A ranking-based differential evolution algorithm for hybrid flow shop sustainable scheduling
Abstract With the increasing of environmental pressure, the sustainable production for hybrid flow shops (HFS) has attracted more attention due to its broad industrial applications. In implementing the sustainable production of HFS, the selection of parallel machines for various jobs is a vital step. In light of this, a multi-objective mathematical model for minimization of makespan and energy consumption of HFSP was formulated. The sustainability of parallel machines were evaluated and ranked according to fuzzy TOPSIS method. To solve the multi-objective model of HFSP, an improved differential evolution algorithm was presented to assign the job with the ranked parallel machines, which can narrow the search scope and accelerate the convergence speed. Finally, a case study was presented to evaluate the effectiveness of the proposed ranking-based algorithm and to prove the feasibility of the model. The results showed that the proposed improved algorithm outperforms NSGA-II and PSO in searching for non-dominated solutions, which can effectively solve the sustainable production of HFSP.
