20.500.12556/DKUM-97221 Monte Carlo Tree Search improved Genetic Algorithm for unmanned vehicle routing problem with path flexibility With the gradual normalization of the COVID-19, unmanned delivery has gradually become an important contactless distribution method around China. In this paper, we study the routing problem of unmanned vehicles considering path flexibility and the number of traffic lights in the road network to reduce the complexity of road conditions faced by unmanned vehicles as much as possible. We use Monte Carlo Tree Search algorithm to improve the Genetic Algorithm to solve this problem, first use Monte Carlo Tree Search Algorithm to compute the time-saving path between two nodes among multiple feasible paths and then transfer the paths results to Genetic Algorithm to obtain the final sequence of the unmanned vehicles fleet. And the hybrid algorithm was tested on the actual road network data around four hospitals in Beijing. The results showed that compared with normal vehicle routing problem, considering path flexibility can save the delivery time, the more complex the road network composition, the better results could be obtained by the algorithm. unmanned vehicle path flexibility vehicle routing problem Genetic Algorithm GA Monte Carlo Tree Search algorithm MCTS COVID-19 pandemics samovozeča vozila cestni promet fleksibilnost poti stanje na cestah problem usmerjanja vozila genetski algoritem hibridni algoritem pandemija true false true Chair of Production Engineering (CPE), University of Maribor Faculty of Mechanical Engineering Angleški jezik Slovenski jezik https://apem-journal.org/ Content from this work may be used under the terms of the Creative Commons Attribution 4.0 International Licence (CC BY 4.0). Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Članek v reviji 2026-02-24 08:47:01 2026-02-24 08:47:01 2026-02-25 03:27:59 0000-00-00 00:00:00 2022 0 0 str. 425-438 no. 4 Vol. 17 Dec. 2022 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2022-08-15 2022-10-15 2022-12-30 656.1:004.421 269405699 10.14743/apem2022.4.446 1854-6250 APEM17-4_425-438.pdf APEM17-4_425-438.pdf 1 40A4F78B86079F29538C0466982E3159 af757ae7c6d84b830b42ce98a23aefb4ebc451f576770052a1431e7f6f9e38ce 50bcfebd-1155-11f1-a1a6-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203737 https://apem-journal.org/Archives/2022/APEM17-4_425-438.pdf 0 5655cb62-1155-11f1-a1a6-00155d580e1a https://dk.um.si/Dokument.php?lang=slv&id=203738 Fakulteta za strojništvo Advances in production engineering & management 0 0 0