Solving fuzzy scheduling using clustering method and bacterial foraging algorithm

Multi-objective fuzzy flexible job-shop scheduling problem (MFFJSSP) is a combination of multi-objective fuzzy scheduling and flexible job shop scheduling, which has higher complexity and is an NP-hard problem. For incompletely automated job shop, MFFJSSP is more important and conforms to the actual production. To solve the MFFJSSP, an effective multi-objective fuzzy scheduling method was proposed. First, a fuzzy number processing method based on fuzzy clustering that is more in line with the actual production was designed to replace the classic triangular fuzzy number. Second, based on the critical path theory, a more efficient method of critical operation block selection and critical operation movement were designed and applied them to the chemotaxis stage of bacterial foraging optimization algorithm to improve the accuracy of optimization and reduce invalid movement. Third, a novel decision tree-based bacterial reproduction method was designed to prevent the algorithm from falling into a local optimal solution. Finally, some cases were generated to verify the feasibility and effectiveness of the proposed algorithm from multiple dimensions.

Automated summary

Multi-objective fuzzy flexible job-shop scheduling problem (MFFJSSP) is a complex NP-hard problem that combines multi-objective fuzzy scheduling and flexible job shop scheduling. For incompletely automated job shops, solving MFFJSSP is crucial as it aligns with actual production. To address this problem, an effective multi-objective fuzzy scheduling method was proposed.