考虑能源效率的批调度问题研究与算法设计

Batch scheduling problem (BSP), which is derived from semiconductor manufacturing, has become a kind of important new production scheduling problem. It has extensive applications in many fields, such as aviation industry, steel casting, metallurgy, electroplating and so on. The research on BSP has important theoretical value and practical significance. Traditional batch scheduling research is mainly oriented by productivity. However, modern "green manufacturing" requires that enterprises not only pursue productivity, but also take into account the impact of production to the environment and the utilization ratio of resource and energy. In view of the current research situation of lack of energy efficiency on BSP, a batch scheduling problem considering energy efficiency is proposed in this project: Construct various batch scheduling optimization models for minimizing total energy consumption on different machine environments; Analyze the complexity of the involved problems and the corresponding models; Through the analysis of the constraint of processing energy consumption, extract the main factors that influence the objective of minimizing the total energy consumption and then design the constructive based meta-heuristic; By relax constraint conditions on different models, propose lower bounds to evaluate the algorithm performance and verify the effectiveness of the proposed algorithm by simulation experiments. Through the research of this project, it can further expand and enrich the modern production scheduling theory, and offer basis for manufacturing enterprises to implement the green manufacturing and sustainable development.

批调度是从半导体制造业中提炼出的一类重要的新型生产调度问题，已广泛应用于航空工业，钢铁铸造、冶金、电镀等各个领域，其研究具有重要的理论价值和实际意义。传统批调度研究主要以生产效益为导向,而现代"绿色制造"要求工业生产不仅要追求生产效益,还需要综合考虑生产过程对环境的影响以及资源和能源的利用效率。针对当前批调度研究缺乏考虑能源效率的现状，本课题提出考虑能源效率的批调度问题：构建不同机器环境下最小化总能源消耗的批调度优化模型；分析所涉及问题以及建立的相应模型的复杂性程度；通过对加工能耗约束的分析，提炼影响最小化总能源消耗目标的主要因素，进而设计基于构建性的元启发式算法；通过松弛不同模型的约束条件提出问题下界评价算法性能，并设计仿真实验验证所提算法的有效性。通过本项目的研究，进一步拓展和丰富现代生产调度理论，为生产企业实现绿色制造及可持续发展目标提供依据和帮助。

绿色制造是当前制造业的重要发展方向，批调度是制造业广泛存在的一类新型调度问题。研究考虑能源效率的批调度问题，对于相关企业实现“绿色制造”具有重要现实意义，同时也符合国家节能减排和能源安全战略的要求。本项目从理论分析和算法设计等方面进行了研究，取得的成果主要如下：1）针对考虑能源效率的新型批调度问题，分别从模型构建、问题复杂性、最优解性质、松弛问题下界及算法性能分析等方面进行了理论研究；2）考虑批调度问题中能源效率相关约束特性，针对性的提出了若干个与能源效率相关的新概念、公式和定理，包括首次提出资源敏感度和批属性比的概念，设计考虑能源效率的新型资源函数，设计度量工件间特性的距离函数。3）设计了求解考虑能源效率的混合遗传算法、禁忌搜索算法及多目标蚁群优化算法等元启发式优化算法，特别从候选列表、搜索策略和混合策略等方面进行了相应改进，研究表明基于构建性的优化算法在多数情况下更易于在批调度类型问题上进行求解。4）提出了一系列考虑能源效率的测试算例，搭建统一仿真实验平台，通过大规模实验验证所提算法的有效性。通过本项目的研究，进一步拓展和丰富了现代生产调度理论，并为生产企业实现绿色制造及可持续发展目标提供了依据和帮助。