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

Junyi Mo, Shunichi Ohmori

研究成果: Article査読

抄録

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.

本文言語English
ページ(範囲)636-645
ページ数10
ジャーナルWSEAS Transactions on Business and Economics
18
DOI
出版ステータスPublished - 2021

ASJC Scopus subject areas

  • 経済学、計量経済学
  • 戦略と経営
  • 組織的行動および人的資源管理
  • マーケティング

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