Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling
Abstract On-demand food delivery services are a rapidly expanding sector within the logistics industry, yet optimizing delivery routes in real-time remains a significant challenge, particularly in high-demand and complex environments. This gap hinders operational efficiency and customer satisfaction...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-10371-w |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849387825200365568 |
|---|---|
| author | Li Liu Shikun Chen Huan Jin Xiaoying Deng Yangguang Liu Yang Lin |
| author_facet | Li Liu Shikun Chen Huan Jin Xiaoying Deng Yangguang Liu Yang Lin |
| author_sort | Li Liu |
| collection | DOAJ |
| description | Abstract On-demand food delivery services are a rapidly expanding sector within the logistics industry, yet optimizing delivery routes in real-time remains a significant challenge, particularly in high-demand and complex environments. This gap hinders operational efficiency and customer satisfaction, highlighting the need for advanced decision-making frameworks. In response, we propose a multi-agent system (MAS) using the Belief-Desire-Intention (BDI) framework to enhance delivery efficiency. Our dynamic model simulates interactions between platforms, riders, and shops, utilizing Monte Carlo Tree Search (MCTS) and Insertion Heuristic methodologies to optimize routes. Through simulations of varying complexity, we demonstrate that MCTS outperforms the Insertion Heuristic, especially in complex scenarios, by effectively managing multiple objectives and maintaining high service quality. These results indicate that advanced intention scheduling methods like MCTS can significantly improve real-time decision-making, thereby enhancing both customer satisfaction and operational efficiency in high-demand delivery contexts. |
| format | Article |
| id | doaj-art-249a80ed60a84499ae62c5b09e993e12 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-249a80ed60a84499ae62c5b09e993e122025-08-20T03:42:28ZengNature PortfolioScientific Reports2045-23222025-07-0115111810.1038/s41598-025-10371-wOptimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search schedulingLi Liu0Shikun Chen1Huan Jin2Xiaoying Deng3Yangguang Liu4Yang Lin5College of Digital Technology and Engineering, Ningbo University of Finance and EconomicsCollege of Finance and Information, Ningbo University of Finance and EconomicsSchool of Computer Science, University of Nottingham Ningbo ChinaCollege of Finance and Information, Ningbo University of Finance and EconomicsCollege of Finance and Information, Ningbo University of Finance and EconomicsNingbo Development Planning Research InstituteAbstract On-demand food delivery services are a rapidly expanding sector within the logistics industry, yet optimizing delivery routes in real-time remains a significant challenge, particularly in high-demand and complex environments. This gap hinders operational efficiency and customer satisfaction, highlighting the need for advanced decision-making frameworks. In response, we propose a multi-agent system (MAS) using the Belief-Desire-Intention (BDI) framework to enhance delivery efficiency. Our dynamic model simulates interactions between platforms, riders, and shops, utilizing Monte Carlo Tree Search (MCTS) and Insertion Heuristic methodologies to optimize routes. Through simulations of varying complexity, we demonstrate that MCTS outperforms the Insertion Heuristic, especially in complex scenarios, by effectively managing multiple objectives and maintaining high service quality. These results indicate that advanced intention scheduling methods like MCTS can significantly improve real-time decision-making, thereby enhancing both customer satisfaction and operational efficiency in high-demand delivery contexts.https://doi.org/10.1038/s41598-025-10371-wMulti-agent systemBelief-desire-intentionMonte Carlo tree searchOn-demand food deliveryRoute optimization |
| spellingShingle | Li Liu Shikun Chen Huan Jin Xiaoying Deng Yangguang Liu Yang Lin Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling Scientific Reports Multi-agent system Belief-desire-intention Monte Carlo tree search On-demand food delivery Route optimization |
| title | Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling |
| title_full | Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling |
| title_fullStr | Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling |
| title_full_unstemmed | Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling |
| title_short | Optimizing on-demand food delivery with BDI-based multi-agent systems and Monte Carlo tree search scheduling |
| title_sort | optimizing on demand food delivery with bdi based multi agent systems and monte carlo tree search scheduling |
| topic | Multi-agent system Belief-desire-intention Monte Carlo tree search On-demand food delivery Route optimization |
| url | https://doi.org/10.1038/s41598-025-10371-w |
| work_keys_str_mv | AT liliu optimizingondemandfooddeliverywithbdibasedmultiagentsystemsandmontecarlotreesearchscheduling AT shikunchen optimizingondemandfooddeliverywithbdibasedmultiagentsystemsandmontecarlotreesearchscheduling AT huanjin optimizingondemandfooddeliverywithbdibasedmultiagentsystemsandmontecarlotreesearchscheduling AT xiaoyingdeng optimizingondemandfooddeliverywithbdibasedmultiagentsystemsandmontecarlotreesearchscheduling AT yangguangliu optimizingondemandfooddeliverywithbdibasedmultiagentsystemsandmontecarlotreesearchscheduling AT yanglin optimizingondemandfooddeliverywithbdibasedmultiagentsystemsandmontecarlotreesearchscheduling |