受约束的过道布置问题建模及优化方法

刘俊琦; 张则强; 龚举华; 张裕

doi:10.3969/j.issn.0258-2724.20200803

受约束的过道布置问题建模及优化方法

doi: 10.3969/j.issn.0258-2724.20200803

西南交通大学机械工程学院，四川成都 610031

基金项目: 国家自然科学基金（51205328，51675450）；教育部人文社会科学研究青年基金（18YJC630255）；四川省科技计划（2022YFG0245）

详细信息

作者简介:
刘俊琦（1995—），男，博士研究生，研究方向为设施布局与智能优化，E-mail：junqi5497@163.com

通讯作者:
张则强（1978—），男，教授，研究方向为制造系统与智能优化，E-mail：zzq_22@163.com

中图分类号: TH165；TP301.6
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- 文章访问数: 280
- HTML全文浏览量: 75
- PDF下载量: 30
- 被引次数: 0
出版历程
- 收稿日期: 2020-12-01
- 修回日期: 2021-03-22
- 网络出版日期: 2022-08-13
- 刊出日期: 2021-03-26

Modeling and Optimization Method of Constrained Corridor Allocation Problem

School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China

摘要

摘要:
为了研究过道布置问题中设施关系对布局的影响，首先，考虑定位约束与排序约束，构建过道布置问题混合整数规划模型，并提出一种求解该问题的自适应混合克隆选择算法，在克隆操作之前新增符合受约束过道布置问题特性的2-opt操作，随后对所产生种群中最优个体进行禁忌搜索操作，对其他个体进行变异操作并设置自适应变异概率；然后，对模型进行精确求解以验证模型的正确性且求解结果为算法提供了理论依据；最后，应用所提算法分别对受约束过道布置问题与基本过道布置问题的42 ~ 49规模实例进行测试，并将求解结果与克隆选择算法、遗传算法、分散搜索算法、花授粉算法以及烟花算法进行对比，结果表明：混合克隆选择算法可以达到当前先进算法的求解效果且在算例sko-42-04与算例sko49-03上表现更优.
- 设施布局 /
- 受约束的过道布置问题 /
- 克隆选择算法 /
- 禁忌搜索操作 /
- 自适应变异
Abstract:
In order to study the influence of facility relationship on layout in corridor allocation problem, An integer programming model considering location and relationship constraints is constructed, and a hybrid clonal selection algorithm based on clonal selection algorithm is proposed to solve the problem. Before clonal operation, a new 2-opt operation based on problem characteristics is added. Then, tabu search operation is carried out for the optimal individuals in the generated population, mutation operation is carried out for other individuals and adaptive mutation probability is set. The model is accurately solved to verify the correctness of the model and the solution results provide a theoretical basis for the algorithm. Applying the proposed algorithm to test 42−49 scale examples of constrained corridor allocation problem and basic corridor allocation problem respectively. The results are compared with clonal selection algorithm, genetic Algorithm, Scatter Search, flower pollination algorithm and fireworks algorithm. The results show that the hybrid clone selection algorithm can achieve the solution effect of the current advanced algorithm and perform better in the examples sko-42-04 and sko-49-03.
- facility layout /
- constrained corridor allocation problem /
- clone selection algorithm /
- tabu search operation /
- adaptive mutation

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图 1 过道布置问题距离计算方式

Figure 1. Distance calculation method of CAP

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图 2 基于两种约束的2-opt操作示意

Figure 2. Improved 2-opt operation diagram based on two kinds of constraints

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图 3 ACSATS 流程

Figure 3. Flow chart of ACSATS

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图 4 CSA与ACSATS在9~15规模下问题求解结果箱线图

Figure 4. Box-plot of the solution results of CSA and ACATS in the scale of 9−15

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表 1 参数名称与定义

Table 1. Parameter names and definition

变量	意义
n	问题规模，即布置的设施数目
I	所有设施的集合，I={1, 2, 3, …, n}
i, j, h, k	设施编号，i, j, h∈ I
c_ij	设施 i 与设施 j 之间的物流成本，$\forall $i∈{1,2,…, n−1}，$\forall $j∈ {1,2,…,n−1}
d_ij	设施 i, j 之间物流交互点在 X 轴方向的距离
l_i	设施 i 沿 X 轴边线的长度
w	通道宽度
α_ij	二进制变量，i, j 分配在同一行，且设施 i 被放置在设施 j 的左侧 α_ij= 1，否则 α_ij= 0
q_ij	二进制决策变量，若设施 i 与设施 j 被布置于同一行，则 q_ij= 1，否则 q_ij= 0
β_hi	二进制决策变量，若设施 h 的物料搬运点在设施 i 的左侧，则 β_hi= 1，否则 β_hi= 0
f	设施间的总物流成本

