A multi-objective stochastic programming approach for supply chain design considering risk

By Amir Azaron, Kenneth N. Brown, Ş. Tarim, Mohammad Modarres. 2008

347 citations recorded by the source corpus.

In this paper, we develop a multi-objective stochastic programming approach for supply chain design under uncertainty. Demands, supplies, processing, transportation, shortage and capacity expansion costs are all considered as the uncertain parameters. To develop a robust model, two additional objective functions are added into the traditional comprehensive supply chain design problem. So, our multi-objective model includes (i) the minimization of the sum of current investment costs and the expected future processing, transportation, shortage and capacity expansion costs, (ii) the minimization of the variance of the total cost and (iii) the minimization of the financial risk or the probability of not meeting a certain budget. The ideas of unreliable suppliers and capacity expansion, after the realization of uncertain parameters, are also incorporated into the model. Finally, we use the goal attainment technique to obtain the Pareto-optimal solutions that can be used for decision-making.

Subfields: Control and Systems Engineering, Management Information Systems, Strategy and Management

View Paper: https://www.semanticscholar.org/paper/198d62a32882e7893891bf75b2482a5203a50732

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Citations in the corpus

Robust supply chain network design with service level against disruptions and demand uncertainties: A real-life case (2013, neutral): “Azaron et al. (2008) developed a multi-objective stochastic programming approach for SC design under uncertainty.”
A Review on Quantitative Models for Sustainable Food Logistics Management (2012, methodology): “Distinct from other studies, Azaron et al. (2008) also adopt the minimization of the variance of the total cost into a multi-objective model to increase the robustness of the model.”
A critical review on supply chain risk – Definition, measure and modeling ☆ (2015, methodology): “Very rarely nonlinear approaches are chosen [4,7] Models exclusively considering multi-objective functions are of minor interest [4,62,159], although the need to balance different types of supply chain objectives would motivate multi-objective approaches.”
Literature review on disruption recovery in the supply chain* (2017, background): “In robust stochastic programming models (Azaron et al. 2008), facility disruptions and capacity expansion costs are also considered to be uncertain. Sawik (2013) developed a stochastic programming model for integrated supplier selection, order quantity allocation and customer order scheduling in the”
A multi-objective robust stochastic programming model for disaster relief logistics under uncertainty (2011, background): “Some research work on victim evacuation can be found in Bakuli and Smith (1996), Barbarosoglu et al. (2002), Han et al. (2006), Yi and Ozdamar (2007), Yi and Kumar (2007), Regnier (2008), Stepanov and Smith (2009) and Saadatseresht et al. (2009).”