A hybrid evolutionary immune algorithm for fuzzy flexible job shop scheduling problem with variable processing speeds

In this study, a fuzzy flexible job shop scheduling problem with variable processing speeds is considered. To address this problem, a multi-objective hybrid evolutionary immune algorithm (HEIA) is proposed, where the fuzzy maximum completion time (makespan) and fuzzy total energy consumption are optimized simultaneously. In the proposed HEIA, a two left-shift heuristic based active decoding method is proposed to optimize the fuzzy makespan. Then, two hybrid evolutionary strategies are used to separately enhance the exploration ability and exploitation ability, where a reference point-based angle selection strategy is incorporated to optimize the search mechanism. For the first evolutionary strategy, a Pareto similar information-based crossover operator is adopted to improve population diversity. For the second evolutionary strategy, a deep local search mechanism and four objective-driven neighborhood structures are developed. Finally, five types of instances are generated to verify the effectiveness of the proposed HEIA.