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.

Automated summary

This study focuses on a fuzzy flexible job shop scheduling problem with variable processing speeds and proposes a multi-objective hybrid evolutionary immune algorithm (HEIA) to solve it by optimizing the fuzzy maximum completion time (makespan) and fuzzy total energy consumption simultaneously. The HEIA adopts a two left-shift heuristic based active decoding method to optimize the fuzzy makespan and incorporates two hybrid evolutionary strategies to enhance the exploration ability and exploitation ability. The proposed HEIA is tested on five types of instances to verify its effectiveness.