A Particle Swarm Optimization Approach to Joint Location and Scheduling Decisions in a Flexible Job Shop Environment

In traditional scheduling literature, it is generally assumed that the location of facilities are predetermined and fixed in advance. However, these decisions are interrelated and may impact each other significantly. Therefore finding a schedule and facility location has become an important problem as an extension of the well-known scheduling problems. In this research we consider joint decisions on planning of machines’ layout and scheduling of jobs on each machine in a Flexible Job Shop environment. The aim is to minimize maximum completion time. The problem is formulated as a mathematical programming model and is solved using an enhanced particle swarm optimization (PSO). Furthermore, parameters of algorithm is optimized by Taguchi statistical tool. A lower bound is also devised for evaluating the obtained results.