工业工程

工业工程 ›› 2024, Vol. 27 ›› Issue (5): 43-52.doi: 10.3969/j.issn.1007-7375.230117

• 服务运营管理与调度优化 • 上一篇    

考虑业务冲突的煤炭多周期库存–运输联合优化问题

刘志江1, 杜刚2, 张海龙2, 徐陆飞2, 白志军1, 耿桦1   

  1. 1. 国家能源投资集团有限责任公司，北京 100011;
    2. 南瑞集团有限公司(国网电力科学研究院有限公司)，江苏 南京 211106)
  • 收稿日期:2023-06-03 发布日期:2024-11-05
  • 通讯作者: 徐陆飞 (1985—) ，男，辽宁省人，高级工程师，主要研究方向为优化算法及应用、电力系统自动化。Email: 220213552@seu.edu.cn E-mail:220213552@seu.edu.cn
  • 作者简介:刘志江 (1965—)，男，河北省人，教授级高级工程师，主要研究方向为大型能源类企业一体化运营模式、电网电厂技术等。Email: 17210001@ceic.com
  • 基金资助:
    国家重点研发计划资助项目 (2016YFB1200105，2016YFB0900502)；国家电网电力科学研究院研发资助项目 (FD-GF-DWGS-200001) 。

Joint Optimization of Multi-period Inventory and Transportion for Coal Considering Business Conflicts

LIU Zhijiang1, DU Gang2, ZHANG Hailong2, XU Lufei2, BAI Zhijun1, GENG Hua1   

  1. 1. China Energy Investment Group Co., Ltd., Beijing 100011, China;
    2. NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing 211106, China
  • Received:2023-06-03 Published:2024-11-05

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摘要： 研究大型能源企业在制定运营计划时面临的产业链产运销储一体化全局优化问题。针对现有模型通常只涵盖单一时间周期、忽略业务冲突，以及将运输和库存分开处理的不足，构建以企业计划时间范围内多周期总利润最大和业务冲突影响最小为目标，以不同周期的库存平衡关系以及产运销业务等为约束，同时考虑业务冲突的多周期库存–运输联合优化模型。针对该模型结构复杂、约束条件多和求解计算量较大的特点，本文提出一种软约束渐进增强的快速遗传算法。相比传统的遗传算法，该算法的运算速度可以提升5 ~ 10倍，显著提高了此类模型的求解效率。最后，将模型和算法应用在实际案例中，并对案例进行详细分析，验证了模型的可靠性和算法的高效性。本文提出的模型和算法有助于为大型能源企业的产业链一体化最佳运营提供科学、合理、高效、精确的决策支持。

关键词: 库存–运输联合优化, 多周期, 业务冲突, 快速遗传算法

Abstract: This study delves into the comprehensive optimization challenge of integrating production, operation, sales, and storage of the industrial chain when large-scale energy enterprises formulate their operational plans. To address the limitations of existing models, which typically cover only a single time period, ignore business conflicts, and treat transportation and inventory separately, this paper develops a multi-period inventory-transportation joint optimization model. The objective is to maximize the total profit and minimize the impact of business conflicts within the enterprise planning horizon. This model considers constraints such as inventory balance across different time periods and production-operation-sales activities, while business conflicts are also taken into account. Given the complexity of the model structure, numerous constraints, and substantial computation cost, this paper introduces a fast genetic algorithm with progressive enhancement of soft constraints. Compared to traditional genetic algorithms, this algorithm can accelerate computation by 5-10 times, significantly improving the efficiency of solving such models. Finally, the model and algorithm are applied to practical cases, while detailed analysis of these cases is conducted, verifying the reliability of the model and the efficiency of the algorithm. The model and algorithm proposed in this paper contribute to providing scientific, rational, efficient, and precise decision support for the optimal operations of the integrated industrial chain in large-scale energy enterprises.

Key words: inventory-transport joint optimization, multi-period, business conflicts, fast genetic algorithm

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