Intelligent optimization under blocking constraints: a variant iterated greedy algorithm for hybrid flow shop scheduling problem

Abstract The hybrid flow shop scheduling problem (HFSP) is one of the most relevant optimization problems in manufacturing industry. This paper aims to minimize the makespan for a hybrid flow shop scheduling problem with blocking constraints (BHFSP), which is an extension of traditional HFSP and has more practical significance. We construct the mathematical model of BHFSP and verify its correctness by Gurobi. Specifically, our study develops a variant iterated greedy (VIG) algorithm to solve the above model. The main novelties of the proposed algorithm are that a hybrid decoding strategy, i.e., forward decoding and backward decoding, are designed to calculate the objective value; a parallel mechanism is adopted to increase the diversity of VIG; a simple local search based on swap operator and a cooperative mechanism based on crossover are proposed to enhance the convergence and diversity of the algorithm. Comprehensive computational experiments are conducted on 100 instances to evaluate the performance of the proposed algorithm. The experimental results and statistical analyses show that the proposed algorithm outperforms the six state-of-the-art algorithms and can effectively solve BHFSP.