面向復雜物流配送場景的車輛路徑規劃多任務輔助進化算法

李堅強; 蔡俊創; 孫濤; 朱慶靈; 林秋鎮

doi:10.16383/j.aas.c230043

面向復雜物流配送場景的車輛路徑規劃多任務輔助進化算法

doi: 10.16383/j.aas.c230043

李堅強^{1, 2,},
蔡俊創^1,,
孫濤^1,,
朱慶靈^1,,
林秋鎮^1,

1.
深圳大學計算機與軟件學院深圳 518060
2.
深圳大學大數據系統計算技術國家工程實驗室深圳 518060

基金項目: 國家自然科學基金 (62325307, 62073225, 62203134, 62376163, 62203308),廣東省自然科學基金 (2023B1515120038, 2019B151502018), 深圳市科技計劃項目(20220809141216003), 深圳大學科學儀器開發項目 (2023YQ019) 資助

詳細信息

作者簡介:
李堅強：深圳大學計算機與軟件學院教授. 2008年獲華南理工大學博士學位. 主要研究方向為嵌入式系統和物聯網. E-mail: lijq@szu.edu.cn

蔡俊創：深圳大學計算機與軟件學院博士研究生. 主要研究方向為進化計算及其在物流領域中的應用. E-mail: caijunchuang2020@email.szu.edu.cn

孫濤：中興通訊股份有限公司工程師. 2022年獲深圳大學碩士學位. 主要研究方向為進化計算和路徑規劃. E-mail: 1910272020@email.szu.edu.cn

朱慶靈：深圳大學計算機與軟件學院博士后. 2021年獲香港城市大學博士學位. 主要研究方向為進化多目標優化和機器學習. E-mail: zhuqingling@szu.edu.cn

林秋鎮：深圳大學計算機與軟件學院副教授. 2014年獲香港城市大學博士學位. 主要研究方向為人工免疫系統,多目標優化和動態系統. 本文通信作者. E-mail: qiuzhlin@szu.edu.cn

計量
- 文章訪問數: 402
- HTML全文瀏覽量: 118
- PDF下載量: 179
- 被引次數: 0
出版歷程
- 收稿日期: 2023-02-10
- 錄用日期: 2023-08-07
- 網絡出版日期: 2024-02-19
- 刊出日期: 2024-03-29

Multitask-based Assisted Evolutionary Algorithm for Vehicle Routing Problems inComplex Logistics Distribution Scenarios

1.
College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060
2.
National Engineering Laboratory for Big Data System Computing Technology, Shenzhen University, Shenzhen 518060

Funds: Supported by National Natural Science Foundation of China(62325307, 62073225, 62203134, 62376163, 62203308), Natural ScienceFoundation of Guangdong Province (2023B1515120038, 2019B151502018), Shenzhen Science and Technology Program (20220809141216003), and the Scientific Instrument Developing Project of Shenzhen University (2023YQ019)

More Information

Author Bio:
LI Jian-Qiang　Professor at the College of Computer Science and Software Engineering, Shenzhen University. He received his Ph.D. degree from South China University of Technology in 2008. His research interest covers embedded systems and internet of things

CAI Jun-Chuang　Ph.D. candidate at the College of Computer Science and Software Engineering, Shenzhen University. His research interest covers evolutionary computation and its applications in the field of logistics

SUN Tao　Engineer at ZTE Corporation. He received his master degree from Shenzhen University in 2022. His research interest covers evolutionary computation and path planning

ZHU Qing-Ling　Postdoctor at the College of Computer Science and Software Engineering, Shenzhen University. He received his Ph.D. degree from the City University of Hong Kong in 2021. His research interest covers evolutionary multiobjective optimization and machine learning

LIN Qiu-Zhen　Associate professor at the College of Computer Science and Software Engineering, Shenzhen University. He received his Ph.D. degree from the City University of Hong Kong in 2014. His research interest covers artificial immune system, multiobjective optimization, and dynamic system. Corresponding author of this paper

