提出新算法优化制造排产能耗与工期，兼顾效率与节能。
Refined Iterated Pareto Greedy for Energy-aware Hybrid Flowshop Scheduling with Blocking Constraints
- 用改进的迭代帕累托贪心法求解多目标排产问题。
- 在大规模实例上显著降低能耗与完工时间，优于现有算法。
- 适合关注绿色制造与智能排产的企业或研究者。
能源短缺、供应地缘政治风险、价格上涨及气候变化迫使全球经济寻求更节能的运营方案。制造业作为主要耗能行业之一，亟需高效节能调度策略。本文研究带阻塞约束的混合流水车间调度问题（BHFS），旨在同时最小化最大完工时间（即工期）和总体能耗，这是汽车、制药等多个行业的典型场景。能耗与工期常存在冲突，为此我们构建了新的多目标混合整数规划模型，并提出增强epsilon约束法求解帕累托最优解。此外，设计了一种高效的多目标元启发式算法——精炼迭代帕累托贪心（RIPG），可在合理时间内求解大规模实例。通过小、中、大三类规模实例测试，与两种经典算法对比，结果表明所提方法在求解质量与效率上均具优势。
原文摘要 · Abstract (English)
The scarcity of non-renewable energy sources, geopolitical problems in its supply, increasing prices, and the impact of climate change, force the global economy to develop more energy-efficient solutions for their operations. The Manufacturing sector is not excluded from this challenge as one of the largest consumers of energy. Energy-efficient scheduling is a method that attracts manufacturing companies to reduce their consumption as it can be quickly deployed and can show impact immediately. In this study, the hybrid flow shop scheduling problem with blocking constraint (BHFS) is investigated in which we seek to minimize the latest completion time (i.e. makespan) and overall energy consumption, a typical manufacturing setting across many industries from automotive to pharmaceutical. Energy consumption and the latest completion time of customer orders are usually conflicting objectives. Therefore, we first formulate the problem as a novel multi-objective mixed integer programming (MIP) model and propose an augmented epsilon-constraint method for finding the Pareto-optimal solutions. Also, an effective multi-objective metaheuristic algorithm. Refined Iterated Pareto Greedy (RIPG), is developed to solve large instances in reasonable time. Our proposed methods are benchmarked using small, medium, and large-size instances to evaluate their efficiency. Two well-known algorithms are adopted for comparing our novel approaches. The computational results show the effectiveness of our method.
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