An Iterated Local Search Algorithm for the Multi-Vehicle Covering Tour Problem
被引:0
作者:
Takada, Yosuke
论文数: 0引用数: 0
h-index: 0
机构:
Nagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, JapanNagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, Japan
Takada, Yosuke
[1
]
Hu, Yannan
论文数: 0引用数: 0
h-index: 0
机构:
Nagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, JapanNagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, Japan
Hu, Yannan
[1
]
论文数: 引用数:
h-index:
机构:
Hashimoto, Hideki
[2
]
Yagiura, Mutsunori
论文数: 0引用数: 0
h-index: 0
机构:
Nagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, JapanNagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, Japan
Yagiura, Mutsunori
[1
]
机构:
[1] Nagoya Univ, Grad Sch Informat Sci, Dept Comp Sci & Math Informat, Nagoya, Aichi, Japan
[2] Tokyo Univ Marine Sci & Technol, Dept Logist & Informat Engn, Tokyo, Japan
来源:
2015 IEEE INTERNATIONAL CONFERENCE ON INDUSTRIAL ENGINEERING AND ENGINEERING MANAGEMENT (IEEM)
|
2015年
关键词:
multi-vehicle covering tour problem;
iterated local search;
dynamic programming;
large-scale instances;
D O I:
暂无
中图分类号:
T [工业技术];
学科分类号:
08 ;
摘要:
Given two sets of vertices V and W, where each vertex in V covers a subset of W, the multi-vehicle covering tour problem asks to determine a number of vehicle routes on a subset of V so as to minimize the total distance under the constraint that every vertex in W must be covered by vertices in the routes. We propose an iterated local search algorithm that features two procedures to improve solutions: one consists of three operations to remove or insert vertices from or into a route; the other is path reconstruction that replaces a partial path with an optimal one, for which we propose a dynamic programming algorithm. We also propose efficient methods to reduce the computation time to search for improved solutions in neighborhoods. The computational results on benchmark instances show that our algorithm performs better than existing methods and is efficient for large-scale instances.