A Self-Adapting Water Wave Optimization Algorithm for Distributed Blocking Flow-Shop Scheduling Problem

The distributed blocking flow-shop scheduling problem (DBFSP), which has been proven to be a strongly NP-hard problem, has important applications in a variety of industrial systems. In this paper, a self-adapting water wave optimization (SAWWO) algorithm is proposed to solve the blocking flow-shop scheduling problem with the criterion of minimizing the makespan. In SAWWO, the candidates are represented as discrete job permutations. Two heuristics are utilized to obtain the desirable initial solution. In the propagation phase, the self-adapting spatial dispersal operator is designed to balance the exploration and exploitation of SAWWO. Four local search methods are introduced to intensify the exploitation ability of the algorithm in the local region. Furthermore, the redesigned path-relinking method is presented as the modified refraction operator to help the algorithm jump out the local optimal. Additionally, the performance of the proposed algorithm is evaluated by comparing with five other state-of-the-art algorithms. The statistical results demonstrate the effectiveness of SAWWO for solving the DBFSP.