Novel Approach to Energy-Efficient Flexible Job-Shop Scheduling Problems

Nikolaos Rakovitis, Dan Li, Nan Zhang, Jie Li, Liping Zhang, Xin Xiao

Research output: Contribution to journalArticlepeer-review

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In this work, we develop a novel mathematical formulation for the energy-efficient flexible job-shop scheduling problem using the improved unit-specific event-based time representation. The flexible job-shop is represented using the state-task network. It is shown that the proposed model is superior to the existing models with the same or better solutions by up to 13.5% energy savings in less computational time. Furthermore, it can generate feasible solutions for large-scale instances that the existing models fail to solve. To efficiently solve large-scale problems, a grouping-based decomposition approach is proposed to divide the entire problem into smaller subproblems. It is demonstrated that the proposed decomposition approach can generate good feasible solutions with reduced energy consumption for large-scale examples in significantly less computational time (within 10 minutes). It can achieve up to 43.1% less energy consumption in comparison to the existing gene-expression programming-based algorithm.
Original languageEnglish
Pages (from-to)121773
Early online date12 Aug 2021
Publication statusPublished - 12 Aug 2021


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