Proactive Depot Discovery: A Generative Framework for Flexible Location-Routing
Site Qu, Guoqiang Hu

TL;DR
This paper introduces a data-driven generative framework using deep reinforcement learning to proactively identify optimal depot locations for the Location-Routing Problem, improving routing efficiency without relying on predefined depot candidates.
Contribution
It presents a novel generative DRL approach that dynamically generates depot locations based on customer data, addressing a key gap in existing heuristic methods for LRP.
Findings
Generated depots lead to lower routing costs compared to random attempts.
The framework adapts to geographic and demand data for improved logistics planning.
Potential applications in emergency response and disaster relief logistics.
Abstract
The Location-Routing Problem (LRP), which combines the challenges of facility (depot) locating and vehicle route planning, is critically constrained by the reliance on predefined depot candidates, limiting the solution space and potentially leading to suboptimal outcomes. Previous research on LRP without predefined depots is scant and predominantly relies on heuristic algorithms that iteratively attempt depot placements across a planar area. Such approaches lack the ability to proactively generate depot locations that meet specific geographic requirements, revealing a notable gap in current research landscape. To bridge this gap, we propose a data-driven generative DRL framework, designed to proactively generate depots for LRP without predefined depot candidates, solely based on customer requests data which include geographic and demand information. It can operate in two distinct modes:…
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Taxonomy
TopicsMobile Agent-Based Network Management · Data Management and Algorithms · Service-Oriented Architecture and Web Services
