The optimization of facility location decisions is critical for the success of a supply chain in a market since it can contribute to long-term performance of the supply chain. In the last two decades, the number of research in this field has been growing to address more realistic problems such as incorporating uncertainties in repair time and demand. In this paper, a particle swarm optimization algorithm (PSO) is employed to locate repair shops in a stochastic environment. The problem aim is to decide about the location and the capacity of local repair shops as well as identifying the capacity of central repair shop to minimize total expected cost. It is assumed that customers select the closest local repair shop. In the local repair shops, services are available to repair customer’s broken items and a number of spare parts are stored to supply customers’ needs. Additionally, each repair shop is allowed to open some servers, depending on the number of customers, to serve its customers. If a stock-out happens, a customer should wait until the part is repaired in that shop. When a local repair shop is unable to repair a part, the part is sent to the central repair shop to be repaired. The central repair shop follows similar strategy for spare part inventory. The contribution of this paper is to employ a meta-heuristic solution approach based on particle swarm optimization for locating repair shops problem. In order to evaluate the performance of the employed solutions approach, its result is compared to other methods and differences are highlighted.
A discrete particle swarm optimization algorithm with local search for a production-based two-echelon single-vendor multiple-buyer supply chain
