考虑人员技能匹配的多资源可重入混合流水车间调度优化研究

Manufacturing industry is one of core industries in China, where shop scheduling is a key factor influencing productivity in manufacturing. Under the existing scale in the current manufacturing environment, to improve productivity is an urgent problem to be solved for China's enterprise management. Reentrant job operations, scarce production resources, and personnel qualification requirements, make practical production issues more complex. This project aims to study hybrid flow shop scheduling problem with the consideration of personnel qualification matching, multi-resource satisfaction and reentrant job operation. By appropriately coordinating multi-resources and skilled workers matching decisions, and determining job operations’ schedules, we target at increasing the productivity of enterprises. Our research may include: (1) Taking into consideration of reentrant job operations, multi-resource coordination, and personnel qualification matching, we will establish an integer programming formulation for the studied hybrid flow shop scheduling problem, with the purpose of maximizing the production efficiency; (2) Based on the analysis of problem structures and optimal solution properties, we will devise a branch-and-bound algorithm and several heuristics to efficiently solve practical problem instances; (3) We will establish a more complete mathematical model, by further considering personnel absence, workload balance, equipment maintenance, and production space limit, and no-wait constraints, and then we will develop corresponding heuristic algorithms. We will apply our solution methods to practice in manufacturing enterprises, and further validate and improve our model and algorithms. We expect to publish our project results on some well-known international journals, and to formulate enterprise scheduling software and patents. This project aims to expand the application fields of operations research, to promote the development of optimization algorithms, and to assist enterprises making production decisions.

制造业是我国经济支柱产业，车间调度是影响制造业生产率的关键因素。在现有企业生产规模下如何提高生产率是我国企业管理中亟待解决的问题。可重入工件加工、生产资源约束以及人员资质要求，使实际生产问题更为复杂。本项目拟研究考虑人员技能匹配的多资源可重入混合流水车间调度问题，通过协调资源和人员分配决策、优化生产排序，提高企业生产效率。研究内容有：（1）全面考虑工件可重入、多资源和人员技能匹配，建立混合流水车间调度整数规划模型，最大化生产效率；（2）分析问题和最优解结构性质，据此设计分支定界算法和启发式算法，以快速求解实际问题；（3）建立更完备的数学模型，逐步考虑人员缺勤、负载均衡、设备维护、生产场地限制、无等待条件，并开发启发式算法。再通过企业实践完善模型并测试改进算法。预期在国际主流期刊发表成果并形成企业调度软件和专利。本项目旨在拓展运筹学研究领域、促进优化算法发展、辅助企业生产决策。

制造业是中国经济的第一大产业，车间调度是影响制造业生产率的关键因素。在现有企业生产规模下如何提高生产率是我国企业管理中亟待解决的问题。可重入工件加工、生产资源约束以及人员资质要求，使实际生产问题更为复杂。本项目以考虑人员技能匹配的多资源可重入混合流水车间调度问题为研究对象，通过协调资源和人员分配决策、优化生产排序，提高企业生产效率。研究内容有：i）考虑人员技能匹配的可重入多资源flow shop问题研究；ii）不确定性的可重入多资源flow shop问题研究；iii）可重入多资源flow shop问题优化算法研究。.全面考虑工件可重入、多资源和人员技能匹配，建立混合流水车间调度整数规划模型。在分析问题和最优解结构性质的基础上，提出了求解所研究问题的高效算法，以快速求解实际问题。通过对基准实例和大量随机生成算例的测试，验证了模型和算法的准确性和有效性。此外，进一步考虑生产加工环境和约束，建立更完备的数学模型，考虑人员缺勤、负载均衡、设备维护、生产场地限制、无等待条件，并开发相应的算法。在科学基金支持下，项目组已在Omega、IEEE Transactions on Intelligent Transportation Systems、International Journal of Production Research、Computers & Operations Research、Computers & Industrial Engineering、International Journal of Production Economics、Journal of Combinatorial Optimization、Journal of Industrial Information Integration、Asia-Pacific Journal of Operational Research等领域内重要期刊上发表学术论文24篇，在领域内权威国际会议上发表学术论文17篇，完成了项目预定的目标与计划。