Optimizing supply chain network design with location-inventory decisions for perishable items: A Pareto-based MOEA approach

In this paper, a bi-objective mathematical model is presented to optimize supply chain network with location-inventory decisions for perishable items. The goal is to minimize total cost of the system, including transportation cost of perishable items from hub center into DCs and from DCs to ultimate centers, transportation cost of unusual orders, and xed cost of centers as DCs, as well as demand unresponsiveness. Considering special conditions for holding items and regional DCs, and determining average lifetime for the items assigned to centers are other features of the proposed model. With regard to complexity of the proposed model, a Pareto-based meta-heuristic approach, called Multi-Objective Imperialist Competitive Algorithm (MOICA), is presented to solve it. To demonstrate performance of the proposed algorithms, two well-developed multiobjective algorithms based on genetic algorithm, including Non-dominated Ranked Genetic Algorithm (NRGA) and Non-dominated Sorting Genetic Algorithm (NSGA-II), are applied. In order to analyze the results, several numerical illustrations are generated; then, the algorithms are compared both statistically and graphically. Analysis of the results shows the robustness of MOICA to nd and manage Pareto solutions.