Stamping workshop scheduling optimization considering multiple constraints under time-of-use electricity pricing
•First consideration of time‑of‑use electricity pricing in stamping workshop scheduling optimization.•A stamping workshop scheduling model considering multiple constraints is established.•A multi-threaded parallel decoding method is designed to improve decoding efficiency.•A selection strategy based...
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| Vydané v: | Computers & industrial engineering Ročník 206; s. 111199 |
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| Hlavní autori: | , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.08.2025
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| Predmet: | |
| ISSN: | 0360-8352 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | •First consideration of time‑of‑use electricity pricing in stamping workshop scheduling optimization.•A stamping workshop scheduling model considering multiple constraints is established.•A multi-threaded parallel decoding method is designed to improve decoding efficiency.•A selection strategy based on non-dominated hierarchy is proposed to accelerate the convergence of the algorithm.•Application validation is conducted in the stamping workshop of an automobile manufacturing enterprise.
In the context of intelligent manufacturing, the automotive manufacturing industry is facing the dual challenges of intensifying competition and rising energy costs. The stamping process is a critical stage in automobile manufacturing. To enhance the production efficiency of the stamping workshop and reduce energy costs, this paper aims to minimize the maximum completion time and total electricity cost by establishing a stamping workshop scheduling model considering multiple constraints under time-of-use (TOU) electricity pricing. An improved NSGA-II algorithm is proposed to solve this problem. The algorithm adopts a hybrid three-layer encoding scheme and designs a multi-threaded parallel decoding method to improve decoding efficiency. A penalty function approach is adopted to generate feasible solutions that satisfy workshop constraints. Meanwhile, a selection strategy based on non-dominated hierarchy is proposed to accelerate the convergence speed of the algorithm in the early stage. Additionally, adaptive crossover and mutation probabilities are introduced to enhance the search ability of the algorithm. Finally, through actual case studies and algorithm comparisons, the effectiveness and superiority of the improved NSGA-II algorithm are verified. |
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| ISSN: | 0360-8352 |
| DOI: | 10.1016/j.cie.2025.111199 |