arXiv:2608. 06668v1 Announce Type: new Abstract: As an important component of the supply chain industry, transportation has experienced rapid development in the past decade with the assistance of digital platforms and intelligent algorithms.
By Siliang Lu, Dan Hu, Lili Wu
arXiv:2608. 14140v1 Announce Type: new Abstract: The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth.
By Andrew Soroka, German Mikhelson, Alexander Mescheryakov, Sergey Gerasimov
arXiv:2607. 22356v1 Announce Type: new Abstract: In recent years, the growing complexity of last-mile pickup operations has increased the need for fast and accurate decision-making on logistics platforms.
By Yida Xu, Zhaofang Mao, Yuheng Miao, Jiaxin Zhang, Yiting Sun
arXiv:2609.26275v1 Announce Type: new
Abstract: The vehicle routing problems with real-world constraints (we consider vehicles capacity limits, time windows constrains, pickup-and-delivery multi-depo...
By Andrew Soroka, Alex Meshcheryakov
arXiv:2405. 13947v2 Announce Type: replace Abstract: Deep neural networks based on reinforcement learning (RL) for solving combinatorial optimization (CO) problems are developing rapidly and have shown a tendency to approach or even outperform traditional solvers.
By Chaoyang Wang, Pengzhi Cheng, Jingze Li, Weiwei Sun
The paper introduces an integrated optimization framework that links automated warehouse operations with last‑mile multi‑modal transport for differentiated on‑demand delivery. It employs a deep reinforcement learning approach—MORM‑AGDQN for warehouse scheduling and MRMH‑HCVRP for external routing—to balance service level, cost, and demand. The results demonstrate significant performance gains, including a 100 % on‑time delivery rate, a 29.3 % reduction in average last‑mile delivery time, a 46.4 % cut in total transportation distance, and a high‑priority service rate exceeding 92 % while maintaining cost‑customer satisfaction balance.
By Xiaozhu Sun, Bilal Farooq