Swarm-based neighbourhood search algorithm for fuzzy flexible job shop scheduling

Abstract

Fuzziness and flexibility are the features of most production processes; however, the scheduling problems with both flexible process plans and fuzzy processing conditions are not investigated fully for high complexity. This paper proposes an efficient swarm-based neighbourhood search algorithm (SNSA) for the fuzzy flexible job shop scheduling problem. In SNSA, ordered operation-based representation is used to indicate the solution of operation sequence sub-problem and machine assignment sub-problem is converted into a cell formation one, in which machines are regarded as cells and operations are allocated into cells. In each generation, two swaps, an insertion and tournament selection are applied to update swarms. Some numerical experiments are conducted by using some instances to show the effectiveness of SNSA. Computational results show that SNSA performs better than the existing methods from literature.

Keywords:

• fuzzy flexible job shop scheduling
• swarm-based neighbourhood search
• swap
• insertion
• tournament selection

Acknowledgements

This paper is supported by the National Natural Science Foundation of China (70901064).