Truck-Drone Collaborative Delivery Strategies in Rural Last-Mile Logistics: A Constraint Theory and Contingency Theory Review

Main Article Content

Yichen Su
Fuzhou University, Fuzhou, China

DOI: https://doi.org/10.70267/icrafm.202617485492

Keywords

truck-drone collaborative delivery, rural last-mile logistics, constraint theory, contingency theory, two-stage and integrated optimization

Abstract

Compared with urban regions, rural last-mile logistics faces special delivery conditions, including scattered delivery points, sparse road infrastructure and low customer demand density. Currently, truck-drone collaborative delivery has emerged as a promising solution to this issue. But strategy selection across varying rural factors remains unexplored. This review aims to examine how collaborative delivery strategies perform under distinct types of rural bottlenecks through integrating Constraint Theory and Contingency Theory. Existing literature identifies that rural last-mile delivery is primarily confronted with four bottleneck types. They include road-network-driven, demand-driven, structure-driven and hybrid. Using this classification, this study compares the performance of two-stage and integrated optimization methods across categories. Findings reveal that integrated strategies are superior under road-network and structure bottlenecks, whereas two-stage strategies suffice under demand bottlenecks. A bottleneck-oriented strategy selection framework is constructed. The review delivers a theoretical framework by integrating two management theories into collaborative delivery research, practically providing valuable decision-making guidance for logistics enterprises and policy-makers.  

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Published
Oct 20, 2026
Conference Proceedings Volume
Vol. 17 (2026): Proceedings of the International Conference on Research in Accounting, Finance and Management (ICRAFM 2026)
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How to Cite

Su, Yichen. “Truck-Drone Collaborative Delivery Strategies in Rural Last-Mile Logistics: A Constraint Theory and Contingency Theory Review”. 2026. Exploring Science Academic Conference Series, vol. 17, Oct. 2026, pp. 485-92, https://doi.org/10.70267/icrafm.202617485492.