Department of Industrial Engineering, University of Tehran, Tehran, Iran
Department of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran
Department of Industrial Engineering, University of Tehran, Kish Int’l Campus, Iran
In previous investigations in the field of flexible flow shop scheduling problem, rework probability for operations was ignored. As these kinds of problems are NP-hard, so we presented an enhanced invasive weed optimization (EIWO) meta-heuristic algorithm in order to solve the addressed problem with probable rework times, transportation times with a conveyor between two subsequent stages, different ready times and anticipatory sequence dependent setup times. The optimization criterion is to minimize makespan. Although invasive weed optimization (IWO) is an efficient algorithm and has been attracted by many researchers recently, but to increase the capability of IWO, we added mutation operation to enhance the exploration in order to prevent sticking in local optimum. In addition, affinity function is embedded to obstruct premature convergence. With these changes, we balance exploration and exploitation of IWO. Since, the performance of our proposed algorithm depends on parameters values, hence, we applied a popular design of experimental methodology called response surface method (RSM). To evaluate the proposed algorithm, first some random test problems were generated and compared with three benchmark algorithms. The related results were analyzed by statistical tools. The experimental results and statistical analyses demonstrated that the proposed EIWO was effective for the problem.