Two calibrated metaheuristic algorithms for solving a multi-plant capacitated lot-sizing problem in an integrated production-distribution network

In this paper a model is developed to investigate the value of coordination of production and distribution planning in a three-layer supply chain consisting of multiple suppliers, manufacturers, and distribution centers. The combinations of several factors such as purchasing, production, storage, backordering, and transportation are considered. The aim of the model is to find the optimal order, production, and shipment quantities, so that the cost of the whole supply chain is minimized and demand is satisfied over a given planning horizon without violating the capacity restrictions of the plants and suppliers. Transfer decisions between plants are made when production at a plant cannot meet demand due to lack of adequate resources and it can be satisfied by other production plants. Since the proposed model is NP-hard, two metaheuristic algorithms namely simulated annealing and genetic algorithm are used to find the optimal or near-optimal solution within a moderate computation times. The results show that presented algorithms are computationally effective and beneficial for obtaining the optimal solution for the proposed lot-sizing problem.