摘要

摘要: 在現代社會中, 復雜物流配送場景的車輛路徑規劃問題(Vehicle routing problem, VRP)一般帶有時間窗約束且需要提供同時取送貨的服務. 這種復雜物流配送場景的車輛路徑規劃問題是NP-難問題. 當其規模逐漸增大時, 一般的數學規劃方法難以求解, 通常使用啟發式方法在限定時間內求得較優解. 然而, 傳統的啟發式方法從原大規模問題直接開始搜索, 無法利用先前相關的優化知識, 導致收斂速度較慢. 因此, 提出面向復雜物流配送場景的車輛路徑規劃多任務輔助進化算法(Multitask-based assisted evolutionary algorithm, MBEA), 通過使用遷移優化方法加快算法收斂速度, 其主要思想是通過構造多個簡單且相似的子任務用于輔助優化原大規模問題. 首先從原大規模問題中隨機選擇一部分客戶訂單用于構建多個不同的相似優化子任務, 然后使用進化多任務(Evolutional multitasking, EMT)方法用于生成原大規模問題和優化子任務的候選解. 由于優化子任務相對簡單且與原大規模問題相似, 其搜索得到的路徑特征可以通過任務之間的知識遷移輔助優化原大規模問題, 從而加快其求解速度. 最后, 提出的算法在京東物流公司快遞取送貨數據集上進行驗證, 其路徑規劃效果優于當前最新提出的路徑規劃算法.
- 車輛路徑規劃問題 /
- 時間窗約束 /
- 同時取送貨 /
- 進化算法 /
- 遷移優化
Abstract: In complex logistics, addressing the vehicle routing problem (VRP) with simultaneous pickup and delivery and time windows, an NP-hard problem, becomes increasingly challenging as the scale expands. Traditional heuristic methods, often unable to leverage prior optimization knowledge, result in slow convergence. To address this, we introduce a multitask-based evolutionary algorithm (MBEA), which assists the optimization of the original large-scale problem by constructing multiple simple and similar subtasks and utilizing transfer learning to accelerate convergence speed. First, a subset of orders is randomly selected from the original problem to construct various subtasks, and then a multitask evolutionary approach is applied to generate candidate solutions for the original problem and subtasks. Given that the subtasks are simpler yet similar to the original problem, useful routing traits can be shared through knowledge transfer among the tasks, thereby speeding up its evolutionary search. To validate MBEA＇s efficacy, empirical studies were conducted on a large-scale express dataset from Jingdong, and the results demonstrate that MBEA outperforms recently proposed vehicle routing algorithms.
- Vehicle routing problem (VRP) /
- time windows constraint /
- simultaneous pickup and delivery /
- evolutionary algorithm /
- transfer optimization

HTML全文

圖 1 VRPPDT模型

Fig. 1 The model of the VRPPDT

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圖 2 MBEA總體框架圖

Fig. 2 The overall framework diagram of MBEA

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圖 3 一個個體的編碼方法

Fig. 3 The coding method of an individual

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圖 4 子任務生成及解碼過程

Fig. 4 The generation and decoding process of the subtask

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圖 5 一個解的切分過程

Fig. 5 The splitting process of a solution

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圖 6 基于路徑的交叉過程

Fig. 6 The operation process of the route-based crossover

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圖 7 順序交叉操作過程

Fig. 7 The operation process of the order crossover

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圖 8 本文提出的方法和對比算法的平均搜索收斂軌跡

Fig. 8 Averaged search convergence traces of the proposed method and the compared algorithms

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表 1 京東數據集的特性

Table 1 Properties of Jingdong dataset

問題	\|V\|	C	J	$ {u_1} $	$ {u _2} $
F201 ~ F204	200	2.5	500	300	0.014
F401 ~ F404	400	2.5	500	300	0.014
F601 ~ F604	600	2.5	500	300	0.014
F801 ~ F804	800	2.5	500	300	0.014
F1001 ~ F1004	1 000	2.5	500	300	0.014

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表 2 MBEA算法參數設置

Table 2 Parameter settings in MBEA

參數	含義	值
Evaluation	算法總評價次數	18 000
TE	每階段的評價次數	3 600
N	種群大小	36
N_re	階段數	5
N_be	保留個體的數量	18
k	子任務個數	1
lower	子任務維度最低占比	0.7

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表 3 MBEA和5種對比算法在京東數據集對比實驗結果

Table 3 Comparative experimental results of MBEA and five compared algorithms on Jingdong dataset

