Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (3): 910-916.doi: 10.13229/j.cnki.jdxbgxb20200203

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Static semi⁃kitting strategy⁃based multi⁃objective just⁃in⁃time material distribution scheduling

Bing-hai ZHOU(),Zhao-xu HE   

  1. College of Mechanical Engineering,Tongji University,Shanghai 201804,China
  • Received:2020-04-01 Online:2021-05-01 Published:2021-05-07

Abstract:

In order to solve the Just-in-Time (JIT) material distribution scheduling problem for mixed-model assembly lines, a static semi-kitting strategy is presented. First, the material distribution scheduling problem based on the static semi-kitting strategy is described and a multi-objective optimization model is established to minimize the Total Energy Cost (TEC) and the Total Line-side Inventory (TLI). Then, an improved multi-objective gravitational search algorithm is proposed. The chaotic gravitation operator and the memory search strategy are introduced to the proposed algorithm to accelerate the convergence speed and increase the population diversity. In addition, a local search optimization operator is constructed to optimize the TEC and the TLI. Finally, the experimental results prove the feasibility and effectiveness of the proposed algorithm by comparing with benchmark algorithms.

Key words: computer application, material distribution, mixed-model assembly lines, Just-in-Time, energy cost, multi-objective optimization model, gravitational search algorithm

CLC Number: 

  • TP39

Fig.1

Layout of static semi-kitting strategy system"

Fig.2

Coding scheme of particle in MMCGSA"

Fig.3

Flowchart of the MMCGSA algorithm"

Table 1

Parameters of algorithms"

算法参数问题规模

MMCGSA

种群数量100100100
初始引力常数111
惯性因子0.40.40.8
局部搜索次数5510
最大迭代次数200250300
MOPSO种群数量100100100
学习因子0.70.70.7
惯性权重范围[0.2,0.65][0.2,0.8][0.2,0.9]
速度范围[-0.1,0.1][-0.1,0.1][-0.1,0.1]
最大迭代次数200250300
NSGA-II种群数量100100100
交叉率0.50.550.6
变异率0.10.150.2
最大迭代次数200250300

Table 2

Comparison results of different algorithms"

规模组别DPOESIGD
MMCGSAMOPSONSGA-IIMMCGSAMOPSONSGA-IIMMCGSAMOPSONSGA-II
10.7620.3290.6840.8390.6820.7250.2900.3750.318
20.9850.2280.0930.7901.0020.8230.0840.4900.678
30.3930.1720.9490.9141.0850.6860.2680.3270.321
40.9800.0520.4510.8831.0340.7910.2290.3890.448
50.8440.0600.7400.9040.9660.9170.1720.4010.468
60.8760.1020.4010.9961.2260.9240.2580.6500.621
70.6330.0440.8210.8750.9440.7770.1560.4160.303
80.6710.0960.5440.9441.2660.8110.2580.3560.391
90.8830.1500.2631.0200.8900.8690.0550.4420.425
100.9150.0820.2721.0071.0330.8580.0510.3760.694
110.6740.1740.6901.0010.9411.0840.1250.4510.366
120.7600.3430.4721.0840.9640.9170.1120.2960.344
均值0.7810.1530.5320.9381.0030.8490.1720.4140.448

Fig.4

Impact of station and AGVcapacity on objectives"

Fig.5

Pareto front of MMCGSA andbenchmark algorithms"

1 Lee S, Prabhu V V. Just-in-time delivery for green fleets: a feedback control approach[J]. Transportation Research Part D: Transport and Environment, 2016, 46:229-245.
2 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.
3 Qiu L, Wang J, Chen W, et al. Heterogeneous AGV routing problem considering energy consumption[C]∥IEEE Int Conf on Robotics and Biomimetics, Zhuhai, China, 2015:1894-1899.
4 Zhou B, Shen C. Multi-objective optimization of material delivery for mixed model assembly lines with energy consideration[J]. Journal of Cleaner Production, 2018, 192:293-305.
5 Briand C, He Y, Ngueveu S U. Energy‑efficient planning for supplying assembly lines with vehicles[J]. EURO Journal on Transportation and Logistics, 2018, 7(4):387-414.
6 Mohammed A, Bernd N. A genetic algorithm for supermarket location problem[J]. Assembly Automation, 2015, 35(1):122-127.
7 周炳海,彭涛. 混流装配线准时化物料配送调度优化[J]. 吉林大学学报:工学版,2017,47(4):1253-1261.
Zhou Bing-hai, Peng Tao. Optimal schedule of just-in-time part distribution for mixed-model assembly lines[J]. Journal of Jilin University (Engineering and Technology Edition), 2017, 47(4):1253-1261.
8 Emde S, Fliedner M, Boysen N. Optimally loading tow trains for just-in-time supply of mixed-model assembly lines[J]. IIE Transactions, 2012, 44(2):121-135.
9 Emde S, Gendreau M. Scheduling in-house transport vehicles to feed parts to automotive assembly lines[J]. European Journal of Operational Research, 2017, 260(1):255-267.
10 王真,王晨曦,王宇豪,等. 车间多载自动导引车绿色物流调度[J]. 重庆大学学报,2020,43(1):44-52.
Wang Zhen, Wang Chen-xi, Wang Yu-hao, et al. On multi-load AGV green logistics scheduling in knitting workshop[J]. Journal of Chongqing University, 2020, 43(1):44-52.
11 Rashedi E, Nezamabadi-Pour H, Saryazdi S. GSA:a gravitational search algorithm[J]. Information Sciences, 2009,179(13): 2232-2248.
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