基于静态半成套策略的多目标准时化物料配送调度

doi:10.13229/j.cnki.jdxbgxb20200203

摘要/Abstract

摘要：

针对混流装配线的准时化物料配送调度问题，提出了静态半成套供料策略。首先，对基于静态半成套供料策略的物料配送调度问题进行了描述，建立了以最小化总线边库存和总能源消耗为目标的多目标优化模型。然后，提出了一种改进的多目标引力搜索算法，该算法引入混沌引力算子和记忆搜索策略，加快了算法收敛速度并提高了种群多样性，此外建立了局部搜索优化算子对总能耗和总线边库存进行优化。最后，通过实验与标杆算法进行对比，结果验证了本文算法的可行性和有效性。

关键词: 计算机应用, 物料配送, 混流装配线, 准时化, 能源消耗, 多目标优化模型, 引力搜索算法

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

中图分类号:

TP39

周炳海,何朝旭. 基于静态半成套策略的多目标准时化物料配送调度[J]. 吉林大学学报(工学版), 2021, 51(3): 910-916.

Bing-hai ZHOU,Zhao-xu HE. Static semi⁃kitting strategy⁃based multi⁃objective just⁃in⁃time material distribution scheduling[J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(3): 910-916.

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参考文献 11

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	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.
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算法	参数	问题规模
算法	参数	低	中	高
MMCGSA	种群数量	100	100	100
	初始引力常数	1	1	1
	惯性因子	0.4	0.4	0.8
	局部搜索次数	5	5	10
	最大迭代次数	200	250	300
MOPSO	种群数量	100	100	100
	学习因子	0.7	0.7	0.7
	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	200	250	300
NSGA-II	种群数量	100	100	100
	交叉率	0.5	0.55	0.6
	变异率	0.1	0.15	0.2
	最大迭代次数	200	250	300

规模	组别	DPO			ES			IGD
规模	组别	MMCGSA	MOPSO	NSGA-II	MMCGSA	MOPSO	NSGA-II	MMCGSA	MOPSO	NSGA-II
小	1	0.762	0.329	0.684	0.839	0.682	0.725	0.290	0.375	0.318
	2	0.985	0.228	0.093	0.790	1.002	0.823	0.084	0.490	0.678
	3	0.393	0.172	0.949	0.914	1.085	0.686	0.268	0.327	0.321
	4	0.980	0.052	0.451	0.883	1.034	0.791	0.229	0.389	0.448
中	5	0.844	0.060	0.740	0.904	0.966	0.917	0.172	0.401	0.468
	6	0.876	0.102	0.401	0.996	1.226	0.924	0.258	0.650	0.621
	7	0.633	0.044	0.821	0.875	0.944	0.777	0.156	0.416	0.303
	8	0.671	0.096	0.544	0.944	1.266	0.811	0.258	0.356	0.391
大	9	0.883	0.150	0.263	1.020	0.890	0.869	0.055	0.442	0.425
	10	0.915	0.082	0.272	1.007	1.033	0.858	0.051	0.376	0.694
	11	0.674	0.174	0.690	1.001	0.941	1.084	0.125	0.451	0.366
	12	0.760	0.343	0.472	1.084	0.964	0.917	0.112	0.296	0.344
均值		0.781	0.153	0.532	0.938	1.003	0.849	0.172	0.414	0.448