Vehicle-as-Prompt: A Unified Deep Reinforcement Learning Framework for Heterogeneous Fleet Vehicle Routing Problem
arXiv:2604.05195v1 Announce Type: new Abstract: Unlike traditional homogeneous routing problems, the Heterogeneous Fleet Vehicle Routing Problem (HFVRP) involves heterogeneous fixed costs, variable travel costs, and capacity constraints, rendering solution quality highly sensitive to vehicle selection. Furthermore, real-world logistics applications often impose additional complex constraints, markedly increasing computational complexity. However, most existing Deep Reinforcement Learning (DRL)-based methods are restricted to homogeneous scenarios, leading to suboptimal performance when applied to HFVRP and its complex variants. To bridge this gap, we investigate HFVRP under complex constraints and develop a unified DRL framework capable of solving the problem across various variant settings. We introduce the Vehicle-as-Prompt (VaP) mechanism, which formulates the problem as a single-stage autoregressive decision process. Building on this, we propose VaP-CSMV, a framework featuring a c
arXiv:2604.05195v1 Announce Type: new Abstract: Unlike traditional homogeneous routing problems, the Heterogeneous Fleet Vehicle Routing Problem (HFVRP) involves heterogeneous fixed costs, variable travel costs, and capacity constraints, rendering solution quality highly sensitive to vehicle selection. Furthermore, real-world logistics applications often impose additional complex constraints, markedly increasing computational complexity. However, most existing Deep Reinforcement Learning (DRL)-based methods are restricted to homogeneous scenarios, leading to suboptimal performance when applied to HFVRP and its complex variants. To bridge this gap, we investigate HFVRP under complex constraints and develop a unified DRL framework capable of solving the problem across various variant settings. We introduce the Vehicle-as-Prompt (VaP) mechanism, which formulates the problem as a single-stage autoregressive decision process. Building on this, we propose VaP-CSMV, a framework featuring a cross-semantic encoder and a multi-view decoder that effectively addresses various problem variants and captures the complex mapping relationships between vehicle heterogeneity and customer node attributes. Extensive experimental results demonstrate that VaP-CSMV significantly outperforms existing state-of-the-art DRL-based neural solvers and achieves competitive solution quality compared to traditional heuristic solvers, while reducing inference time to mere seconds. Furthermore, the framework exhibits strong zero-shot generalization capabilities on large-scale and previously unseen problem variants, while ablation studies validate the vital contribution of each component.
Executive Summary
The article addresses the Heterogeneous Fleet Vehicle Routing Problem (HFVRP), a complex logistics challenge where traditional Deep Reinforcement Learning (DRL) methods underperform due to vehicle heterogeneity and intricate constraints. The authors propose a unified DRL framework, Vehicle-as-Prompt (VaP), which reframes the problem as a single-stage autoregressive decision process. Their VaP-CSMV model leverages a cross-semantic encoder and multi-view decoder to handle diverse HFVRP variants, demonstrating superior performance over existing DRL solvers and competitive results against heuristic methods while reducing inference time to seconds. The framework also exhibits robust zero-shot generalization to unseen problem scales and variants, validated through ablation studies.
Key Points
- ▸ HFVRP is computationally complex due to heterogeneous vehicle costs, variable travel costs, and capacity constraints, challenging traditional DRL approaches optimized for homogeneous problems.
- ▸ The Vehicle-as-Prompt (VaP) mechanism reformulates HFVRP as a single-stage autoregressive process, enabling unified treatment of diverse problem variants.
- ▸ VaP-CSMV combines a cross-semantic encoder and multi-view decoder to capture intricate mappings between vehicle heterogeneity and customer attributes, achieving state-of-the-art performance in DRL and competitive results against heuristic solvers.
Merits
Novelty of Vehicle-as-Prompt (VaP) Mechanism
The VaP mechanism introduces a paradigm shift by treating vehicles as prompts within a single-stage decision process, unifying heterogeneous fleet routing problems under a cohesive framework, unlike traditional multi-stage or heuristic approaches.
Computational Efficiency and Scalability
VaP-CSMV achieves inference times of mere seconds, a significant improvement over traditional heuristic solvers, while demonstrating strong zero-shot generalization to large-scale and unseen problem variants, addressing a critical gap in DRL-based logistics optimization.
Modularity and Adaptability
The cross-semantic encoder and multi-view decoder design allows the framework to handle diverse HFVRP variants without task-specific retraining, offering a flexible solution for real-world logistics applications with evolving constraints.
Demerits
Dependency on Problem Formulation
The autoregressive single-stage formulation may limit applicability to problems where multi-stage decision-making is inherently required, potentially constraining the framework's adaptability to certain HFVRP variants.
Generalization Boundaries
While zero-shot generalization is demonstrated, its robustness across highly divergent problem scales or entirely new constraint types (e.g., dynamic time windows, stochastic demands) remains untested, warranting further empirical validation.
Hardware and Implementation Complexity
The advanced architecture, including cross-semantic encoding and multi-view decoding, may impose significant computational and memory demands during training, potentially limiting accessibility for smaller organizations or resource-constrained environments.
Expert Commentary
The Vehicle-as-Prompt framework represents a significant advancement in the application of DRL to complex logistics problems, addressing a longstanding gap in the literature. By unifying heterogeneous fleet routing under a single-stage autoregressive process, the authors have demonstrated a scalable and efficient solution that bridges the divide between DRL-based neural solvers and traditional heuristic methods. The cross-semantic encoder and multi-view decoder architecture is particularly noteworthy, as it captures the nuanced interplay between vehicle heterogeneity and customer attributes—a critical factor in real-world HFVRP. However, the framework's reliance on a single-stage formulation may limit its applicability in scenarios where multi-stage decision-making is unavoidable, such as problems with strict time-dependent constraints. Furthermore, while zero-shot generalization is impressive, its robustness to highly dynamic or stochastic environments remains an open question. Nonetheless, the work sets a new benchmark for DRL-based logistics optimization and paves the way for future research into adaptive, real-time routing systems.
Recommendations
- ✓ Further research should explore the integration of multi-stage decision processes within the VaP framework to extend its applicability to time-dependent or stochastic HFVRP variants.
- ✓ Empirical validation of VaP-CSMV in real-world logistics environments with dynamic constraints (e.g., urban freight delivery) would strengthen its practical relevance and highlight potential deployment challenges.
- ✓ Collaboration with industry partners could facilitate the development of user-friendly interfaces and toolkits, enabling broader adoption of the framework across diverse logistics sectors.
Sources
Original: arXiv - cs.LG