Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm

The flexible job shop scheduling problem (FJSP), which is NP-hard, widely exists in many manufacturing industries. It is very hard to be solved. A multi-swarm collaborative genetic algorithm (MSCGA) based on the collaborative optimization algorithm is proposed for the FJSP. Multi-population structure is used to independently evolve two sub-problems of the FJSP in the MSCGA. Good operators are adopted and designed to ensure this algorithm to achieve a good performance. Some famous FJSP benchmarks are chosen to evaluate the effectiveness of the MSCGA. The adaptability and superiority of the proposed method are demonstrated by comparing with other reported algorithms.

Automated summary

A multi-swarm collaborative genetic algorithm based on the collaborative optimization algorithm was proposed for the NP-hard flexible job shop scheduling problem. Good operators were adopted to ensure good algorithm performance, which was evaluated using famous benchmarks for FJSP. The proposed method demonstrated adaptability and superiority compared to other reported algorithms.