Recent developments in the Last-Mile Location-Routing problem

Recent technological advancements have facilitated various initiatives in the last-mile location routing problem (LM-LRP), yet existing LM-LRP literature largely evaluates these initiatives in isolation and lacks a unified framework. This study aims to address this limitation by conducting a systematic literature review of emerging initiatives in LM-LRP to synthesize their technological and operational implications. First, transportation nodes and modes are categorized to show how emerging initiatives change the spatial configuration of logistics facilities and affect sustainability dimensions, while increasing coordination requirements across LM-LRP networks. The literature is then assessed based on key LM-LRP features, including distribution network structure, objective-function design, and last-mile applications. A digital twin perspective is further proposed as a unified decision-support system to connect LM-LRP components with data-driven modeling and simulation approaches. Sustainability trade-offs are subsequently examined across emerging LM-LRP initiatives, showing that their economic, environmental, and social benefits remain context-dependent. Key research gaps are identified, including limited operational realism, fragmented technology integration, and insufficient governance and policy considerations. Based on these findings, a forward-looking framework is proposed to summarize initiative adoption and future development pathways. Finally, this review provides actionable research insights that can facilitate the implementation of emerging initiatives for more efficient last-mile distribution networks.

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Publication Details

Journal
Computers & Industrial Engineering
Published
2026-09-01
DOI
https://doi.org/10.1016/j.cie.2026.112344
Primary Topic
Vehicle Routing Optimization Methods
Type
article
Field-Weighted Citation Impact
0.00

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article

Recent developments in the Last-Mile Location-Routing problem

Michael Kirley, Lele Zhang, Masoud Kahalimoghadam, Russell Thompson
Computers & Industrial Engineering
Vehicle Routing Optimization Methods
article

Recent developments in the Last-Mile Location-Routing problem

Michael Kirley, Lele Zhang, Masoud Kahalimoghadam, Russell Thompson
article en

Abstract

Recent technological advancements have facilitated various initiatives in the last-mile location routing problem (LM-LRP), yet existing LM-LRP literature largely evaluates these initiatives in isolation and lacks a unified framework. This study aims to address this limitation by conducting a systematic literature review of emerging initiatives in LM-LRP to synthesize their technological and operational implications. First, transportation nodes and modes are categorized to show how emerging initiatives change the spatial configuration of logistics facilities and affect sustainability dimensions, while increasing coordination requirements across LM-LRP networks. The literature is then assessed based on key LM-LRP features, including distribution network structure, objective-function design, and last-mile applications. A digital twin perspective is further proposed as a unified decision-support system to connect LM-LRP components with data-driven modeling and simulation approaches. Sustainability trade-offs are subsequently examined across emerging LM-LRP initiatives, showing that their economic, environmental, and social benefits remain context-dependent. Key research gaps are identified, including limited operational realism, fragmented technology integration, and insufficient governance and policy considerations. Based on these findings, a forward-looking framework is proposed to summarize initiative adoption and future development pathways. Finally, this review provides actionable research insights that can facilitate the implementation of emerging initiatives for more efficient last-mile distribution networks.

Computers & Industrial Engineering
The University of Melbourne (AU)
Australian Research Council
Openalex Percentile: Top 11%
Vehicle Routing Optimization Methods
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