Proactive Depot Discovery: A Generative Framework for Flexible Location-Routing

2025-02-17

AI Summary

This paper addresses the location-routing problem (LRP) without predefined candidate depot locations—a setting where conventional methods suffer from restricted solution spaces and suboptimal solutions due to reliance on fixed depot candidate sets. We propose the first data-driven generative deep reinforcement learning framework for LRP, integrating geographic pattern representation learning with a multivariate Gaussian distribution model to enable both precise coordinate generation and probabilistic sampling for geographically aware, proactive depot placement. The framework performs end-to-end joint optimization of depot location and vehicle routing solely from customer coordinates and demand data. Experimental results demonstrate that our approach significantly reduces total routing cost compared to random search and classical heuristic algorithms, particularly excelling in dynamic logistics scenarios such as emergency medical response and post-disaster relief operations.

Technology Category

• Planning, Routing, and Scheduling: Learning for Planning and Scheduling
• Search and Optimization: Learning to Search
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services

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: direct generation of exact depot locations, and the creation of a multivariate Gaussian distribution for flexible depots sampling. By extracting depots' geographic pattern from customer requests data, our approach can dynamically respond to logistical needs, identifying high-quality depot locations that further reduce total routing costs compared to traditional methods. Extensive experiments demonstrate that, for a same group of customer requests, compared with those depots identified through random attempts, our framework can proactively generate depots that lead to superior solution routes with lower routing cost. The implications of our framework potentially extend into real-world applications, particularly in emergency medical rescue and disaster relief logistics, where rapid establishment and adjustment of depot locations are paramount, showcasing its potential in addressing LRP for dynamic and unpredictable environments.

Problem

• Generates depot locations without predefined candidates
• Reduces total routing costs dynamically
• Applicable in emergency and disaster relief logistics

Innovation

• Generative DRL framework
• Dynamic depot generation
• Customer data-driven logistics