考虑运输时间的绿色作业车间调度问题研究

doi:10.3969/j.issn.1007-7375.2020.02.006

摘要/Abstract

摘要： 针对制造车间能量消耗大、利用率低的现状，以作业车间为研究对象，建立了一种AGV (automated guided vehicle)运输与加工资源集成调度的模型。首先，通过考虑机器间利用AGV运输工件所需的时间与对应的能耗构建了车间总能耗和完工时间的多目标优化模型，并设置权重系数来调节优化目标的偏好。其次，提出了一种融入模拟退火搜索策略的分布估计算法对模型进行求解。该算法主要是利用分布估计算法全局搜索能力快和模拟退火算法突跳性强的特点构建的一种新的混合算法。同时设计新的退火函数来进一步提高算法深度搜索能力。最后，通过实例验证所提算法的可行性和模型节能的有效性。

关键词: 集成调度, 节能, 作业车间调度, 混合分布估计算法

Abstract: Aiming at the current situation of large energy consumption and low utilization rate in the manufacturing workshop, an integrated scheduling model of AGV (automated guided vehicle) transportation and processing resources is established by taking the workshop as the research object. Firstly, considering the time and energy consumption of AGV transfer between machines, a multi-objection model with the total energy consumption and completion time of the workshop is proposed, and the preference with regard to the optimization goal is adjusted by setting the weight coefficient. Secondly, a distribution estimation algorithm is proposed which integrates the simulated annealing search strategy to solve the model. It mainly uses the global search ability of the distribution estimation algorithm and the strong jump of the simulated annealing algorithm to design a new hybrid algorithm. At the same time, a new annealing function is designed to further improve the depth search ability of the algorithm. Finally, an example is given to verify the feasibility of the proposed algorithm and the effectiveness of model energy saving.

Key words: integrated scheduling, energy saving, job shop scheduling, hybrid distribution estimation algorithm

中图分类号:

TP278

戴敏, 张玉伟, 曾励. 考虑运输时间的绿色作业车间调度问题研究[J]. 工业工程, 2020, 23(2): 41-48.

DAI Min, ZHANG Yuwei, ZENG Li. A Research on Green Job Shop Scheduling Considering Transportation Time[J]. Industrial Engineering Journal, 2020, 23(2): 41-48.

