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中国管理科学 ›› 2023, Vol. 31 ›› Issue (6): 276-286.doi: 10.16381/j.cnki.issn1003-207x.2020.1686

• 论文 • 上一篇    

面向时间优化的“任务-人员”匹配逆最优值方法:以石化设备抢修为例

张莉莉1, 杨文文2, 罗冠聪3   

  1. 1.大连海事大学航运经济与管理学院,辽宁 大连116026; 2.大连理工大学商学院,辽宁 盘锦124221;3.广东工业大学自动化学院,广东 广州510006
  • 收稿日期:2020-09-01 修回日期:2021-05-22 发布日期:2023-06-17
  • 通讯作者: 张莉莉(1982-),女(汉族),辽宁鞍山人,大连海事大学航运经济与管理学院,副教授,博士生导师,博士,研究方向:决策理论与方法,Email:lilizhang@dlmu.edu.cn. E-mail:lilizhang@dlmu.edu.cn
  • 基金资助:
    国家自然科学基金资助项目(71771036,72271038)

Inverse Optimal Value Method of “Task-personnel” Matching with Time Inferring: Taking Petrochemical Equipment Emergency Repair as an Example

ZHANG Li-li1, YANG Wen-wen2, LUO Guan-cong3   

  1. School of Economics, Ocean University of China, Qingdao 266100, China
  • Received:2020-09-01 Revised:2021-05-22 Published:2023-06-17
  • Contact: 张莉莉 E-mail:lilizhang@dlmu.edu.cn

摘要: 一旦重大装备突发故障,如不能及时抢修,小则生产中断,大则引发生命、环境、经济等方面的重大损失。抢修具有时间紧、任务急、高度复杂性等特征。针对此类抢修实际问题,本文以最小化设备紧急抢修实际损失与机会损失为目标,考虑关键任务与非关键任务的工艺逻辑串并联顺序,构建 “任务-人员”匹配正优化模型。然而,在该模型参数下,即使该模型的最优值仍超出同业对标成本容忍值,在此情况下,通过逆向思维,由果导因,逆向推演作业时间,构建对标成本值驱动的双层规划逆最优值模型。针对其0-1混合整数、非线性、双层规划、NP-hard的特征,设计混合“遗传-整数线性规划”算法,该算法结合了遗传算法的并行化和整数线性规划较好的全局搜索能力。基于企业实际调研与访谈获得相关数据,数值分析结果表明:逆最优值方法能够确保对标成本实现,给出“任务-人员-时间”指派方案。智能算法求解速度具有显著优势,对于抢修这类时间紧、任务急、情况复杂的问题更加适用。本方法可以应用在更广泛的领域,例如:项目工期进度控制、人力资源绩效管理的标准工时制定、目标管理视角的资源配置决策等多个领域,以预期结果为源动力,同时获得决策参数和方案的一类问题,都可以得到方法启发和思路借鉴。

关键词: 逆最优值模型; 0-1混合整数非线性双层规划;混合遗传-整数线性规划算法;“任务-人员”匹配;时间优化;抢修项目

Abstract: Once major equipment are broken down suddenly, if it cannot be responded in time, the production is suspended, and life cost, environment cost and economy cost are happened. Time-tight, urgent and high complexity are the characteristics of emergency repairs. For such practical problems of emergency repair, based on the above characteristics, aiming at minimizing the direct and indirect losses of emergency repairs, the serial and parallel relationships of logic sequence of critical tasks and non-critical tasks are considered. A forward optimization model for “task-personnel” matching is constructed. However, even if the optimal value of the model is still higher than the benchmark cost value, in order to solve this kind of problem, in view of the adjustable space for completion time, the inverse optimal value model is constructed. It is driven by the benchmark cost value. It is followed the idea of reverse thinking, which is the causes are found out from the results, and the task time is reverse-derived. In view of the inverse optimal value model has the characteristics of 0-1 mixed integer, nonlinear, bi-level and NP-hard, the hybrid “genetic-integer linear planning” algorithm is designed. The parallelization of genetic algorithm with the global search capability of integer linear planning are combined. Relevant data are obtained based on the actual investigation and interview of the relevant experts of the enterprise. The numerical analysis are showed that: the forward optimization method cannot get the benchmark value. Based on inverse optimal value method, both the intelligent algorithm and the exact algorithm can get the benchmark value with different “task-personnel” matching scheme and completion time. It shows that for the cost control, both the combination of personnel assignment scheme and completion time standard should be considered, and only one of them cannot achieve the expected. The inverse competition value method can ensure the realization of the target cost and reduce the cost of the total repair time. The “task-personnel-time” assignment themes are given by the algorithm. Intelligent algorithm solution speed has significant advantages, which is more suitable for such time-critical, urgent task and complex problems as emergency repair. The method proposed in this study can be applied to many fields such as project schedule control, standard working time design of human resource management, resource allocation decision-making from the perspective of target management, etc. It can give inspiration to the problem for inverse inferring decision parameters according to the expected results of decision-making.

Key words: inverse optimal value model; 0-1 mixed integer nonlinear bi-level programming; mixed genetic-integer linear programming algorithm; “task-person” matching; time optimization; emergency repair project

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