Energy, cost and job-tardiness-minimized scheduling of energy-intensive and high-cost industrial production systems
Energy consumption, production cost, and efficiency are highly concerned by decision makers of energy-intensive and high-cost industrial production systems. Intelligent production scheduling is a necessary means to achieve their optimization. This work delves into a novel multi-objective production...
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| Vydané v: | Engineering applications of artificial intelligence Ročník 133; s. 108477 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Elsevier Ltd
01.07.2024
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| Predmet: | |
| ISSN: | 0952-1976, 1873-6769 |
| On-line prístup: | Získať plný text |
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| Shrnutí: | Energy consumption, production cost, and efficiency are highly concerned by decision makers of energy-intensive and high-cost industrial production systems. Intelligent production scheduling is a necessary means to achieve their optimization. This work delves into a novel multi-objective production scheduling problem arising from a steel hot-rolling process, which is a representative energy-intensive and high-cost industrial process. The challenge of the problem involves scheduling customized production jobs subject to intricate process constraints with the goal to minimize three objective functions, i.e., energy consumption, setup cost, and the number of tardy jobs. A mixed integer linear programming model is formulated for the problem. In order to solve it, an improved multi-objective evolutionary algorithm based on decomposition is presented. The algorithm incorporates problem-specific encoding and model-based decoding mechanisms, rendering it well-suited for addressing the concerned multi-constrained multi-objective optimization problem. The introduced modified Tchebycheff approach mitigates the impact of objective functions with varying value ranges on the algorithm’s convergence. Additionally, a Metropolis acceptance criterion is integrated to facilitate the escape from local optimal solutions, enhancing the algorithm’s global optimization capability. Numerous experiments are conducted to verify the effectiveness of the improvements and to compare the performance of the presented algorithm against its competitive peers. The results demonstrate its high performance, suggesting its significant potential for its application to steel hot-rolling systems.
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•A practical scheduling problem optimizes energy consumption, cost, and efficiency. A mixed integer linear programming model and a new solution method for soling it. |
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| ISSN: | 0952-1976 1873-6769 |
| DOI: | 10.1016/j.engappai.2024.108477 |