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<p>Addressing the critical need for sustainable industrial activities, this study investigates energy-efficient scheduling in multi-factory supply chains, encompassing production, distribution, logistics, batch delivery systems, and variable machining speeds. The objective is to minimize total energy consumption across interconnected factories by capturing the combined effects of processing time, idle times, and batch deliveries with vehicle capacity constraints through a mixed-integer linear programming model. Due to the problem’s computational complexity, two metaheuristic algorithms are proposed: a calibrated Genetic Algorithm and a novel Iterated Greedy Learning-Based method. Computational results show that Iterated Greedy Learning-Based method reduces total energy consumption compared to Genetic Algorithm and achieves near-optimal solutions with less than 2.5% deviation from exact results in small-scale cases. These results emphasize the strategic importance of integrating energy-conscious scheduling into supply chain strategies to advance the pursuit of sustainable industrial systems.</p>

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Towards a Sustainable Future: Integrating Energy Efficiency in Multi-factory Supply Chain Scheduling

  • 期刊论文

  • 发表日期：

    2025年5月3日

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  • Neda Karimi,
  • Soroush Alinia

摘要

Addressing the critical need for sustainable industrial activities, this study investigates energy-efficient scheduling in multi-factory supply chains, encompassing production, distribution, logistics, batch delivery systems, and variable machining speeds. The objective is to minimize total energy consumption across interconnected factories by capturing the combined effects of processing time, idle times, and batch deliveries with vehicle capacity constraints through a mixed-integer linear programming model. Due to the problem’s computational complexity, two metaheuristic algorithms are proposed: a calibrated Genetic Algorithm and a novel Iterated Greedy Learning-Based method. Computational results show that Iterated Greedy Learning-Based method reduces total energy consumption compared to Genetic Algorithm and achieves near-optimal solutions with less than 2.5% deviation from exact results in small-scale cases. These results emphasize the strategic importance of integrating energy-conscious scheduling into supply chain strategies to advance the pursuit of sustainable industrial systems.