrid simulation–optimization methods: A taxonomy and discussion

“…The method starts with a given sample size, which increases until required confidence and variance levels are achieved. An example is given in [47]. These SPDE approaches typically require some assumptions / simplifications to be made, when compared to S-O using discrete-event simulation.…”

Integrated Strategic Planning of Global Production Networks and Financial Hedging under Uncertain Demands and Exchange Rates

“…Alonso-Ayuso, Escudero, Garìn, Ortuño, and Pérez (2003) presented a two-stage stochastic program and a corresponding solution method for a similar supply chain design problem. Santoso, Ahmed, Goetschalckx, and Shapiro (2005) presented an accelerated Benders' Decomposition method for mixed-integer, two-stage stochastic programs for planning realistically scaled supply chain design networks. Utilizing a sampling strategy, they were able to handle a great number of scenarios.…”

“…3.1 Overview and notation The strategic single-period multi-commodity supply network design model of Santoso, Ahmed, Goetschalckx, and Shapiro (2005) serves as the basis for our integrated model. In this two-stage stochastic mixed-integer programming model, strategic investment decisions are represented by first-stage variables, and production and transportation quantities are represented by second-stage variables.…”

“…Two of the main sources of risk in global supply network design planning are uncertain exchange rates and demands. In this paper, we integrate an anticipation scheme for financial hedging and portfolio planning into a stochastic supply chain design model of the type described by Santoso, Ahmed, Goetschalckx, and Shapiro (2005) and Bihlmaier, Koberstein, and Obst (2009). We develop a multistage stochastic mixed-integer linear programming model, which includes discrete strategic decisions on facility location, product-plant assignment and capacity dimensions on the first time stage and operational production and logistics and financial decisions on subsequent time stages under uncertain exchange rates and demands.…”