Modeling and Evolutionary Optimization for Multi-objective Vehicle Routing Problem with Real-time Traffic Conditions

被引：1

作者：

Xiao, Long ^{[1
,2
]}

Li, Changhe ^{[1
,2
]}

Wang, Junchen ^{[1
,2
]}

Mavrovouniotis, Michalis ^{[3
,4
]}

Yang, Shengxiang ^{[5
]}

Dan, Xiaorong ^{[6
]}

机构：

[1] China Univ Geosci, Sch Automat, Wuhan 430074, Peoples R China

[2] Hubei Key Lab Adv Control & Intelligent Automat C, Wuhan 430074, Peoples R China

[3] Univ Cyprus, KIOS Res & Innovat Ctr Excellence, CY-2109 Nicosia, Cyprus

[4] Univ Cyprus, Dept Elect & Comp Engn, CY-2109 Nicosia, Cyprus

[5] De Montfort Univ, Sch Comp Sci & Informat, Leicester LE1 9BH, Leics, England

[6] State Grid Elect Power Res Inst Wuhan Nanrui Co L, Wuhan 430074, Peoples R China

来源：

ICMLC 2020: 2020 12TH INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND COMPUTING | 2018年

基金：

中国国家自然科学基金; 欧盟地平线“2020”;

关键词：

Vehicle Routing Problem; Local Search; Multi-objective Optimization; Constrained Optimization; ALGORITHM; WINDOWS;

D O I：

10.1145/3383972.3384041

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

The study of the vehicle routing problem (VRP) is of outstanding significance for reducing logistics costs. Currently, there is little VRP considering real-time traffic conditions. In this paper, we propose a more realistic and challenging multi-objective VRP containing real-time traffic conditions. Besides, we also offer an adaptive local search algorithm combined with a dynamic constrained multi-objective evolutionary framework. In the algorithm, we design eight local search operators and select them adaptively to optimize the initial solutions. Experimental results show that our algorithm can obtain an excellent solution that satisfies the constraints of the vehicle routing problem with real-time traffic conditions.

引用

页码：518 / 523

页数：6

共 13 条

[1] HypE: An Algorithm for Fast Hypervolume-Based Many-Objective Optimization [J].

Bader, Johannes ;

Zitzler, Eckart .

EVOLUTIONARY COMPUTATION, 2011, 19 (01) :45-76

[2] An adaptive large neighborhood search heuristic for dynamic vehicle routing problems [J].

Chen, Shifeng ;

Chen, Rong ;

Wang, Gai-Ge ;

Gao, Jian ;

Sangaiah, Arun Kumar .

COMPUTERS & ELECTRICAL ENGINEERING, 2018, 67 :596-607

[3] A fast and elitist multiobjective genetic algorithm: NSGA-II [J].

Deb, K ;

Pratap, A ;

Agarwal, S ;

Meyarivan, T .

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197

[4] Solving the multi-objective Vehicle Routing Problem with Soft Time Windows with the help of bees [J].

Iqbal, Sumaiya ;

Kaykobad, M. ;

Rahman, M. Sohel .

SWARM AND EVOLUTIONARY COMPUTATION, 2015, 24 :50-64

[5]

Li P, 2015, DISCRETE DYN NAT SOC, P1

[6] Applying hybrid meta-heuristics for capacitated vehicle routing problem [J].

Lin, Shih-Wei ;

Lee, Zne-Jung ;

Ying, Kuo-Ching ;

Lee, Chou-Yuan .

EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (02) :1505-1512

[7] An adaptive large neighborhood search heuristic for the vehicle routing problem with time windows and synchronized visits [J].

Liu, Ran ;

Tao, Yangyi ;

Xie, Xiaolei .

COMPUTERS & OPERATIONS RESEARCH, 2019, 101 :250-262

[8]

Mavrovouniotis M, 2012, IEEE C EVOL COMPUTAT

[9] Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows [J].

Pierre, Djamalladine Mahamat ;

Zakaria, Nordin .

APPLIED SOFT COMPUTING, 2017, 52 :863-876

[10] A General Framework of Dynamic Constrained Multiobjective Evolutionary Algorithms for Constrained Optimization [J].

Zeng, Sanyou ;

Jiao, Ruwang ;

Li, Changhe ;

Li, Xi ;

Alkasassbeh, Jawdat S. .

IEEE TRANSACTIONS ON CYBERNETICS, 2017, 47 (09) :2678-2688

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