考虑云制造服务协同的多用户任务调度优化  

Multi-user task scheduling optimization considering cloud manufacturing service collaboration

作　　者：王天日[1] 张敏敏 刘娟 张鹏志[1] WANG Tianri;ZHANG Minmin;LIU Juan;ZHANG Pengzhi(School of Economics and Management,Taiyuan University of Technology,Taiyuan 030024,China;State Key Laboratory of Clean and Efficient Coal Utilization,Taiyuan University of Technology,Taiyuan 030024,China)

机构地区：[1]太原理工大学经济管理学院,山西太原030024 [2]太原理工大学省部共建煤基能源清洁高效利用国家重点实验室,山西太原030024

出　　处：《计算机集成制造系统》2023年第9期3006-3017,共12页Computer Integrated Manufacturing Systems

基　　金：国家自然科学基金资助项目(71701141);教育部人文社会科学研究资助项目(21YJC630135);山西省回国留学人员科研资助项目(2023-070);山西省哲学社会科学规划资助项目(2020YJ035)。

摘　　要：云制造系统中有各种不同的分布式制造服务,而以往对制造服务调度的研究忽略了服务提供企业间的协同效应。为考虑云制造服务社会属性对服务协同的影响,从合作与竞争的角度建立了云服务间协同效应的测度模型。进而,以最大化平均用户满意度及最大化云服务协同效应为优化目标,构建了双目标云服务选择与调度模型。基于灰狼优化算法设计了一种灰狼优化模拟退火混合算法(GWO-SA)对该模型进行优化求解。使用算例进行测试并与其他多目标优化算法比较,仿真结果表明了该模型的有效性和GWO-SA算法的高效性。There are various distributed manufacturing services in cloud manufacturing system.In the previous research of manufacturing service scheduling,the collaboration effect of cloud service enterprises was often ignored.To consider the impact of the social attributes of cloud manufacturing services on the services collaboration,a measurement model of the collaboration effect between cloud services was established from the perspective of cooperation and competition.A bi-objective cloud service selection and scheduling model was constructed to maximize the average user satisfaction degree and the collaborative effect of cloud services.Based on the Grey Wolf Optimizer(GWO),a hybrid algorithm GWO—Simulated Annealing algorithm(GWO-SA)was designed to solve the model.Numerical examples were used to test and compare with other multi-objective optimization algorithms.The simulation results showed the effectiveness of the proposed model and the efficiency of GWO-SA algorithm.

关 键 词：云制造 多用户任务 服务协同 任务调度 灰狼优化算法 

分 类 号：C935[经济管理—管理学]