面向需求不确定的混装线物流车辆配送区域划分方法
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摘要
静态的物流车辆调度方案难以适应需求不确定引起的混流装配线物料需求动态变化,导致配送效率低下,甚至生产流程紊乱等问题。针对此问题,提出了一种面向需求不确定的混流装配线物流车辆配送区域划分方法。首先,分析需求不确定对物流车辆配送量的影响,提出了基于信息熵的配送任务复杂性测度方法。其次,在复杂性测度的基础上,建立以物流配送车辆满载率最大、配送任务复杂度最小为目标的调度优化模型。最后,在实例中应用遗传算法对调度优化模型进行求解,得出物流车辆配送区域最优划分方案。结果表明,所提方法能够输出适应需求不确定的物流车辆配送区域划分方案,满足需求变化的同时,保证车辆满载率维持在最高水平,并降低配送任务复杂度,减少配送错误发生率。
It is difficult for static scheduling solutions of logistics vehicles to adapt to the ever-changing material requirements for the mixed-model assembly line caused by uncertain demands, which has led to the low efficiency of distribution and even the production disorder. To this end, a method is set forth for the regional distribution of logistics vehicles in the mixedmodel assembly line geared to uncertain demands. Firstly, the analysis is conducted on the influence of uncertain demands upon the distribution volume of logistics vehicles; the method of complexity measurement of distribution tasks is proposed based on information entropy. Secondly, with the aid of complexity measurement, the optimized scheduling model is set up, which pursues the maximum capacity rate of logistics vehicles and the minimum complexity of distribution tasks. Finally, the genetic algorithm is employed to solve the scheduling model by means of some examples; the optimized scheme of regional distribution of logistics vehicles is obtained. The results show that by means of the proposed method, the reasonable scheme of regional distribution of logistics vehicles geared to uncertain demands is formulated, while ensuring the highest capacity rate of vehicles. It can also reduce the complexity of distribution tasks and the number of incidence of distribution errors.
It is difficult for static scheduling solutions of logistics vehicles to adapt to the ever-changing material requirements for the mixed-model assembly line caused by uncertain demands, which has led to the low efficiency of distribution and even the production disorder. To this end, a method is set forth for the regional distribution of logistics vehicles in the mixedmodel assembly line geared to uncertain demands. Firstly, the analysis is conducted on the influence of uncertain demands upon the distribution volume of logistics vehicles; the method of complexity measurement of distribution tasks is proposed based on information entropy. Secondly, with the aid of complexity measurement, the optimized scheduling model is set up, which pursues the maximum capacity rate of logistics vehicles and the minimum complexity of distribution tasks. Finally, the genetic algorithm is employed to solve the scheduling model by means of some examples; the optimized scheme of regional distribution of logistics vehicles is obtained. The results show that by means of the proposed method, the reasonable scheme of regional distribution of logistics vehicles geared to uncertain demands is formulated, while ensuring the highest capacity rate of vehicles. It can also reduce the complexity of distribution tasks and the number of incidence of distribution errors.
引文
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[2] Boysen N,Emde S,Hoeck M,et al. Part logistics in the automotive industry:decision problems,literature review and research agenda[J]. European Journal of Operational Research,2015,242(1):107-120.
[3]安蔚瑾,郭伟,于鸿彬.混合装配过程物流控制策略和方法的研究[J].机械设计,2008,25(6):11-14.
[4] Jainury S M,Ramli R,Rahman M N A,et al. Integrated set parts supply system in a mixed-model assembly line[J].Computers&Industrial Engineering,2014,75(1):266-273.
[5] Emde S,Boysen N. Optimally routing and scheduling tow trains for JIT-supply of mixed-model assembly lines[J]. European Journal of Operational Research,2012,217(2):287-299.
[6] Emde S,Boysen N. Optimally locating in-house logistics areas to facilitate JIT-supply of mixed-model assembly lines[J].International Journal of Production Economics,2012,135(1):393-402.
[7] Boysen N,Bock S. Scheduling just-in-time part supply for mixed-model assembly lines[J]. European Journal of Operational Research,2011,211(1):15-25.
[8] Alnahhal M,Noche B. Dynamic material flow control in mixed model assembly lines[J]. Computers&Industrial Engineering,2015,85(C):110-119.
[9] Bortolini M,Ferrari E,Gamberi M,et al. New Kanban model for tow-train feeding system design[J]. Assembly Automation,2015,35(1):128-136.
[10] Golz J,Gujjula R,Günther H O,et al. Part feeding at highvariant mixed-model assembly lines[J]. Flexible Services&Manufacturing Journal,2010,24(2):119-141.
[11] Choi W,Lee Y. A dynamic part-feeding system for an automotive assembly line[J]. Computers&Industrial Engineering,2002,43(1):123-134.
[12] Deshmukh A V,Talavage J J,Barash M M. Complexity in manufacturing systems,Part 1:Analysis of static complexity[J]. IIE Transactions,1998,30(7):645-655.
[13] Frizelle G,Woodcock E. Measuring complexity as an aid to developing operational strategy[J]. International Journal of Operations&Production Management,1995,15(5):26-39.
[14] Elmaraghy W H,Urbanic R J. Modelling of manufacturing systems complexity[J]. CIRP Annals-Manufacturing Technology,2003,52(1):363-366.
[15] Efthymiou K,Mourtzis D,Pagoropoulos A,et al. Manufacturing systems complexity analysis methods review[J]. International Journal of Computer Integrated Manufacturing,2016:1-20.
[16]罗宜美,林强,周刚,等.基于当量物流量的设备系统平面布置设计研究[J].机械设计,2000,17(11):46-48.
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