机械工程学报 >

2018 , Vol. 54 >Issue 9: 55 - 63

DOI: https://doi.org/10.3901/JME.2018.09.055

特邀专栏：航天先进制造技术专栏

双资源约束的航天结构件车间生产调度方法

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  • 1. 东华大学机械工程学院 上海 201620;
    2. 上海航天精密机械研究所 上海 201620
杨长祺,男,1974年出生,博士,研究员。主要研究方向为先进制造技术、车间生产控制。E-mail:yang_qi_2000@163.com;吕佑龙:男,1988年出生,博士,讲师。主要研究方向为智能制造中的大数据分析决策方法。E-mail:xsygll1@163.com;金永乔,男,1983年出生,博士。主要研究方向为制造系统建模、生产优化控制。E-mail:kerrking@163.com;张洁,女,1963年出生,博士,教授,博士研究生导师。主要研究方向为智能制造系统与大数据技术、先进制造系统智能优化与调度方法、制造业信息化与智能化关键技术等。E-mail:mezhangjie@dhu.edu.cn

收稿日期: 2017-06-30

  修回日期: 2017-11-22

  网络出版日期: 2018-05-05

基金资助

国家自然科学基金资助项目（U1637211）。

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Scheduling the Production of Aerospace Structural Parts with Dual Resource Constraints

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  • 1. School of Mechanical Engineering, Donghua University, Shanghai 201620;
    2. Shanghai Aerospace Precision Machinery Research Institute, Shanghai 201620

Received date: 2017-06-30

  Revised date: 2017-11-22

  Online published: 2018-05-05

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摘要

针对航天结构件生产调度过程中工序加工时间随操作人员能力级别而变化，关重件和关键工序特定设备和人员要求约束等问题，构建综合考虑关键设备和人员双资源约束的柔性车间作业调度数学模型，解决航天结构件生产设备资源维、人员资源维、工序信息维的描述。为实现双资源约束下的柔性调度，提出嵌套式蚁群-遗传混合算法，针对结构件加工工序设备和人员双资源选择问题，建立资源选择问题与蚁群并行搜索的映射关系，设计蚁群算法实现资源选择；为提高算法搜索性能，利用遗传算法求解当前蚂蚁资源选择下的调度问题，在避免人员资源使用冲突前提下实现设备加工工序的优化排序，使得关键设备利用率最大，总完工时间最短，并通过综合考虑蚁群和遗传算法阶段的求解目标进行蚁群算法信息素的更新，大大提高算法获得较优解的性能。最后，通过航天结构件车间实际案例进行算法测试，测试结果表明，嵌套式蚁群-遗传混合算法能有效地求解双资源约束的航天结构件车间生产调度问题，可降低资源总负荷，提高关键设备利用率，避免人员资源冲突，具有良好的综合调度性能。

关键词： 航天结构件生产; 柔性调度; 双资源约束; 嵌套式蚁群-遗传混合算法

本文引用格式

周亚勤, 杨长祺, 吕佑龙, 金永乔, 张洁 . 双资源约束的航天结构件车间生产调度方法[J]. 机械工程学报, 2018 , 54(9) : 55 -63 . DOI: 10.3901/JME.2018.09.055

Abstract

The production scheduling method of aerospace structural parts with dual resource constraints of critical equipment and operator is investigated, where structural parts' processing time varies with the operator's ability, and the key components and processes have specific equipment and operator requirements constraints. A mathematical model of the flexible job shop scheduling with dual resource constraints (FJSSDR) is constructed to describe the equipment、operator and process information of the parts. A nested ant colony-genetic hybrid algorithm (NACGHA) is designed to solve the FJSSDR problem. Aiming at the dual resource selection problem, the mapping relationship between resource selection problem and ant colony parallel search is structured. Genetic algorithm is used to solve the optimal scheduling problem by taking the selected double resource constraints as the processing constraint information of the job. The equipment processing sequence is optimized with avoiding conflict of key operator resources, where the objectives of fullest utilization of critical equipment and makespan minimization are considered. The ant colony algorithm pheromones are updated by integrating the objective value obtained by ant colony and genetic algorithm to improve the performance. At last, a scheduling case of aerospace structural parts production shop is analyzed. The case study demonstrates that the proposed NACGHA method has good performance on FJSSDR in terms of reducing resource total load, avoiding resource conflict and improving the utilization of key equipment.

Key words： production of aerospace structural parts; flexible scheduling; dual resource constraints; nested ant colony-genetic hybrid algorithm

