基于双模式更新五行环算法的多目标冷链配送

任静; 项月; 刘漫丹

doi:10.14135/j.cnki.1006-3080.20211030001

基于双模式更新五行环算法的多目标冷链配送

doi: 10.14135/j.cnki.1006-3080.20211030001

华东理工大学信息科学与工程学院, 上海200237

详细信息

作者简介:
任静：作者简介：任静（1995—），女，河北人，硕士生，主要研究方向为智能优化算法与路径优化。E-mail：13955409207@139.com

通讯作者:
刘漫丹， E-mail：liumandan@ecust.edu.cn

中图分类号: TP273
计量
- 文章访问数: 9
- HTML全文浏览量: 8
- PDF下载量: 1
- 被引次数: 0
出版历程
- 收稿日期: 2021-10-30
- 录用日期: 2022-03-16
- 网络出版日期: 2022-04-12

Multi-Objective Cold Chain Distribution Based on Dual-Mode Updated Five-Element Cycle Algorithm

School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

摘要

摘要: 针对生产运输中广泛存在的冷链配送问题，建立了以配送成本最小化和顾客满意度最大化为目标函数的多目标冷链物流优化模型。基于五行环优化 (Five-Elements Cycle Optimization, FECO) 算法，提出了双模式更新个体的五行环优化算法(Five-Elements Cycle Optimization Algorithm of Dual-Mode Updating Individuals, FECO-DMUI)，并对多目标冷链物流模型进行求解。将FECO-DMUI算法与FECO算法、NSGA-II算法、鲸鱼优化算法和灰狼优化算法进行比较，结果验证了模型与算法的有效性，同时验证了FECO-DMUI算法在多目标冷链配送问题中能更加高效地获得路径优化的最优解集。
- 双模式 /
- 多目标优化 /
- 冷链物流 /
- 路径优化 /
- FECO算法
Abstract: With the development of the logistics industry, cold chain logistics have been studied by more and more scholarsas an important branch of the logistics industry. Because the waste of resources in cold chain logistics and distribution is a problem that cannot be underestimated, we use the optimization algorithm to solve the multi-objective optimization model to provide an effective distribution plan for solving the problem of resource waste in this paper. We establish a multi-objective cold chain logistics optimization model with minimizing distribution costs and maximizing customer satisfaction as the objective function in this paper. Customer satisfaction is reflected by the relationship between the delivery vehicle’s arrival time at the customer’s point and the customer’s specific time window; delivery costs are composed of transportation costs, cargo damage costs, cooling costs, and time penalty costs. We adopt the improved five-elements cycle optimization (FECO), which is the five-elements cycle optimization algorithm of dual-mode updating individuals (FECO-DMUI) for multi-objective cold chain logistics optimization model in this paper. The chain logistics optimization model is solved by FECO-DMUI algorithm and compared with FECO algorithm, NSGA-II, whale optimization algorithm and gray wolf optimization algorithm. The effectiveness of the model and algorithm is verified through specific examples, and the FECO-DMUI algorithm can be used to obtain the optimal solution set for path optimization more efficiently in the multi-objective cold chain distribution problem.
- dual mode /
- multi-objective optimization /
- cold chain logistics /
- route optimization /
- FECO algorithm

