【作者】 刘杰；

【导师】 刘明周； 侯域；

【作者基本信息】 合肥工业大学 ， 工业工程与管理（专业学位）， 2025， 硕士

【摘要】 减少能源消耗是制造业流程中的一个重要议题,而作为制造业基石之一的冲压行业更是重要考虑对象。此外,针对部分时段电力使用量不平衡的情况,国家发改委推出了分时电价政策,这也是推进能源使用平衡、减少浪费的重要举措。以这二者为背景,本文重点研究了分时电价下的冲压车间作业调度问题。首先,本文进行了经典冲压车间及考虑能耗的新式冲压车间的生产分析,给出了两类冲压车间的生产模式及生产特点。在得到冲压车间生产特征的基础上,本文分析了冲压车间在生产中的重要能源使用对象,并整合得到了适用于冲压车间的能源消耗模型。其次,本文选择了适合车间调度优化的带精英策略的非支配排序遗传算法(NSGA-Ⅱ算法)以及适用于跳出局部最优解的模拟退火算法。针对两类算法中的不足,本文在NSGA-Ⅱ算法中采用了拉丁超立方采样(LHS)初始化种群及自适应邻域拥挤度策略,在模拟退火算法中采用了分段的退火系数及自适应劣解接受概率策略。在进行针对优化后,本文对这二者进行融合,形成了改进的融合退火NSGA-Ⅱ算法,且对改进算法利用标准验证函数及标准调度算例进行了验证,证明了算法的有效性与稳定性。在验证算法之后,本文以分时电价为重点,以生产能耗、能耗成本及最大完工时间为目标补全了冲压车间三目标调度优化模型,并构建了一套可供计算的实例。在对实例进行计算后,本文得出了针对不同目标的偏向性优化结果以及各个优化目标同等权重的综合优化结果。在完成以分时电价为重点的实例分析后,本文继续引入目前企业生产中较为常用的经济生产批量概念,并对上文实例进行针对性修改,形成了考虑经济生产批量的新实例。在新实例修改完成后,本文对其进行了调度优化计算,输出了不同优化调度结果。同时,本文在实例验证中将改进算法与同类型调度算法进行了对比,利用超体积指标(HV)值及多次实验的Wilcoxon秩和检验结果进一步证明了改进算法的显著性与结果的稳定性。最后,在以上分析的基础上,本文设计了一套适用于冲压车间的生产计划与调度系统(APS系统)。该系统具备多项生产及调度相关功能,可以帮助企业较好地提高生产效率,应用价值较高。本研究通过算法优化验证与系统开发,为分时电价下冲压车间节能调度提供了可参考方案,对制造业的绿色化转型具有实际意义。更多还原

【Abstract】 Reducing energy consumption is a critical issue in manufacturing processes,with the stamping industry—a cornerstone of manufacturing—being a key focus.Additionally,to address imbalances in electricity usage during specific periods,China’s National Development and Reform Commission(NDRC)has implemented a time-of-use(TOU)electricity pricing policy,serving as an important measure to promote balanced energy utilization and reduce waste.Against this dual backdrop,this study focuses on the stamping workshop scheduling problem under TOU pricing.First,this paper conducts a production analysis of both classic stamping workshops and modern stamping workshops with energy consumption considerations,outlining the production modes and characteristics of these two types of workshops.Based on the identified production features of stamping workshops,the paper analyzes key energy-consuming components in the production process and integrates them into an energy consumption model suitable for stamping workshops.Secondly,this study selects the NSGA-II algorithm for its suitability in workshop scheduling optimization and the simulated annealing algorithm for its effectiveness in escaping local optima.To address the limitations of both algorithms,several enhancements were implemented:for NSGA-II,Latin Hypercube Sampling(LHS)was adopted for population initialization along with an adaptive neighborhood crowding strategy;for simulated annealing,a segmented cooling coefficient and adaptive inferior solution acceptance probability strategy were introduced.Following these targeted improvements,the two algorithms were systematically integrated to develop an enhanced hybrid algorithm-the Annealing Adaptive Neighborhood-NSGA-Ⅱalgorithm.The proposed algorithm was rigorously validated using standard benchmark functions and canonical scheduling test cases,demonstrating both its effectiveness and operational stability.Following algorithm validation,a three-objective scheduling optimization model for stamping workshops is established,prioritizing TOU pricing while considering production energy consumption,energy costs,and maximum completion time.A computational case study is constructed,yielding optimization results for individual objectives and a comprehensive solution under equal weighting.Subsequently,the economic production lot(EPL)concept—commonly used in industrial practice—is incorporated to modify the case study,generating a revised instance that accounts for batch production economics.Optimization results for this revised instance are also derived.Meanwhile,in the experimental validation,the improved algorithm was compared with other scheduling algorithms of the same category.By employing the Hypervolume Indicator(HV)values and conducting multiple Wilcoxon signed-rank tests,this study further substantiated the statistical significance and robustness of the enhanced algorithm’s performance.Finally,building on the above analyses,an Advanced Planning and Scheduling(APS)system for stamping workshops is designed and developed.This system integrates multiple production and scheduling functionalities,demonstrating significant potential to enhance manufacturing efficiency and practical value for enterprises.Through algorithmic optimization verification and system development,this study provides a referential solution for energy-saving scheduling in stamping workshops under time-of-use electricity pricing,demonstrating practical significance for the green transformation of manufacturing industries.更多还原