Recently, metaheuristic algorithms (MHAs) have gained noteworthy attention for their abilities to solve difficult optimization problems in engineering, business, economics, finance, and other fields. This chapter introduces some applications of MHAs in supply chain management (SCM) problems. For example, consider a multi-product multi-constraint SCM problem in which demands for each product are not deterministic, the lead-time varies linearly with regard to the lot-size and partial backordering of shortages are assumed. Thus, since the main goal is to determine the re-order point, the order quantity and number of shipments under the total cost of the whole chain is minimized. In this chapter, the authors concentrate on MHAs such as harmony search (HS), particle swarm optimization (PSO), genetic algorithm (GA), firefly algorithm (FA), and simulated annealing (SA) for solving the following four supply chain models: single-vendor single-buyer (SBSV), multi-buyers single-vendor (MBSV), multi-buyers multi-vendors (MBMV) and multi-objective multi-buyers multi-vendors (MOMBMV). These models typically are in any supply chain. For illustrative purposes, a numerical example is solved in each model.
An EPQ model for three-layer supply chain with partial backordering and disruption: Triangular dense fuzzy lock set approach
