عنوان مقاله [English]
One of the most important strategic decisions that affects the success of an organization, is to locate the facility in an appropriate place. Facilities are characterized on a continuum spectrum from manufacturing facilities at one end and service facilities at the other end. In terms of trends in enterprise turn-over and gross domestic products (GDP) of nations, service industries play an increasingly more important role than their manufacturing counterparts. This article presents a bi-objective non-linear integer mathematical model for reliable facility location problem with stochastic demand. The concentration of this article is to present a new mathematical model in reliable facility location problem with immobile servers with congested facilities. The goal is to determine the location of both inexpensive and reliable facilities. Therefore, a bi-objective mathematical programming model is presented in which total cost and reliability of system are simultaneously optimized. There are many real life applications of the proposed model such as: automated teller machines, communication networks, vending machines, local clinics, hospitals and medical centers, relief distribution centers and reconstruction center locations, kinds of education systems, police stations, truck terminals, hotels, city logistics terminals, parks, bus stops, press delivery networks, locating post boxes, and the like. Since the proposed model is NP-Hard, a multi-objective water flow-like algorithm (MOWFLA) is presented to solve the model. To demonstrate the performance of the proposed algorithm, different test problems are first generated. Then, multi-objective genetic algorithm as best-developed algorithm in the literature and GAMS software, integrating the objectives with LP-metric method, are applied to justify the performance of proposed MOWFLA. According to objective function value (OFV) and computational time (CPUT) metrics, the results show that the proposed algorithm are capable to solve proposed congested facility location problem in large size problems.