Self-adaptive discrete invasive weed optimization for the blocking flow-shop scheduling problem to minimize total tardiness

This paper proposes a self-adaptive discrete invasive weed optimization (SaDIWO) to solve the blocking flow-shop scheduling problem (BFSP) with the objective of minimizing total tardiness which has important applications in a variety of industrial systems. In the proposed SaDIWO, an improved NEH-based heuristic is firstly presented to generate an initial solution with high quality. Then, to guide the global exploration and local exploitation, a self-adaptive insertion-based spatial dispersal is presented. A distance-based competitive exclusion is developed to strike a compromise between the quality and diversity of offspring population. A variable neighborhood search with a speed-up mechanism is embedded to further enhance exploitation in the promising region around the individuals. Afterward, the parameters setting and the effectiveness of each component of the proposed algorithm are investigated through numerical experiments. The performance of the proposed algorithm is evaluated by comparisons with the existing state-of-the-art algorithms in the literature. Experimental results show that the proposed algorithm outperforms the existing state-of-the-art algorithms. Furthermore, the proposed SaDIWO also improves the best known solutions for 132 out of 480 problem instances.