A Comprehensive Survey of Methods and Challenges of Vehicle Routing Problem with Uncertainties

Meraryslan Meraliyev, Cemil Turan (), Shirali Kadyrov and Ualikhan Sadyk
Additional contact information
Meraryslan Meraliyev: Department of Computer Science, School of Information Technology and Applied Mathematics, SDU University, Abylaikhan 1/1, Kaskelen 040900, Almaty Region, Kazakhstan
Cemil Turan: Department of Computer Science, School of Information Technology and Applied Mathematics, SDU University, Abylaikhan 1/1, Kaskelen 040900, Almaty Region, Kazakhstan
Shirali Kadyrov: Department of General Education, New Uzbekistan University, Movarounnahra 1, Tashkent 100125, Uzbekistan
Ualikhan Sadyk: Department of Computer Science, School of Information Technology and Applied Mathematics, SDU University, Abylaikhan 1/1, Kaskelen 040900, Almaty Region, Kazakhstan

Mathematics, 2025, vol. 13, issue 23, 1-32

Abstract: This paper presents a comprehensive survey of the methodologies and challenges associated with the Vehicle Routing Problem (VRP), focusing on the uncertainties that impact routing decisions in real-world logistics and transportation scenarios. Traditional VRP models often assume static and deterministic conditions, which do not fully capture the complexities of actual logistics operations. This paper categorizes uncertainties into demand variability, travel-time fluctuations, and other dynamic factors, such as service-time variability and vehicle breakdowns. It reviews various approaches to addressing these uncertainties, including dynamic VRP models and the application of reinforcement learning in stochastic environments. The research methodology includes a systematic review of articles published in recent years, emphasizing influential research at the intersection of VRP and uncertainty. The findings highlight the importance of bridging theoretical advances with practical applications to enhance the robustness and adaptability of VRP solutions. The paper concludes by advocating for continued research in this area to improve operational efficiency and service reliability in logistics.

Keywords: vehicle routing problem; uncertainty; dynamic routing; demand variability; travel time variability; reinforcement learning; transportation management (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/23/3782/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/23/3782/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:23:p:3782-:d:1802481

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().