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

A multi-objective model for locating distribution centers in a supply chain network considering risk and inventory decisions

“…Robust programming, introduced by Mulvey et al (1995), is an improved stochastic programming to deal with the preferred risk aversion of decision makers, which was not possible to use in routine stochastic programming (Bozorgi Amiri et al, 2012;Azaron et al, 2008) to locate distribution facilities in a three levels supply chain network under uncertainty. In this method, the variability term was supplemented to the main objective function by a related weighting parameter to show the tolerance of modelers' risk.…”

“…As the expected total cost, the total cost variance and the financial risk are in contrast with each other (Azaron et al, 2008), here a multi-objective mathematical model is proposed based on Epsilon Constraint Method and Lingo 9 software is implemented to create a set of Pareto optimal possible solution. Epsilon Constraint Method is one of the common approaches for dealing with multiobjective problems, which solves such problems by considering all objective functions as constraints and retaining only one of them in each phase as the main objective function (Ehrgott & Gandibleux, 2002).…”

“…In addition to conquer the disadvantages of traditional, stochastic supply chain design approaches namely minimizing (maximizing) cost (profit)as a single objective, minimizing the risk reflected by variance of total cost and not considering financial risk, etc. (Azaron et al 2008), robust stochastic programming approach is developed to design the uncertain supply chain using discrete scenariosbased approach, each with a certain probability of occurrence. The model aims to determine:  Number of DCs to be opened,  The location of these DCs,  The amount of inventory should be kept,  The items need to be delivered from each supplier to each DC and each DC to a customer.…”

“…The variance of the total cost should be considered in the model, because concentrating only on the expected total cost, may lead to sub-optimal solution if the total cost significantly alters because of randomness (Mulvey et al 1995). In practice, the interpretation of the total cost variance is hard, so, adding a new objective function capturing risk, is necessary (Azaron et al, 2008). The purpose of the objective function is to minimize the financial risk, which is defined as the probability of not meeting a certain cost level.…”

An Effective Heuristic Algorithm for Robust Supply Chain Network Design under Uncertainty

“…Their model determined the configuration of an SC which consisted of deciding which of the processing centres to build (major configuration decisions) and which processing and finishing machines to procure (minor configuration decisions). Azaron [11] developed a multi-objective stochastic programming approach for SC design under uncertainty. Demands of markets, supplies of suppliers, processing, transportation and capacity expansion costs were all considered as uncertain parameters.…”