An enhanced invasive weed optimization for makespan minimization in a flexible flowshop scheduling problem

In previous investigations in the field of flexible flow shop scheduling problems, the rework probability for operations was ignored. As these kinds of problems are NP-hard, we present an Enhanced Invasive Weed Optimization (EIWO) 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 meta-heuristic algorithm and has been used by many researchers recently, to increase the capability of IWO, we added a mutation operation to enhance the exploration in order to prevent sticking in local optimum. In addition, an affinity function is embedded to obstruct premature convergence. With these changes, we balance the exploration and exploitation of IWO. Since the performance of our proposed algorithm depends on parameters values, we apply the popular design of an experimental methodology, called the Response Surface Method (RSM). To evaluate the proposed algorithm, first, some random test problems are generated and compared with three benchmark algorithms. The related results are analyzed by statistical tools. The experimental results and statistical analyses demonstrate that the proposed EIWO is effective for the problem. (C) 2014 Sharif University of Technology. All rights reserved.