参考文献

[1] U.S. Energy information administration. International Energy Outlook 2016[R/OL]. (2016-05)[2019-12-12]. http://www.eia.gov.
[2] MOUZON G, YILDIRIM M B, TWOMEY J. Operational methods for minimization of energy consumption of manufacturing equipment[J]. International Journal of Production Research, 2007, 45(18-19): 4247-4271
[3] 刘彩洁, 徐志涛, 张钦, 等.分时电价下基于NAGA-Ⅱ的柔性作业车间绿色调度[J/OL].中国机械工程, (2019-5-23)[2019-12-12]. http://kns.cnki.net/kcms/detail/42.1294.th.20190520.1536.004.html.
LIU Caijie, XU Zhitao, ZHANG Qin, et al. Green scheduling of flexible job shop based on NAGA-Ⅱunder TOU power price[J/OL]. China Mechanical Engineer, (2019-5-23)[2019-12-12] http://kns.cnki.net/kcms/detail/42.1294.th.20190520.1536.004.html.
[4] 陈辅斌, 李忠学, 杨喜娟. 基于改进NSGA2算法的多目标柔性作业车间调度[J]. 工业工程, 2018, 21(2): 55-61
CHEN Fubin, LI Zhongxue, YANG Xijuan. Multi-objective flexible job shop scheduling based on improved NSGA2 algorithm[J]. Industrial Engineering Journal, 2018, 21(2): 55-61
[5] DAI M, TANG D B, GIRET A, et al. Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm[J]. Robotics and Computer-Integrated Manufacturing, 2013, 29(5): 418-429
[6] LU C, LI X Y, GAO L, et al. An effective multi-objective discrete virus optimization algorithm for flexible job-shop scheduling problem with controllable processing times[J]. Computers & Industrial Engineering, 2017, 104: 156-174
[7] LIU Y, DONG H B, LOHSE N, et al. A multi-objective genetic algorithm for optimisation of energy consumption and shop floor production performance[J]. International Journal of Production Economics, 2016, 179: 259-272
[8] LU C, GAO L, LI X Y, et al. Energy-efficient permutation flow shop scheduling problem using a hybrid multi-objective backtracking search algorithm[J]. Journal of Cleaner Production, 2017, 144: 228-238
[9] AHMADIZAR F, SHAHMALEKI P. Group-shop scheduling with sequence-dependent set-up and transportation times[J]. Applied Mathematical Modelling, 2014, 38(21-22): 5080-5091
[10] 刘二辉, 姚锡凡, 陶韬, 等. 基于改进花授粉算法的共融AGV作业车间调度[J]. 计算机集成制造系统, 2019, 25(9): 2219-2236
LIU Erhui, YAO Xifan, TAO Tao, et al. Improved flower pollinaton algorithm for job shop scheduling problems integrated with AGVs[J]. Computer Integrated Manufacturing Systems, 2019, 25(9): 2219-2236
[11] LACOMME P, LARABI M, TCHERNEV N. Job-shop based framework for simultaneous scheduling of machines and automated guided vehicles[J]. International Journal of Production Economics, 2013, 143(1): 24-34
[12] 施金良, 刘飞, 许弟建, 等. 数控机床空载运行时节能决策模型及实用方法[J]. 中国机械工程, 2009, 20(11): 1344-1346
SHI Jinliang, LIU Fei, XU Dijian, et al. Decision model and practical method of energy-saving in NC machine tool[J]. China Mechanical Engineering, 2009, 20(11): 1344-1346
[13] 曹华军, 刘飞, 何彦. 机械加工系统节能降噪型综合任务分配模型及其应用[J]. 机械工程学报, 2006, 42(5): 97-102
CAO Huajun, LIU Fei, HE Yan. Integrated task-assigning model of energy saving and noise reduction in the machining systems and its application[J]. Chinese Journal of Mechanical Engineering, 2006, 42(5): 97-102
[14] 王圣尧, 王凌, 许烨, 等. 求解混合流水车间调度问题的分布估计算法[J]. 自动化学报, 2012, 38(3): 437-443
WANG Shengyao, WANG Ling, XU Ye, et al. An estimation of distribution algorithm for solving hybrid flow-shop scheduling problem[J]. Acta Automatica Sinica, 2012, 38(3): 437-443
[15] 黄海松, 刘凯, 初光勇. 改进模拟退火算法在柔性调度中的应用[J]. 组合机床与自动化加工技术, 2018(2): 148-151, 156
HUANG Haisong, LIU Kai, CHU Guangyong. Improved simulated annealing algorithm for low-carbon flexible job shop scheduling[J]. Modular Machine Tool & Automatic Manufacturing Technique, 2018(2): 148-151, 156
[16] 肖世昌, 孙树栋, 杨宏安. 混合分布估计算法求解随机Job shop提前/拖期调度问题[J]. 控制与决策, 2015, 30(10): 1854-1860
XIAO Shichang, SUN Shudong, YANG Hong’an. Hybrid estimation of distribution algorithm for stochastic job shop scheduling with earliness/tardiness penalty[J]. Control and Decision, 2015, 30(10): 1854-1860
[17] TAILLARD E. Benchmarks for basic scheduling problems[J]. European Journal of Operational Research, 1993, 64(2): 278-285
[18] HAO X C, LIN L, GEN M, et al. Effective estimation of distribution algorithm for stochastic job shop scheduling problem[J]. Procedia Computer Science, 2013, 20: 102-107
[19] 王凌. 车间调度及其遗传算法[M]. 北京: 清华大学出版社, 2003.
[20] ABELE E, SIELAFF T, SCHIFFLER A, et al. Analyzing energy consumption of machine tool spindle units and identification of potential for improvements of efficiency[M]. Berlin Heidelberg: Springer, 2011.