Abstract
Due to the change of consumption pattern, distributed flexible production is now becoming a major manufacturing mode. The machine breakdown seriously affects the production in workshops. How to efficiently adjust the scheduling scheme after machine breakdowns in distributed flexible job shop is an important concern for enterprises. Meanwhile, energy consumption is attracting more and more attention as environmental issues become increasingly serious. Thus, the energy-saving scheduling on distributed flexible job shop considering machine breakdowns is studied, and a mixed-integer programming model is established to optimize makespan, total energy consumption and machine load difference, simultaneously. Based on the problem characteristics, an improved memetic algorithm is designed. To improve the quality of initial solutions, an initialization strategy incorporating multiple rules is designed. An active decoding method with an adjustment strategy is adopted to fully utilize machine idleness. To expand the search range, the idea of dual-population collaborative optimization is used. Multi-level intelligent mutation operation strategies are introduced to further enhance the quality of solutions. In addition, an adaptive parameter rule is designed to adjust to appropriate search capabilities at different evolutionary stages. To verify the effectiveness of the proposed strategies and algorithm, the memetic algorithm, the artificial bee colony algorithm, the differential evolution algorithm and the estimation of distribution algorithm are chosen as comparison algorithms. The same iteration number and same running time are used as the termination criteria of all algorithms, and 60 instances of different scales are solved. The experimental results demonstrate that the effectiveness of the strategies and algorithm designed in this paper. Notably, our algorithm consistently achieved good results even when the same running time is used as the termination criterion.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.