Multiobjective Multiple Neighborhood Search Algorithms for Multiobjective Fleet Size and Mix Location-Routing Problem With Time Windows

被引：25
|
作者：
Wang, Jiahai [1 ,2 ,3 ]
Yuan, Liangsheng [1 ]
Zhang, Zizhen [1 ]
Gao, Shangce [4 ]
Sun, Yuyan [1 ]
Zhou, Yalan [5 ]
机构：
[1] Sun Yat Sen Univ, Dept Comp Sci, Guangzhou 510275, Peoples R China
[2] Sun Yat Sen Univ, Key Lab Machine Intelligence & Adv Comp, Minist Educ, Guangzhou 510275, Peoples R China
[3] Sun Yat Sen Univ, Guangdong Key Lab Big Data Anal & Proc, Guangzhou 510275, Peoples R China
[4] Univ Toyama, Fac Engn, Toyama 9308555, Japan
[5] Guangdong Univ Finance & Econ, Coll Informat, Guangzhou 510320, Peoples R China
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Search problems; Delays; Benchmark testing; Manganese; Optimization; Microsoft Windows; Time factors; Heterogeneous fleet; location-routing problem (LRP) with time windows; multiobjective optimization; multiple neighborhood search (MNS); DATA MINING METHODS; EVOLUTIONARY ALGORITHM; KNOWLEDGE DISCOVERY; OPTIMIZATION PART; LOCAL SEARCH; DECOMPOSITION; SELECTION;
D O I：
10.1109/TSMC.2019.2912194
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This paper introduces a multiobjective fleet size and mix location-routing problem with time windows and designs a set of real-world benchmark instances. Then, two versions of multiobjective multiple neighborhood search algorithms based on decomposition and vector angle are developed for solving the problem. In the proposed algorithms, three different kinds of neighborhood search operators, including general local search, objective-specific local search, and large neighborhood search, are carefully designed and combined in a synergistic manner. The experimental results show the effectiveness of the proposed algorithms. Relationships between different objectives in this multiobjective problem are also discussed.
引用
收藏
页码：2284 / 2298
页数：15
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