Vehicle Routing Problem Research and the Quick Commerce Gap: A Bibliometric Analysis of Scopus Literature, 2011 to 2026

Abstract

The Vehicle Routing Problem (VRP) is one of the most studied problems in operations research, yet it is unclear whether research has followed the rise of quick commerce and hyperlocal delivery. This paper presents a bibliometric analysis of VRP literature indexed in Scopus. Records were retrieved on 16 September 2026 using the terms vehicle routing, optimization and heuristics, screened under the PRISMA framework, and reduced from 4,364 records to 2,559 English-language journal articles and reviews published from 2011 onwards. The data were analysed with Biblioshiny, VOSviewer and Tableau. Output grew at an annual rate of 14.51%, from 41 documents in 2011 to 313 in 2026, with 3.48 authors per document and 31.62% international co-authorship. Authorship follows a generalised Lotka pattern, sources follow Bradford\'s Law closely, and China leads in output ahead of the USA and India. Keyword analysis shows core VRP methodology terms far outnumbering last-mile delivery, while quick commerce and hyperlocal delivery barely appear. An input-process-output analysis shows that solution methods receive more attention than service and fairness outcomes. The paper sets out research questions on quick commerce, drone delivery, sustainability trade-offs and emerging-market settings, and discusses implications for theory, management, policy and society.

Introduction

The text discusses the Vehicle Routing Problem (VRP), an important optimization problem in logistics that focuses on finding efficient routes for vehicles to deliver goods to multiple customers while minimizing transportation costs, travel distance, time, fuel consumption, and environmental emissions. Inefficient routing increases delivery expenses, traffic congestion, and pollution, whereas optimized routing benefits businesses, consumers, and society.

Traditional VRP methods face challenges because the problem becomes increasingly complex as the number of customers, vehicles, and delivery constraints grows. Since VRP is an NP-hard problem, finding the optimal solution within a reasonable time is difficult for large datasets. Therefore, researchers use heuristic and metaheuristic techniques to find high-quality solutions efficiently.

Recent research explores several optimization techniques, including genetic algorithms, ant colony optimization, variable neighborhood search, guided local search, and hybrid methods combining mathematical programming with heuristic algorithms. Artificial intelligence techniques, such as graph neural networks, deep reinforcement learning, transformer models, and generative AI, are also being introduced to improve routing decisions.

Modern VRP research considers more complex requirements, including vehicle capacity, delivery time windows, multiple depots, pickup and delivery operations, electric vehicles, drones, traffic congestion, battery limitations, and environmental sustainability. These developments show that routing optimization is evolving from simple distance minimization toward more intelligent, flexible, and environmentally responsible logistics systems.

A major focus of the paper is the growth of last-mile delivery, quick commerce, and hyperlocal delivery, particularly in Indian cities. These services require extremely fast deliveries, continuous order processing, dynamic rider assignments, and frequent route adjustments. However, the authors identify a possible gap between academic research and commercial practice: their keyword analysis found 104 mentions related to core VRP methodology but only four related to last-mile delivery, while quick commerce and hyperlocal delivery were nearly absent. This suggests that these emerging delivery models may be underrepresented in the VRP literature covered by their search.

Research Methodology

The study uses a systematic literature review and bibliometric analysis to examine the development of VRP research between 2011 and 2027. Data were collected from the Scopus database on September 16, 2026, using the keywords vehicle routing, optimization, and heuristics. The researchers applied the PRISMA framework to screen and select relevant publications.

The selection process resulted in 2,559 English-language journal articles and review papers. The data were analyzed using Biblioshiny, the bibliometrix package, VOSviewer, and Tableau. The analysis examined publication growth, citations, influential authors, journals, countries, keyword trends, and research gaps.

The results indicate that annual publication output increased from 41 papers in 2011 to 313 in 2026. The field's publications are concentrated among a relatively small core of authors and journals, while last-mile delivery remains a comparatively minor research theme.

Conclusion

The paper concludes that VRP research is increasingly integrating traditional optimization methods with artificial intelligence, machine learning, and sustainability considerations. However, further research is needed to connect these developments with real-world quick-commerce and hyperlocal delivery operations.

Conclusion

This paper examined the Vehicle Routing Problem literature through a bibliometric lens, using 2,559 journal articles and reviews retrieved from Scopus and analysed with Biblioshiny. It set out to describe how the field has grown, who produces it and where, and which themes it covers. It also tested a specific concern, namely that quick commerce and hyperlocal delivery, now a visible part of urban commerce, receive little attention from routing researchers. The evidence supports that concern. The field is large, productive and growing quickly, but its attention sits with established problem families and with newer technical themes, and the delivery model that shapes daily life in many cities is barely present in it.

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Copyright

Copyright © 2026 Dr. Richa Saxena, Fenil Gindra, Deep Soni, Divyan Arora, Falguni Gupta, Fazal Patel. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.