基于改进NSGA-Ⅱ算法的协同制造任务调度研究
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摘要
针对网络协同制造环境下子任务在企业间的任务调度问题,充分考虑协同制造环境下各子任务并行与串行并存的复杂时序关系,制造企业在空间上异构以及生产目标要求多样的特征,在免除调查用户偏好过程的基础上,构建了网络协同制造任务调度的多目标优化模型,改进了NSGA-Ⅱ算法,采用了合理有效的编解码方式、遗传算子和精英策略,在保证种群多样性的同时提高了运算速度,最终得到包含多个解的Pareto最优解集供用户选择。通过算例仿真验证了其有效性,发现其能够很好地解决协同制造系统的任务调度问题。
Research on task scheduling of subtasks among enterprises under network collaborative manufacturing environment. Considering the new features of network collaborative manufacturing system,such as: subtasks parallel and serial co-exist,the cost and time of the material during transportation and Diversity of production objectivest. Exempting survey user preference process,a multi-objective optimization model of task scheduling in network collaborative manufacturing is constructed. Improved NSGA-Ⅱ algorithm and using reasonable and efficient codec,genetic operators and elite strategies,while ensuring the diversity of the population,it improves the speed of operation. Finally the set of pareto optimum solutions was obtained. The validity of the example was verified by simulation. It is found that it is a good solution to the collaborative manufacturing system task scheduling problem.
Research on task scheduling of subtasks among enterprises under network collaborative manufacturing environment. Considering the new features of network collaborative manufacturing system,such as: subtasks parallel and serial co-exist,the cost and time of the material during transportation and Diversity of production objectivest. Exempting survey user preference process,a multi-objective optimization model of task scheduling in network collaborative manufacturing is constructed. Improved NSGA-Ⅱ algorithm and using reasonable and efficient codec,genetic operators and elite strategies,while ensuring the diversity of the population,it improves the speed of operation. Finally the set of pareto optimum solutions was obtained. The validity of the example was verified by simulation. It is found that it is a good solution to the collaborative manufacturing system task scheduling problem.
引文
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[16]王海鑫,王祖和,温国锋,等.自适应粒子群算法求解资源受限多项目调度问题[J].管理工程学报,2017,31(4):220-225.
[17]陶莎,盛昭瀚,徐峰.项目调度与多尺度资源配置的集成优化[J].管理工程学报,2018,32(1):204-211.
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[20]方伯芃,孙林夫.不确定环境下的产业链生产与配送协同调度优化[J].计算机集成制造系统,2018,24(1):224-244.
[2]程八一,黄小曼,杨艳艳,等.差异分批模式下的联合成本优化问题及算法[J].管理科学学报,2016,19(8):102-112.
[3]Klemmt A,Weigert G,Almeder C,et al.A comparison of MIP-based decomposition techniques and VNS approaches for batch scheduling problems[C]Simulation Conference.IEEE,2009:1686-1694.
[4]Varela M L R,Ribeiro R A.Distributed manufacturing scheduling based on a dynamic multi-criteria decision model[J]Recent Developments and New Directions in Soft Computing.Springer International Publishing,2014:81-93.
[5]Mo Nch L.Heuristics to minimize total weighted tardiness of jobs on unrelated parallel machines[C]IEEE International Conference on Automation Science and Engineering.IEEE,2008:572-577.
[6]徐文忠,彭志平,左敬龙.基于遗传算法的云计算资源调度策略研究[J].计算机测量与控制,2015,23(5):1653-1656.
[7]刘明周,张玺,刘从虎,等.不确定环境下再制造加工车间生产调度优化方法[J].机械工程学报,2014,50(10):206-212.
[8]王建华,黄贤凤,梅强,等.离散可调度时段下多供应商敏捷供应链静态调度优化[J].计算机集成制造系统,2015,21(10):2739-2745.
[9]Yazgan H R.Genetic algorithm parameter optimisation using taguchi method for a flexible manufacturing system scheduling problem[J].International Journal of Production Research,2015,53(3):897-915.
[10]郑晓龙,王凌,王圣尧.求解置换流水线调度问题的混合离散果蝇算法[J].控制理论与应用,2014,31(2):159-164.
[11]李劲,李洪,徐丽丽,等.基于改进遗传算法的置换装配线调度问题研究[J].中国管理科学,2016,24(12):63-71.
[12]陈可嘉,周晓敏.多目标置换流水车间调度的改进食物链算法[J].中国机械工程,2015,26(3):348-353.
[13]陈鸿海,蒋增强,左乐,等.基于近亲变异NSGA-Ⅱ算法的多目标柔性作业车间调度[J].农业机械学报,2015,46(4):344-350.
[14]张智勇,吴波,杨叔子.网络协同制造系统的构想与实现[J].中国科学基金,2003,17(1):16-20.
[15]于静,徐哲,谢芳.活动重叠模式与资源约束下的项目调度优化[J].管理科学学报,2017,20(9):36-45.
[16]王海鑫,王祖和,温国锋,等.自适应粒子群算法求解资源受限多项目调度问题[J].管理工程学报,2017,31(4):220-225.
[17]陶莎,盛昭瀚,徐峰.项目调度与多尺度资源配置的集成优化[J].管理工程学报,2018,32(1):204-211.
[18]黄健仓.建设企业多项目管理中的资源调度问题研究[J].中国软科学,2016,1(1):176-183.
[19]王艳婷,何正文,刘人境.随机工期下反应性多模式项目调度优化[J].系统管理学报,2017,26(1):85-93.
[20]方伯芃,孙林夫.不确定环境下的产业链生产与配送协同调度优化[J].计算机集成制造系统,2018,24(1):224-244.
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