A two-level approach for multi-objective flexible job shop scheduling and energy procurement

Sascha Christian Burmeister , Daniela Guericke , Guido Schryen
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Abstract

Dynamic energy tariffs in combination with energy storage systems (ESS) and renewable energy sources (RES) offer manufacturers new opportunities to optimize their energy consumption. Flexible production planning empowers decision-makers not only to minimize makespan, but also to reduce energy costs and emissions. However, flexible production planning is a major challenge due to the fact that scheduling decisions affect energy demand, whose costs and emissions depend on energy procurement decisions. In Operations Research, the Green Flexible Job Shop Scheduling Problem (FJSP) addresses production planning decisions incorporating resource, environmental, and economic objectives. The Energy Procurement Problem (EPP) aims to efficiently acquire energy resources. In the literature, existing approaches for energy-aware scheduling neglect to procure energy from sources such as an uncertain dynamic energy market, RES, and ESS. We aim to close this research gap and propose a two-level approach based on a memetic Non-dominated Sorting Genetic Algorithm (NSGA-III) and linear programming with the goal of minimizing the makespan, energy costs, and emissions of a schedule, incorporating dynamic energy prices and emissions, RES, and ESS. We evaluate the approach in computational experiments using FJSP benchmark instances from the literature as part of a rolling horizon approach with real energy market data. We investigate the impact of RES and ESS by presenting estimated Pareto fronts, showing potential savings in energy cost and carbon emissions.

Abstract Image

多目标柔性作业车间调度与能源采购的两级方法
动态能源关税与储能系统(ESS)和可再生能源(RES)相结合,为制造商提供了优化能源消耗的新机会。灵活的生产计划不仅使决策者能够最大限度地缩短完工时间,而且还能降低能源成本和排放。然而,灵活的生产计划是一个主要挑战,因为调度决策会影响能源需求,而能源需求的成本和排放取决于能源采购决策。在运筹学中,绿色柔性作业车间调度问题(FJSP)解决了结合资源、环境和经济目标的生产计划决策。能源采购问题(EPP)旨在有效地获取能源资源。在文献中,现有的能源感知调度方法忽略了从不确定的动态能源市场、RES和ESS中获取能源。为了缩小这一研究差距,我们提出了一种基于模因非支配排序遗传算法(NSGA-III)和线性规划的两级方法,其目标是将动态能源价格和排放、RES和ESS结合起来,使计划的完工时间、能源成本和排放最小化。我们在计算实验中使用文献中的FJSP基准实例来评估该方法,作为具有真实能源市场数据的滚动地平线方法的一部分。我们通过提出估计的帕累托前沿来研究RES和ESS的影响,显示了能源成本和碳排放的潜在节约。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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