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首页> 外文期刊>International Journal of Production Research >Reduction of power consumption and carbon footprints by applying multi-objective optimisation via genetic algorithms
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Reduction of power consumption and carbon footprints by applying multi-objective optimisation via genetic algorithms

机译：通过遗传算法应用多目标优化来减少功耗和碳足迹

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摘要

Firms heavily emphasise reducing carbon footprint, an area warranting further improvement. This study examines carbon footprint within the context of production scheduling. Two multi-objective scheduling problems involving economic- and environmental-related criteria are studied: (1) a batch-processing machine scheduling problem to minimise the total weighted tardiness and carbon footprint simultaneously; (2) a triple-criteria scheduling problem involving of a hybrid flow shop consisting of a batch-processing machine followed by two parallel-processing machines, in which the shop attempts to minimise the total weighted tardiness, carbon footprint and peak power. Since the above problems are treated as a true multi-objective optimisation problem, decision-makers should select a solution among the trade-off solutions provided in the Pareto-optimal set. Therefore, the non-dominated sorting-based genetic algorithm Ⅱ (NSGA-Ⅱ) is implemented, which identifies the set of approximate efficient schedules to both multi-objective scheduling problems. Moreover, an adaptive multi-objective genetic algorithm (AMGA) is developed to generate the reference Pareto front, which validates the results that are obtained using NSGA-Ⅱ. Results of this study demonstrate both the effectiveness of AMGA in converging to the true Pareto-optimal set and the efficiency of NSGA-Ⅱ.
机译：公司高度重视减少碳足迹，这是需要进一步改善的领域。这项研究在生产计划的背景下研究了碳足迹。研究了两个涉及经济和环境相关标准的多目标调度问题：（1）批量处理机调度问题，以同时最小化总加权拖延和碳足迹； （2）一个三标准调度问题，涉及一个混合流水车间，该车间由批处理机和随后的两台并行处理机组成，其中，该车间试图使总加权拖尾率，碳足迹和峰值功率最小化。由于上述问题被视为真正的多目标优化问题，决策者应在帕累托最优集合中提供的权衡解决方案中选择一个解决方案。因此，实现了基于非支配排序的遗传算法Ⅱ（NSGA-Ⅱ），该算法识别了针对两个多目标调度问题的近似有效调度集。此外，还开发了一种自适应多目标遗传算法（AMGA）来生成参考帕累托前沿，从而验证了使用NSGA-Ⅱ获得的结果。研究结果表明，AMGA收敛于真实的帕累托最优集的有效性和NSGA-Ⅱ的有效性。

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