4.7 Article

Waiting strategy for the vehicle routing problem with simultaneous pickup and delivery using genetic algorithm

期刊

EXPERT SYSTEMS WITH APPLICATIONS
卷 165, 期 -, 页码 -

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2020.113959

关键词

Vehicle routing problem with simultaneous pickup and delivery; Waiting strategy; Rerouting indicator; Genetic algorithm

资金

  1. Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Science and ICT [NRF-2019R1F1A1061349]
  2. National Research Foundation of Korea [2019R1F1A1061349] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

向作者/读者索取更多资源

With the widespread use of smartphones and the popularity of online shopping, consumers are increasingly shifting towards online purchases, resulting in a growing volume and variety of products being delivered. Companies in the logistics sector need to develop operational strategies to cope with the increasing demand, with a focus on real-world vehicle routing problems involving dynamic orders.
With the development of information and telecommunication technology and the wide adoption of smartphones, consumers gradually change their purchase pattern toward online shopping. They can order products from their smartphones at any moment from any place, and the volume and variety of products delivered to consumers are increasing explosively. Companies in this industry need to set up the operational strategies to accommodate the increasing demand for delivery and return of products, and their focus should be the real-world vehicle routing problems with an additional consideration of the dynamic orders placed over time. This study proposes a waiting strategy for the vehicle routing problem with simultaneous pickup and delivery. This strategy implements an index called the rerouting indicator, which functions as a decision-making threshold to determine the rerouting point for real-time demands. For the most real-world-cases with complex problems, this study proposes a genetic algorithm to solve and validate its accuracy and performance by comparing the computational results. The significance and application of the waiting strategy are validated through several experiments, and the appropriate discretion by a decision maker can demonstrate the value of the proposed strategy.

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