参考文献

[1] BRUCKER P, SCHLIE R. Job-shop scheduling with multi-purpose machines[J]. Computering, 1990, 45(4):369-375.
[2] 何彦,王乐祥,李育锋,等. 一种面向机械车间柔性工艺路线的加工任务节能调度方法[J]. 机械工程学报, 2016, 52(19):168-179 HE Yan, WANG Lexiang, LI Yufeng, et al. A scheduling method for reducing energy consumption of machining job shops considering the flexible process plan[J]. Journal of Mechanical Engineering, 2016, 52(19):168-179.
[3] 朱传军,邱文,朱孟周,等. 随机工时下的多目标柔性作业车间鲁棒调度问题[J]. 中国机械工程, 2016, 27(12):1667-1672, 1678. ZHU Chuanjun, QIU Wen, ZHU Mengzhou, et al. Multi-objective flexible job shops robust scheduling problem under stochastic processing times[J]. China Mechanical Engineering, 2016, 27(12):1667-1672, 1678.
[4] 汪俊亮,张洁,秦威,等. 加工时间不确定的柔性作业车间鲁棒调度方法[J]. 中国机械工程, 2015, 26(5):627-632. WANG Junliang, ZHANG Jie, QIN Wei, et al. Robust scheduling on flexible job shop with uncertain processing time[J]. China Mechanical Engineering, 2015, 26(5):627-632.
[5] TANG Dunbing, DAI Min. Energy-efficient approach to minimizing the energy consumption in an extended job-shop scheduling problem[J]. Chinese Journal of Mechanical Engineering, 2015, 28(5):1048-1055.
[6] 赵宁,李开典,田青,等. 考虑运输时间柔性作业车间调度问题的快速寻优方法[J]. 计算机集成制造系统, 2015, 21(3):724-732. ZHAO Ning, LI Kaidian, TIAN Qing, et al. Fast optimization approach of flexible job shop scheduling with transport time condieration[J]. Computer Integrated Manufacturing Systems, 2015, 21(3):724-732.
[7] AHMADI E, ZANDIEH M, FARROKH M, et al. A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms[J]. Computers & Operations Research, 2016, 73:56-66.
[8] YI Wenchao, LI Xinyu, PAN Baolin. Solving flexible job shop scheduling using an effective memetic algorithm[J]. International Journal of Computer Applications in Technology, 2016, 53(2):157-163.
[9] LI Xinyu, GAO Liang. An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem[J]. International Journal of Production Economics, 2016, 174:93-110.
[10] GONZÁLEZ M, VELA C, VARELA R. Scatter search with path relinking for the flexible job shop scheduling problem[J]. European Journal of Operational Research, 2015, 245(1):35-45.
[11] 赵诗奎. 基于新型邻域结构的混合算法求解作业车间调度[J]. 机械工程学报, 2016, 52(9):141-150. ZHAO Shikui. A hybrid algorithm with a new neighborhood structure for the job shop scheduling problem[J]. Journal of Mechanical Engineering, 2016, 52(9):141-150.
[12] GAO K Z, SUGANTHAN P N, TASGETIREN M F, et al. Effective ensembles of heuristics for scheduling flexible job shop problem with new job insertion[J]. Computers & Industrial Engineering, 2015, 90(C):107-117.
[13] PÉREZ M A F, RAUPP F M P. A Newton-based heuristic algorithm for multi-objective flexible job-shop scheduling problem[J]. Journal of Intelligent Manufacturing, 2016, 27(2):409-416.
[14] ZHAO Ning, YE Song, LI Kaidian et al. Effective iterated greedy algorithm for flow-shop scheduling problems with time lags[J]. Chinese Journal of Mechanical Engineering 2017, 30(3):652-662
[15] NELSON R T. Labor and machine limited production system[J]. Management Science, 1967, 13(9):648-671.
[16] 李兢尧,孙树栋,黄媛,等. 求解双资源约束车间调度问题的继承式双目标遗传算法[J]. 控制与决策, 2011, 26(12):1761-1767. LI Jingyao, SUN Shudong, HUANG Yuan, et al. Double-objective inherited genetic algorithm for dual resource constrained job shop[J]. Control and Desicison, 2011, 26(12):1761-1767.
[17] LI Jingyao, HUANG Yuan, NIU Xinwei. A branch population genetic algorithm for dual-resource constrained job shop scheduling problem[J]. Computers & Industrial Engineering, 2016, 102:113-131.
[18] 曾强,沈玲,任华,等. 双资源批量生产柔性作业车间多目标调度方法[J]. 计算机工程与应用, 2015, 51(1):250-256. ZENG Qiang, SHEN Ling, REN Hua et al. Multi-objective scheduling method for batch production FJSP with dual-resource[J]. Computer Engineering and Applications, 2015, 51(1):250-256.
[19] 张洁,张朋,刘国宝. 基于两阶段蚁群算法的带非等效并行机的作业车间调度[J]. 机械工程学报,2013,49(6):136-144. ZHANG Jie, ZHANG Peng, LIU Guobao. Two-stage ant colony algorithm based job shop scheduling with unrelated parallel machines[J]. Journal of Mechanical Engineering, 2013, 49(6):136-144.
[20] 周亚勤,李蓓智,杨建国. 考虑批量和辅助时间等生产工况的智能调度方法[J]. 机械工程学报, 2006, 42(1):52-56. ZHOU Yaqin, LI Beizhi, YANG Jianguo. Intelligent scheduling algorithm for problems with batch and cutting time[J]. Chinese Journal of Mechanical Engineering, 2006, 42(1):52-56.
[21] KACEM I, HAMMADI S, BORNE P. Approach by localization and multi objective evolutionary optimization for flexible job-shop scheduling problems[J]. IEEE Transaction Systems,Man,and Cybernetics-Part C,2002, 32(1):1-13.
[22] 张国辉,高亮,李培根,等. 改进遗传算法求解柔性作业车间调度问题[J]. 机械工程学报, 2009, 45(7):145-151. ZHANG Guohui, GAO Liang, LI Peigen, et al. Improved genetic algorithm for the flexible job-shop scheduling problem[J]. Journal of Mechanical Engineering, 2009, 45(7):145-151.
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