An improved non-dominated sorting biogeography-based optimization algorithm for the (hybrid) multi-objective flexible job-shop scheduling problem

With the continuous advancement of intelligent manufacturing and industry 4.0, production scheduling has become a significant problem that most enterprises must deal with. Thereinto, (hybrid) multi objective flexible job-shop scheduling problem, widely existing in the real-life manufacturing systems, is one of the NP-hard problems in various scheduling problems. Consequently, in this paper, an improved non-dominated sorting biogeography-based optimization (INSBBO) algorithm has been proposed to solve the problem. First of all, to overcome the pressure scarcity of individual selection in the Pareto dominance principle, especially in the late iteration of the algorithm, a novel V-dominance principle based on the volume enclosed by the normalized objective function values has been developed to enhance the convergence speed. Then, a hybrid variable neighborhood search (HVNS) structure is designed as a local search algorithm to amend the local search ability. Thereafter, for avoiding the loss of the partial (sub-)optimal solutions in the iteration, an elite storage strategy (ESS) is constructed to store the (sub-)optimal solutions. Additionally, we modify the internal habitat suitability index (HSI), migration and mutation operators of the NSBBO algorithm to further improve its performance. To evaluate the effectiveness of the above improved operations and the robustness of parameter setting, we compare the performances of each modified operation and critical parameter combination through multiple independent running the typical scheduling instance from the literature. The statistical results exhibit that each amended operation has a significant influence on the performance of INSBBO and its key parameter configuration is robust. Meanwhile, INSBBO has a better or similar performance among other state-of-the-art intelligent algorithms by comparing three classical benchmark scheduling datasets. (C) 2020 Published by Elsevier B.V.