Journal of Computer Applications ›› 2019, Vol. 39 ›› Issue (11): 3383-3390.DOI: 10.11772/j.issn.1001-9081.2019040712

• Frontier & interdisciplinary applications • Previous Articles     Next Articles

Procurement-production-distribution joint scheduling model in job shop environment

ZHANG Weicun, GAO Rui, ZHANG Man   

  1. School of Economics and Management, Hebei University of Technology, Tianjin 300401, China
  • Received:2019-04-26 Revised:2019-07-14 Online:2019-11-10 Published:2019-08-21
  • Supported by:
    This work is partially supported by the National Social Science Foundation of China (17BGL087), the Project of Natural Science Youth Foundation of Colleges and Universities in Hebei Province (2011125).


张维存, 高蕊, 张曼   

  1. 河北工业大学 经济管理学院, 天津 300401
  • 通讯作者: 张维存
  • 作者简介:张维存(1975-),男,河北唐山人,副教授,博士,主要研究方向:智能优化、工业工程;高蕊(1994-),女,山西吕梁人,硕士研究生,主要研究方向:物流与供应链管理;张曼(1993-),女,河北定州人,硕士研究生,主要研究方向:物流与供应链管理。
  • 基金资助:

Abstract: Aiming at the issue that the Integrated Production and Distribution Scheduling (IPDS) model rarely considers the complex production environment and procurement, the model of Integrated Purchase Production and Distribution Scheduling (IPPDS) with minimizing the order completion time in the job shop environment as target was established and the improved Dynamic Artificial Bee Colony (DABC) algorithm was used to solve the model. Based on characteristics of IPPDS, firstly, to realize the matching relationship between task (processing and transportation) and resource (equipment and vehicle), a coding method of two-dimensional real number matrix was adopted. Secondly, the decoding method based on the process was adopted, and the method to satisfy the constraints for different tasks were designed in the decoding process to ensure the feasibility of the decoding method. Finally, the dynamic coordination mechanism and local heuristic information of leading and following bees were designed in the process of the algorithm. Appropriate parameter intervals of DABC were obtained by experiments, and the experimental results show that:compared with piecewise scheduling and IPDS, IPPDS strategy has the scheduling time reduced by 35.59% and 30.95% respectively. DABC algorithm has the solution effect averagely improved by 2.54% compared with Artificial Bee Colony (ABC) algorithm, and averagely improved by 6.99% compared to the Adapted Genetic Algorithm (AGA). Therefore, IPPDS strategy can meet customer requirements more quickly, and DABC algorithm not only reduces the parameters to be set, but also has good exploration and development ability.

Key words: job shop scheduling, vehicle scheduling, joint scheduling, vehicle sharing, Artificial Bee Colony (ABC) algorithm

摘要: 针对生产-配送联合调度(IPDS)模型较少考虑复杂生产环境以及采购环节的问题,建立了在作业车间环境下,以最小化订单完成时间为目标的采购-生产-配送联合调度(IPPDS)模型,并采用改进的动态人工蜂群(DABC)算法进行求解。根据IPPDS问题的特征,首先,采用二维实数矩阵的编码方式,实现任务(加工与运输)与资源(设备与车辆)的匹配关系;其次,采用基于工艺过程的解码方式,并在解码过程中针对不同任务设计了满足约束条件的方法,来保证解码方案的可行性;最后,在算法过程中设计了引领蜂与跟随蜂的动态协调机制和局部启发式信息。通过实验给出DABC适当的参数区间,对比实验结果表明,IPPDS策略相较于分段调度和IPDS策略,调度时间分别缩短了35.59%和30.95%;DABC相较于人工蜂群(ABC)算法求解效果平均提升了2.54%,相对于改进的遗传算法(AGA)求解效果平均提升了6.99%。因此,IPPDS策略能更快速地满足客户需求,而DABC算法既减少需设置的参数,又具有良好的探索和开发能力。

关键词: 作业车间调度, 车辆调度, 联合调度, 车辆共用, 人工蜂群算法

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