基于多目标的机器人装配线平衡算法

doi:10.13229/j.cnki.jdxbgxb20190983

Abstract

Abstract:

In order to improve the operation efficiency and the energy efficiency of the assembly line， the energy consumptions during task performing， changeover， stand-by and transportation were considered when balancing the robotic assembly lines. The objective was to minimize the number of workstations and the total energy consumption simultaneously. A mathematical model was first established， and then an improved Multi-objective Evolutionary Algorithm was proposed and a special coding scheme was designed. A constraint-handling technique， an adaptive penalty factor and a problem-based local search strategy were introduced to enhance the performance of the algorithm. Finally， the problems of different scales were optimized and the results were compared with other algorithms. The results show that the proposed algorithm is effective and feasible， and the energy efficiency of robotic assembly lines is improved.

Key words: computer applications, assembly line balancing, robot, energy, multi-objective optimization problem, changeover, multi-objective evolutionary algorithm

CLC Number:

TP29

Bing-hai ZHOU,Qiong WU. Balancing and bi⁃objective optimization of robotic assemble lines[J].Journal of Jilin University(Engineering and Technology Edition), 2021, 51(2): 720-727.

Figures/Tables 6

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算法	参数	小	中	大
AMOEA/D-ISB	种群大小	210	136	91
	进化代数	50	40	30
	向量邻居个数	15	15	14
	交叉概率	0.8	0.8	0.8
	变异概率	0.2	0.2	0.2
	权重更新参数	2	2	2
	局部搜索概率	0.6	0.6	0.6
MO-PSO	粒子群数	210	136	91
	迭代次数	50	40	30
	速度因子	0.1	0.1	0.1
	惯性影子	0.8	0.8	0.8
	交叉概率	0.8	0.8	0.8
	变异概率	0.2	0.2	0.2

问题规模	问题名称	指标	A₀	A₁	A₂	MO-PSO	MOEA/D
小规模	BUXEY (I=29)	IGD	0.1011	1.2960	1.4351	1.2166	3.0587
		HV	0.059	0.055	0.034	0.042	0.028
		N	11	8	9	6	5
		TIME	24.74	20.10	22.66	23.78	19.27
	LUTZ1 (I=32)	IGD	10.1726	12.3665	21.3557	13.5115	47.5169
		HV	0.041	0.022	0.037	0.029	0.006
		N	6	5	4	4	3
		TIME	38.00	34.71	32.32	34.02	30.25
中规模	HAHN (I=53)	IGD	10.0403	17.8439	90.0004	32.4227	100.5772
		HV	0.023	0.012	0.005	0.012	0.001
		N	11	7	8	7	4
		TIME	34.68	28.48	27.27	30.59	24.06
	ARC83 (I=83)	IGD	29.3147	135.052	177.9932	146.5395	514.7043
		HV	0.61	0.24	0.21	0.25	0.16
		N	8	5	6	5	3
		TIME	96.93	61.37	55.52	60.77	46.22
大规模	ARC111 (I=111)	IGD	58.9270	78.6921	137.3280	93.3462	202.0398
		HV	0.022	0.019	0.017	0.017	0.015
		N	10	6	11	7	5
		TIME	79.96	66.86	63.54	68.49	49.23
	BARTHOL2 (I=148)	IGD	8.0221	10.1641	16.3182	12.4029	43.0122
		HV	0.19	0.13	0.08	0.11	0.02
		N	10	6	7	5	4
		TIME	209.47	122.80	115.91	112.94	77.85
均值		IGD	19.4297	42.5692	74.0718	49.9066	151.8182
		HV	0.158	0.080	0.064	0.077	0.038
		N	9	6	8	6	4
		TIME	80.63	55.72	52.87	55.10	41.15

Balancing and bi⁃objective optimization of robotic assemble lines

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