International Journal of Industrial Engineering & Production Research

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In this paper, we propose an extended relocation model for warehouses configuration in a supply chain network, in which uncertainty is associated to operational costs, production capacity and demands whereas, existing researches in this area are often restricted to deterministic environments. In real cases, we usually deal with stochastic parameters and this point justifies why the relocation model under uncertainty should be evaluated. Albeit the random parameters can be replaced by their expectations for solving the problem, but sometimes, some methodologies such as two-stage stochastic programming works more capable. Thus, in this paper, for implementation of two stage stochastic approach, the sample average approximation (SAA) technique is integrated with the Bender's decomposition approach to improve the proposed model results. Moreover, this approach leads to approximate the fitted objective function of the problem comparison with the real stochastic problem especially for numerous scenarios. The proposed approach has been evaluated by two hypothetical numerical examples and the results show that the proposed approach can find better strategic solution in an uncertain environment comparing to the mean-value procedure (MVP) during the time horizon.

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In this study, an optimization model is proposed to design a Global Supply Chain (GSC) for a medical device manufacturer under disruption in the presence of pre-existing competitors and price inelasticity of demand. Therefore, static competition between the distributors’ facilities to more efficiently gain a further share in market of Economic Cooperation Organization trade agreement (ECOTA) is considered. This competition condition is affected by disruption occurrence. The aim of the proposed model is to maximize the expected net after-tax profit of GSC under disruption and normal situation at the same time. To effectively deal with disruption, some practical strategies are adopted in the design of GSC network. The uncertainty of the business environment is modeled using the robust optimization technique based on the concept of uncertainty budget. To tackle the proposed Mixed-Integer Nonlinear Programming (MINLP) model, a hybrid Taguchi-based Memetic Algorithm (MA) with an adaptive population size is developed that incorporates a customized Adaptive Large Neighborhood Search (ALNS) as its local search heuristic. A fitness landscape analysis is used to improve the systematic procedure of neighborhood selection in the proposed ALNS. A numerical example and computational results illustrate the efficiency of the proposed model and algorithm in dealing with global disruptions under uncertainty and competition pressure.

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Flexible and dynamic supply chain network design problem has been studied by many researchers during past years. Since integration of short-term and long-term decisions in strategic planning leads to more reliable plans, in this paper a multi-objective model for a sustainable closed-loop supply chain network design problem is proposed. The planning horizon of this model contains multiple strategic periods so that the structure of supply chain can be changed dynamically during the planning horizon. Furthermore, in order to have an integrated design, several short-term decisions are considered besides strategic network design decision. One of these short-term decisions is determining selling price and buying price in the forward and reverse logistics of supply chain, respectively. Finally, an augmented e-constraint method is used to transform the problem to a single-objective model and an imperialist competitive algorithm is presented to solve large scale problems. The results’ analysis indicates the efficiency of the proposed model for the integrated and dynamic supply chain network design problem. 

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A teaching-learning-based optimization (TLBO) algorithm is a new population-based algorithm applied in some applications in the literature successfully. Moreover, a genetic algorithm (GA) is a popular tool employed widely in many disciplines of engineering. In this paper, a hybrid GA-TLBO algorithm is proposed for the capacitated three-stage supply chain network design (SCND) problem. The SCND problem as a strategic level decision-making problem in supply chain management is an NP-hard class of computational complexity. To escape infeasible solutions emerged in the problem of interest due to realistic constraints, combination of a random key and priority-base encoding scheme is also used. To assess the quality of the proposed hybrid GA-TLBO algorithm, some numerical examples are conducted. Then, the results are compared with the GA, TLBO, differential evolution (DE) and branch-and -bound algorithms. Finally, the conclusion is provided.

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Managing income is a considerable dimension in supply chain management in current economic atmosphere. Real world situation makes it inevitable not to design or redesign supply chain. Redesign will take place as costs increase or new services for customers’ new demands should be provided. Pricing is an important fragment of Supply chain due to two reasons: first, represents revenue based each product and second, based on supply-demand relations enables Supply chain to provide demands by making suitable changes in facilities and their capacities. In this study, Benders decomposition approach used to solve multi-product, multi-echelon and multi-period supply chain network redesign including price-sensitive customers.