Abstract
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.
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Notes
- 1.
http://www.visa.com.tw/aboutvisa/mediacenter/NR_tw_122215.html, Visa’s customer survey of e-commerce.
- 2.
http://www.ithome.com.tw/article/87190, IDC, 2016.
- 3.
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Li, BY., Wang, CS. (2017). To Solve the TDVRPTW via Hadoop MapReduce Parallel Computing. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_6
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DOI: https://doi.org/10.1007/978-3-319-54430-4_6
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