The Distributed Permutation Flowshop Scheduling Problem With Different Transport Timetables And Loading Capacities

In this paper, a new type of distributed permutation flow-shop scheduling problem (DPFSP) is proposed, in which different transport timetable and different loading capacity for each factory are considered. Under this model generalization, we assume that there are a total of F different factories and then a sequence has to be calculated for the jobs assigned to each factory. We also assume that the distances to different factories are not equal, transport timetables and loading capacities are also considered. Even though the assumption in this study is necessary for today's economy, there are seldom studies in literature dealing with such constraints. To the best of our knowledge, it is the first report on incorporating vehicle timetables and vehicle capacities to DPFSP. Standard DPFSP is a typical NP-hard problem and this problem is even harder. A simulated annealing based local search with multiple different neighborhoods is used to solve the problem with the objective to minimize the maximum completion time. Three different neighborhood searching methods are also proposed. A comprehensive computational and statistical analysis is conducted in order to analyze the performance of the proposed method.