To Solve the TDVRPTW via Hadoop MapReduce Parallel Computing

被引：0

作者：

Li, Bo-Yi ^{[1
]}

Wang, Chen-Shu ^{[1
]}

机构：

[1] Natl Taipei Univ Technol, Grad Inst Informat & Finance Management, Taipei, Taiwan

来源：

INTELLIGENT INFORMATION AND DATABASE SYSTEMS (ACIIDS 2017), PT II | 2017年 / 10192卷

关键词：

Hadoop; MapReduce; TDVRPTW; Heuristic algorithm;

D O I：

10.1007/978-3-319-54430-4_6

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The convenience of online shopping has made it common to everyone. With the increase of online transaction, optimization of VRP is an important issue in logistics and transportation. TDVRPTW is a crucial problem which considers a given time window in VRP. This paper targets solving TDVRPTW by using Hadoop MapReduce and compares the effectiveness of Hadoop with a single machine. We used an existing program to cluster the demand nodes and then calculated a route for every cluster by using random method and heuristic algorithm including nearest time window algorithm, nearest neighbor algorithm and 2-opt. After that, we executed parallel computing in Hadoop by implementing program on MapReduce. We used Solomon benchmarking problem as the base of experimental examples and made the experiments. This research proved that Hadoop MapReduce has better efficacy to calculate the best solution than a single machine.

引用

页码：55 / 64

页数：10

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