問題	MBEA				EMA				MATE				CCMO				GVNS				VNSME
問題	NV	TD	TC	運行時間 (s)	NV	TD	TC	運行時間 (s)	NV	TD	TC	運行時間 (s)	NV	TD	TC	運行時間 (s)	NV	TD	TC	運行時間 (s)	NV	TD	TC	運行時間 (s)
F201	43	53 851	66 751	3 291	45	54 918	68 418	4 252	42	53 997	66 597	8 712	51	66 099	81 399	2 976	52	84 808	100 408	81	50	60 494	75 494	3
F202	44	53 155	66 355	3 270	47	56 288	70 388	4 340	43	53 649	66 609	14 194	52	63 782	79 382	2 839	53	67 756	83 656	252	49	59 728	74 428	2
F203	43	54 899	67 679	3 356	46	59 009	72 809	5 416	42	54 544	67 024	13 635	49	67 608	82 308	2 881	51	83 079	98 379	103	51	65 951	81 251	2
F204	43	53 311	66 211	2 983	46	56 456	70 256	3 986	43	54 398	67 238	9 929	48	62 329	76 729	2 970	52	74 571	90 171	300	51	60 415	75 715	2
F401	81	99 380	123 620	11 538	93	120 041	147 941	2 567	84	109 863	135 123	16 852	94	124 412	152 612	8 580	98	144 757	174 157	1 229	96	112 942	141 742	15
F402	84	103 091	128 351	13 338	101	122 636	152 936	2 535	87	113 871	139 971	10 742	100	130 655	160 655	7 923	101	160 822	191 122	268	98	117 970	147 370	12
F403	80	98 175	122 055	12 119	95	122 289	150 789	2 731	84	109 212	134 412	10 154	97	123 599	152 699	8 364	98	160 018	189 418	420	93	111 171	139 071	15
F404	83	99 809	124 649	12 656	95	116 269	144 769	2 694	86	110 555	136 295	13 709	100	127 209	157 209	8 154	101	136 483	166 783	651	94	110 775	138 975	17
F601	118	149 868	185 148	15 779	153	202 915	248 816	2 663	126	174 424	212 344	17 795	148	192 176	236 576	15 623	144	240 941	284 141	702	138	171 997	213 397	41
F602	121	153 129	189 429	19 571	164	204 772	253 972	2 656	129	177 851	216 551	18 839	146	199 278	243 078	15 505	141	227 723	270 023	1 624	143	175 068	217 968	49
F603	120	153 681	189 741	16 090	151	202 985	248 285	2 922	128	176 806	215 146	17 636	151	198 996	244 296	15 032	143	219 879	262 779	395	142	171 057	213 657	37
F604	122	153 477	190 137	18 569	157	204 541	251 641	2 886	128	176 943	215 403	18 789	154	196 028	242 228	15 201	145	204 293	247 793	757	141	172 956	215 256	33
F801	159	175 009	222 709	11 565	200	244 506	304 506	3 679	164	196 076	245 156	20 421	200	234 549	294 549	25 467	189	278 179	334 879	1 654	178	189 502	242 902	82
F802	157	173 598	220 577	13 077	210	226 736	289 736	3 657	164	194 325	243 465	20 835	199	236 794	296 494	25 879	184	271 798	326 998	1 153	179	192 243	245 943	107
F803	159	173 474	221 173	14 682	206	240 358	302 158	3 355	165	195 539	244 919	24 212	201	236 025	296 325	25 387	186	231 297	287 097	1 130	180	188 245	242 245	71
F804	156	171 956	218 756	12 743	213	227 247	291 147	3 324	161	191 853	240 033	21 884	198	226 353	285 753	25 707	181	231 743	286 043	1 490	174	186 214	238 414	81
F1001	212	265 385	329 044	9 698	275	363 035	445 535	3 874	222	293 298	359 838	25 957	279	364 136	447 836	34 957	239	391 293	462 993	1 236	232	278 192	347 792	154
F1002	211	264 034	327 213	8 655	279	356 200	439 900	3 858	225	291 180	358 740	27 482	284	354 899	440 099	34 582	240	352 092	424 092	2 847	234	278 465	348 665	126
F1003	212	265 409	329 008	8 910	275	358 768	441 268	3 917	227	295 806	363 786	26 217	283	359 276	444 176	33 748	243	408 770	481 670	554	231	274 553	343 853	126
F1004	212	262 117	325 656	10 331	285	362 496	447 996	3 914	223	289 035	355 815	26 180	289	360 481	447 181	33 515	234	348 460	418 660	890	233	276 896	346 796	123
最佳/ 全部			18/20				0/20				2/20				0/20				0/20				0/20

