不确定环境下项目型装配系统重调度研究
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摘要
在项目型装配系统中,物料配送不及时、零件返工、工艺变更等不确定因素的发生常常会导致原有计划无法执行。此时需要进行重调度以控制实际生产的进度和状态。针对此问题,以最小化项目成本和最小化重调度前后计划变动为目标,建立了项目型装配系统重调度的整数规划模型,并提出了一种基于帝国竞争机制的双目标算法进行工人配置以及工序开工时间的重调度求解。算法中引入了快速非支配排序方法,以期在一次运行中就得到Pareto近似解集。将文中提出的算法与快速非支配排序遗传算法(NSGA II)以及加强Pareto进化算法(SPEA)进行了比较,结果表明文中算法得到的解要显著地优于NSGA II以及SPEA。最后将文中模型与算法应用于某汽轮机厂的阀门装配系统。
Uncertainty factors such as material delay, part rework and procedure changes occur frequently to invalidate the initial schedule in the project-based assembly system. Rescheduling is then practically mandatory for production control. To solve this problem, an integer programming model with objectives of minimizing costs and reschedule instability was established. A bi-objective imperialist competitive-based algorithm(MOICA) was developed to reallocate workers and update operations′ start-up time. The Fast Nondominated Sorting Approach was introduced to obtain the Pareto approximate solution set in a single run. To evaluated the Proposed algorithm, it was compared with the the Fast Nondominated Sorting Genetic Algorithm(NSGA II) and the Strength Pareto Evolutionary Algorithm and experimental results revealed our algorithm had better performance over the other two. Eventually our model and algorithm were applied to valve assembly instances from steam turbine factory.
Uncertainty factors such as material delay, part rework and procedure changes occur frequently to invalidate the initial schedule in the project-based assembly system. Rescheduling is then practically mandatory for production control. To solve this problem, an integer programming model with objectives of minimizing costs and reschedule instability was established. A bi-objective imperialist competitive-based algorithm(MOICA) was developed to reallocate workers and update operations′ start-up time. The Fast Nondominated Sorting Approach was introduced to obtain the Pareto approximate solution set in a single run. To evaluated the Proposed algorithm, it was compared with the the Fast Nondominated Sorting Genetic Algorithm(NSGA II) and the Strength Pareto Evolutionary Algorithm and experimental results revealed our algorithm had better performance over the other two. Eventually our model and algorithm were applied to valve assembly instances from steam turbine factory.
引文
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[2]Vieira G E,Herrmann J W,Lin E.Rescheduling Manufacturing Systems:A Framework of Strategies,Policies,and Methods[J].Kluwer Academic Publishers,2003,6(1):39-62.
[3]刘乐.单机最大延迟重调度的和声变领域搜索算法[J].计算机集成制造系统,2016,22(8):1977-1991.
[4]张洁,秦威,宋代立.考虑工时不确定的混合流水车间滚动调度方法[J].机械工程学报,2015,51(11):100-108.
[5]Shahrabi J,Adibi M A,Mahootchi M.A reinforcement learning approach to parameter estimation in dynamic job shop scheduling[J].Computers&Industrial Engineering,2017,110:75-82.
[6]Chakrabortty R K,Sarker R A,Essam D L.Multi-mode resource constrained project scheduling under resource disruptions[J].Computers&Chemical Engineering,2016,88:13-29.
[7]Deblaere F,Demeulemeester E,Herroelen W.Reactive scheduling in the multi-mode RCPSP[J].Computers&Operations Research,2011,38(1):63-74.
[8]Sabar M,Montreuil B,Frayret J.An agent-based algorithm for personnel shift-scheduling and rescheduling in flexible assembly lines[J].Journal of Intelligent Manufacturing,2012,23(6):2623-2634.
[9]Abd K,Abhary K,Marian R.Multi-Objective Optimization of Dynamic Scheduling in Robotic Flexible Assembly Cells via Fuzzy-Based Taguchi Approach[J].Computers&Industrial Engineering,2016,99:250-259.
[10]Jung S,Woo Y B,Kim B S.Two-stage assembly scheduling problem for processing products with dynamic component-sizes and a setup time[J].Computers&Industrial Engineering,2017,104:98-113.
[11]Niemi E.Worker allocation in make-to-order assembly cells[J].Robotics and Computer-Integrated Manufacturing,2009,25:932-936.
[12]Atashpaz G,Lucas C.Imperialist competitive algorithm:an algorithm for optimization inspired by imperialistic competition[C]//Proceedings of the 2007 IEEE Congress on Evolutionary Computation,Piscataway,IEEE Press,2007:4661-4667.
[13]Deb K,Pratap A,Agarwal S,et al.Fast and Elitist Multiob jective Genetic Algorithm:NSGA-II[J].IEEE Transactions on Evolutionary Computation,2002,6(2):182-197.
[17]Zitzler E,Thiele L.Multi-objective evolutionary algorithms:a comparative case study and the strength Pareto approach[J].IEEE Transactions on Evolutionary Computation,1999,3(4):257-271.