Crowd sourcing dynamic pickup & delivery problem considering task buffering and drivers’ rejection-application of multi-agent reinforcement learning-

Junyi Mo, Shunichi Ohmori

Research output: Contribution to journalArticlepeer-review

Abstract

In the last decade, dynamic and pickup delivery problem with crowd sourcing has been focused on as a means of securing employment opportunities in the field of last mile delivery. However, only a few studies consider both the driver's refusal right and the buffering strategy. This paper aims at improving the performance involving both of the above. We propose a driver-task matching algorithm that complies with the delivery time constraints using multi-agent reinforcement learning. Numerical experiments on the model show that the proposed MARL method could be more effective than the FIFO and the RANK allocation methods.

Original languageEnglish
Pages (from-to)636-645
Number of pages10
JournalWSEAS Transactions on Business and Economics
Volume18
DOIs
Publication statusPublished - 2021

Keywords

  • Crowd Souring
  • Drivers’ Rejection
  • Dynamic Pickup & Delivery Problem
  • Last Mile delivery
  • Multi-agent Reinforcement Learning
  • Task Buffering

ASJC Scopus subject areas

  • Economics and Econometrics
  • Strategy and Management
  • Organizational Behavior and Human Resource Management
  • Marketing

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