USPR: Learning a Unified Solver for Profiled Routing

• URL: http://arxiv.org/abs/2505.05119v1
• Date: Thu, 08 May 2025 10:42:57 GMT
• Title: USPR: Learning a Unified Solver for Profiled Routing
• Authors: Chuanbo Hua, Federico Berto, Zhikai Zhao, Jiwoo Son, Changhyun Kwon, Jinkyoo Park,
• Abstract summary: Profiled Vehicle Routing Problem (PVRP) incorporates vehicle-client-specific preferences and constraints.<n>Recent reinforcement learning (RL) solvers require retraining for each new profile distribution.<n>We introduce USPR, a novel framework that handles arbitrary profile types.
• Score: 15.136899433821894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The Profiled Vehicle Routing Problem (PVRP) extends the classical VRP by incorporating vehicle-client-specific preferences and constraints, reflecting real-world requirements such as zone restrictions and service-level preferences. While recent reinforcement learning (RL) solvers have shown promise, they require retraining for each new profile distribution, suffer from poor representation ability, and struggle to generalize to out-of-distribution instances. In this paper, we address these limitations by introducing USPR (Unified Solver for Profiled Routing), a novel framework that natively handles arbitrary profile types. USPR introduces three key innovations: (i) Profile Embeddings (PE) to encode any combination of profile types; (ii) Multi-Head Profiled Attention (MHPA), an attention mechanism that models rich interactions between vehicles and clients; (iii) Profile-aware Score Reshaping (PSR), which dynamically adjusts decoder logits using profile scores to improve generalization. Empirical results on diverse PVRP benchmarks demonstrate that USPR achieves state-of-the-art results among learning-based methods while offering significant gains in flexibility and computational efficiency. We make our source code publicly available to foster future research at https://github.com/ai4co/uspr.

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