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表 2 CSA与ACSATS算法求解CCAP与CAP的参数

Table 2. Algorithm parameters of CSA and ACSATS in solving CCAP and CAP

问题	算法	n	N	I_{termax_1}	G	α	S/个	搜索次数/次
CCAP	ACSATS	9~15	200	200	0.01	1	20	200
		25	400	600	0.01	1.2	40	400
		30	600	800	0.02	1.6	80	800
		36	800	1000	0.02	1.6	80	800
	CSA	42~49	1000	1000	0.02	1.6	100	800
		9~15	200	200	0.01	1	—	—
		9~15	200	200	0.01	0.8	20	200
CAP	ACSATS	30	600	800	0.02	1.6	80	600
		36	800	1000	0.02	1.6	80	600
		42	1000	1000	0.02	1.6	100	800
		49	1000	1200	0.02	1.8	110	800

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表 3 LINGO精确解与ACSATS算法的计算结果

Table 3. LINGO exact solution and solving results of ACSATS

问题	n	LINGO		ACSATS
问题	n	F₀	t/s	F_min	t/s	g_ap1
S9	9	1372.5	900	1372.5	1.34	0
S9H	9	2946.5	900	2946.5	1.42	0
S10	10	1642.5	900	1642.5	1.77	0
S11	11	4812.5	900	4268.5	2.06	−11.30
Am12 a	12	2269.0	900	1919.0	2.38	−15.43
Am12 b	12	2400.5	900	2101.5	2.45	−12.46
Am13 a	13	3401.5	900	2968.5	2.61	−12.73
Am13 b	13	4291.0	900	3439.0	3.11	−19.86
Am15	15	5146.0	900	3989.0	3.05	−22.48
N-25-05	25	—	900	8408.0	44.78	—
ste-36-05	36	—	900	50210.5	798.67	—
sko-42-05	42	—	900	125647.5	1355.99	—
注：下划线表示LINGO与算法ACSATS的求解结果较优的一方.

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表 4 CSA算法与ACSATS算法对CCAP的求解结果

Table 4. Solving results of CSA and ACSATS for CCAP

问题	CSA		ACSATS
问题	F_CSA	标准偏差	F_ACSATS	标准偏差
S9	1372.5	6.49	1372.5	0
S9H	2946.5	2.63	2946.5	0
S10	1642.5	11.60	1642.5	0
S11	4268.5	22.97	4268.5	0
Am12 a	1919.0	9.51	1919.0	0
Am12 b	2101.5	13.11	2101.5	0
Am13 a	2968.5	15.40	2968.5	0
Am13 b	3457.0	20.29	3439.0	10.68
Am15	3989.0	37.57	3989.0	3.81

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表 5 SS、GA、IDFPA、IFWA与ACSATS在42与49规模问题下测试CAP的求解结果

Table 5. Results of solving CAP with SS, GA, IDFPA, IFWA and ACSATS in 42 and 49 scale instances

算例	SS^[4]		GA^[4]		IDFPA^[11]		IFWA^[9]		ACSATS
算例	f	t/s	f	t/s	f	t/s	f	t/s	f	t/s
42-1	12731.0	1249.04	12731.0	7174.64	12731.0	279.37	12731.0	321.29	12731.0	322.38
42-2	108020.5	1249.04	108049.5	6815.77	108006.5	681.43	108020.5	604.61	108006.5	489.94
42-3	86667.5	956.28	86667.5	6854.25	86644.5	427.61	86655.5	621.45	86644.5	497.01
42-4	68733.0	1141.34	68769.0	6509.35	68708.0	493.77	68722.0	867.15	68701.0	517.67
42-5	124058.5	1236.47	124099.5	7052.59	124017.5	546.72	124017.5	554.07	124017.5	519.47
49-1	20479.0	2683.93	20472.0	14934.38	20470.0	515.02	20470.0	542.13	20470.0	598.67
49-2	208081.0	2733.87	208270.0	14338.86	208068.0	760.20	208078.0	1009.45	208068.0	1007.99
49-3	162196.0	2877.19	162267.0	14351.91	162187.0	822.33	162187.0	1173.59	162183.0	1006.58
49-4	118264.5	3716.16	118311.5	14290.03	118260.5	1809.86	118260.5	1193.88	118260.5	843.93
49-5	332855.0	2596.39	332990.0	14913.18	332836.0	821.67	332811.0	989.91	332836.0	799.05

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