Energy, cost and job-tardiness-minimized scheduling of energy-intensive and high-cost industrial production systems

Ziyan Zhao, Qi Jiang, Shixin Liu, Meng Chu Zhou, Xiaochun Yang, Xiwang Guo

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article number108477
JournalEngineering Applications of Artificial Intelligence
Volume133
DOIs
StatePublished - Jul 2024

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Artificial Intelligence
  • Electrical and Electronic Engineering

Keywords

  • Green manufacturing
  • Mixed integer linear program
  • Multi-objective evolutionary algorithm
  • Production scheduling

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