A multi-objective discrete invasive weed optimization for multi-objective blocking flow-shop scheduling problem

The flow-shop scheduling problem with blocking constraints has received an increasing concern recently. However, multiple scheduling criteria are rarely considered simultaneously in most research. Therefore, in this paper, a multi-objective blocking flow-shop scheduling problem (MOBFSP) that minimizes the makespan and total tardiness simultaneously is investigated. To address this problem, a multi-objective discrete invasive weed optimization (MODIWO) algorithm is proposed. In the proposed MODIWO, a high-quality and diversified initial population is firstly constructed via two heuristics and varying weighed values. Then, a reference line-based reproduction and a sliding insertion-based spatial dispersal are developed to guide the global exploration and local exploitation of algorithm. Meanwhile, to enhance intensification search in local region, a self-adaption phase is introduced, which is implemented by a Pareto-based two stage local search with speedup mechanism. Furthermore, a new competitive exclusion strategy is also embedded to construct a superior population for the next generation. Finally, extensive computational experiments and comparisons with several recent state-of-the-art algorithms are carried out based on the well-known benchmark instances. Experimental results demonstrate the efficiency and effectiveness of the proposed MODIWO in solving the considered MOBFSP.