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表 4 RBX和OX的消融實驗結果

Table 4 Ablation experiment results of RBX and OX

問題	RBX	OX	RBX + OX
F201	66 517	67 966	66 751
F202	66 365	67 744	66 355
F203	68 948	71 718	67 679
F204	66 970	69 372	66 211
F401	124 851	148 685	123 620
F402	128 798	146 954	128 351
F403	123 550	149 781	122 055
F404	125 247	155 403	124 649
F601	187 048	246 299	185 148
F602	192 623	253 252	189 429
F603	193 915	247 466	189 741
F604	193 410	245 400	190 137
F801	224 758	298 889	222 709
F802	228 345	296 430	220 577
F803	226 138	302 783	221 173
F804	220 988	294 788	218 756
F1001	342 544	442 939	329 044
F1002	339 143	440 007	327 213
F1003	341 946	446 173	329 008
F1004	336 077	445 518	325 656
最佳/全部	1/20	0/20	19/20

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表 5 MBEA中參數lower的敏感性分析

Table 5 Sensitivity analysis of lower in MBEA

問題	TC (0.7)	TC (0.5)	TC (0.6)	TC (0.8)	TC (0.9)
F201	66 751	66 664	66 926	66 895	66 971
F202	66 355	66 587	66 597	66 677	66 751
F203	67 679	68 206	67 919	68 027	67 783
F204	66 211	66 215	65 920	66 209	66 150
F401	123 620	123 826	123 103	122 861	122 672
F402	128 351	127 154	127 696	127 771	127 986
F403	122 055	122 428	122 703	122 343	122 270
F404	124 649	124 771	125 365	125 138	124 936
F601	185 148	185 831	186 163	185 579	186 371
F602	189 429	190 317	190 661	190 363	190 731
F603	189 741	189 160	189 740	189 986	189 683
F604	190 137	189 300	189 128	189 743	188 712
F801	222 709	221 057	221 905	221 221	220 910
F802	220 577	221 957	220 655	219 998	220 951
F803	221 173	222 172	222 227	221 495	221 377
F804	218 756	218 002	216 628	217 680	218 297
F1001	329 044	330 379	330 059	330 929	329 632
F1002	327 213	327 346	327 565	326 923	327 199
F1003	329 008	329 596	326 035	328 369	327 482
F1004	325 656	325 790	325 812	326 352	327 106
最佳/全部	9/20	3/20	3/20	2/20	3/20

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表 6 MBEA中參數k的敏感性分析

Table 6 Sensitivity analysis of parameter k in MBEA

問題	TC (1)	TC (0)	TC (2)	TC (3)
F201	66 751	67 985	66 298	66 731
F202	66 355	67 920	67 002	67 111
F203	67 679	68 180	67 917	67 680
F204	66 211	66 621	66 524	65 730
F401	123 620	124 871	124 825	123 839
F402	128 351	127 377	128 292	127 603
F403	122 055	123 494	122 305	121 883
F404	124 649	126 338	124 959	125 486
F601	185 148	187 055	185 004	187 381
F602	189 429	192 343	192 178	189 516
F603	189 741	190 448	190 047	190 610
F604	190 137	191 208	189 940	189 096
F801	222 709	223 158	223 670	224 248
F802	220 577	222 758	223 576	225 523
F803	221 173	222 456	223 684	223 939
F804	218 756	218 393	217 485	220 373
F1001	329 044	330 374	331 387	332 643
F1002	327 213	329 829	329 525	326 603
F1003	329 008	331 877	330 099	329 200
F1004	325 656	330 725	326 082	331 858
最佳/全部	12/20	1/20	3/20	4/20

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