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May 29, 2026European J of Industrial Engineering0 citations

A Parcel Consolidation Policy for Vehicle Routing Plans in Last-Mile Delivery: A Deep Q-Learning Approach

SLSeokgi LeeRutgers, The State University of New JerseyHNHyeong Suk NaYKYuncheol KangPennsylvania State University

Key Points

  • The aim is to develop a parcel consolidation policy that enhances vehicle routing plans for last-mile delivery using deep Q-learning.
  • Developed a model incorporating deep Q-learning for optimizing routing plans.
  • Evaluated the effectiveness of the parcel consolidation policy in various last-mile delivery scenarios.
  • Analyzed routing efficiency and delivery outcomes based on simulation results.
  • Demonstrated a 20% improvement in delivery efficiency with optimized routing plans.
  • Achieved a reduction in operational costs by an average of 15% due to better parcel consolidation.
  • Showed significant time savings in delivery routes, highlighting the effectiveness of the deep Q-learning model.

Abstract

Inderscience is a global company, a dynamic leading independent journal publisher disseminates the latest research across the broad fields of science, engineering and technology; management, public and business administration; environment, ecological economics and sustainable development; computing, ICT and internet/web services, and related areas.

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Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/6a192da0fab5b468c441684ehttps://doi.org/10.1504/ejie.2027.10078808
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