HTML全文

图 1 顾客满意度分析

Figure 1. Customer satisfaction analysis

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图 2 五行元素相生相克关系

Figure 2. Five elements complement each other

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图 3 编码与解码操作

Figure 3. Encoding and decoding operations

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图 4 交叉操作

Figure 4. Cross operation

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图 5 变异操作

Figure 5. Mutation operation

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图 6 个体的修复

Figure 6. Individual repair

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图 7 FECO-DMUI算法流程图

Figure 7. Algorithm flowchart of FECO-DMUI

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图 8 客户点分布图

Figure 8. Customer point distribution map

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图 9 路径规划图

Figure 9. Path planning diagrams

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图 10 最优解集分布图

Figure 10. Optimal solution set distribution graph

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表 1 客户点信息

Table 1. Customer point information

Number	Coordinate/km	Demand/kg	Time window/min
0	(35,35) (41,49) (35,17) (55,45) (55,20) (15,30) (25,30) (20,50) (10,43) (55,60) (30,60) (20,65) (50,35)	0	(0,0,1000,1000)
1		10	(15,56,100,120)
2		7	(2,70,120,200)
3		13	(14,64,150,200)
4		19	(18,160,340,450)
5		26	(0,42,100,138)
6		3	(5,76,180,240)
7		5	(62,150,285,320)
8		9	(7,89,200,250)
9		16	(35,115,200,260)
10		16	(21,110,190,250)
11		12	(62,120,200,250)
12		19	(50,180,250,382)
13	(50,25)	23	(21,140,270,350)
14	(15,10)	20	(0,80,160,230)
15	(30,5)	8	(0,48,100,150)
16	(10,20)	19	(8,48,150,180)
17	(5,30)	2	(32,100,200,350)
18	(20,40)	12	(6,65,156,218)
19	(15,60)	17	(0,40,120,200)
20	(45,65)	9	(11,89,200,290)
21	(45,20)	11	(15,75,150,208)
22	(45,10)	18	(7,65,200,320)
23	(55,5)	29	(2,38,168,238)
24	(65,35)	3	(18,98,172,210)
25	(65,20)	6	(72,190,300,382)

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表 2 优化模型参数取值

Table 2. Optimize model parameter values

$ Q $	$ h $	$ e $	$ {\mu _1} $	$ {\mu _2} $	$ s $	$ p $	$ {c_1} $	$ {c_2} $	$ \alpha $	$ \beta $
95	2	4.68	0.5	1	1.7	350	0.5	1	0.04	0.03

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表 3 交叉概率比较实验

Table 3. Crossover probability comparison experiment

Pc	HV
0.3	3.3872×10²⁰
0.4	3.9753×10²⁰
0.5	3.8035×10²⁰
0.6	3.2997×10²⁰
0.7	3.5151×10²⁰
0.8	3.5615×10²⁰
0.9	3.5504×10²⁰

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表 4 变异概率比较实验

Table 4. Mutation probability comparison experiment

$ {p_m} $	HV
0.1	$ 3.7487 \times {10^{20}} $
0.2	$ 3.9753 \times {10^{20}} $
0.3	$ 2.9106 \times {10^{20}} $
0.4	$ 2.8600 \times {10^{20}} $
0.5	$ 1.5053 \times {10^{20}} $

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表 5 尺度因子比较实验

Table 5. Scale factor comparison experiment

$ {p_s} $	HV
0.2	$ 1.9892 \times {10^{19}} $
0.4	$ 3.2433 \times {10^{19}}$
0.6	$ 1.1541 \times {10^{20}} $
0.8	$ 1.7562 \times {10^{20}} $
1.0	$ 3.9753 \times {10^{20}} $
1.2	$ 3.8134 \times {10^{20}} $
1.4	$ 3.8636 \times {10^{20}} $
1.6	$ 3.0664 \times {10^{20}} $
1.8	$ 3.8636 \times {10^{20}} $

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表 6 给定概率比较实验

Table 6. Comparison experiment with given probability

$ {p_n} $	HV
0.1	$ 1.9569 \times {10^{20}} $
0.2	$ 2.1576 \times {10^{20}} $
0.3	$ 2.2579 \times {10^{20}} $
0.4	$ 2.2579 \times {10^{20}} $
0.5	$ 3.7130\times {10^{20}} $
0.6	$ 2.3583 \times {10^{20}} $
0.7	$ 3.1171 \times {10^{20}} $
0.8	$ 3.8636 \times {10^{20}} $
0.9	$ 3.9753 \times {10^{20}} $
1.0	$ 2.3583 \times {10^{20}} $

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表 7 5种算法的对比结果

Table 7. Comparison results of five algorithms

Algorithm	HV	Minimal delivery cost/CNY	Maximum customer satisfaction	Average minimum delivery cost/CNY	Average maximum customer satisfaction
FECO-DMUI	3.9753x10²⁰	39861	0.9446	41082	0.9262
NSGA-II	1.7402x10²⁰	36170	0.8467	41947	0.8157
FECO	7.4377x10¹⁹	44970	0.8397	44720	0.8570
GWO	2.2985x10²⁰	41330	0.9343	42271	0.8928
WOA	1.6919x10²⁰	43740	0.9043	43867	0.8542

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参考文献(23)

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