GenMRP: A Generative Multi-Route Planning Framework for Efficient and Personalized Real-Time Industrial Navigation

• URL: http://arxiv.org/abs/2602.04174v1
• Date: Wed, 04 Feb 2026 03:21:21 GMT
• Title: GenMRP: A Generative Multi-Route Planning Framework for Efficient and Personalized Real-Time Industrial Navigation
• Authors: Chengzhang Wang, Chao Chen, Jun Tao, Tengfei Liu, He Bai, Song Wang, Longfei Xu, Kaikui Liu, Xiangxiang Chu,
• Abstract summary: We propose GenMRP, a generative framework for multi-route planning.<n>We show that GenMRP achieves state-of-the-art performance with high efficiency in both offline and online environments.<n>GenMRP has been fully deployed in a real-world navigation app, demonstrating its effectiveness and benefits.
• Score: 25.910561524153167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing industrial-scale navigation applications contend with massive road networks, typically employing two main categories of approaches for route planning. The first relies on precomputed road costs for optimal routing and heuristic algorithms for generating alternatives, while the second, generative methods, has recently gained significant attention. However, the former struggles with personalization and route diversity, while the latter fails to meet the efficiency requirements of large-scale real-time scenarios. To address these limitations, we propose GenMRP, a generative framework for multi-route planning. To ensure generation efficiency, GenMRP first introduces a skeleton-to-capillary approach that dynamically constructs a relevant sub-network significantly smaller than the full road network. Within this sub-network, routes are generated iteratively. The first iteration identifies the optimal route, while the subsequent ones generate alternatives that balance quality and diversity using the newly proposed correctional boosting approach. Each iteration incorporates road features, user historical sequences, and previously generated routes into a Link Cost Model to update road costs, followed by route generation using the Dijkstra algorithm. Extensive experiments show that GenMRP achieves state-of-the-art performance with high efficiency in both offline and online environments. To facilitate further research, we have publicly released the training and evaluation dataset. GenMRP has been fully deployed in a real-world navigation app, demonstrating its effectiveness and benefits.

Related papers

• Beyond Shortest Path: Agentic Vehicular Routing with Semantic Context [0.3767731868757604]
  This paper introduces and evaluates PAVe, a hybrid agentic assistant designed to augment classical pathfinding algorithms with contextual reasoning.<n>In a benchmark of realistic urban scenarios, PAVe successfully used complex user intent into appropriate route modifications.<n>We conclude that combining classical routing algorithms with an LLM-based semantic reasoning layer is a robust and effective approach for creating personalized, adaptive, and scalable solutions for urban mobility optimization.
  arXiv  Detail & Related papers  (2025-11-06T15:37:11Z)
• Accelerating Vehicle Routing via AI-Initialized Genetic Algorithms [53.75036695728983]
  Vehicle Routing Problems (VRP) are a fundamental NP-hard challenge in Evolutionary optimization.<n>We introduce an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions.<n>This framework consistently outperforms current state-of-the-art solvers across various time budgets.
  arXiv  Detail & Related papers  (2025-04-08T15:21:01Z)
• SCoTT: Strategic Chain-of-Thought Tasking for Wireless-Aware Robot Navigation in Digital Twins [78.53885607559958]
  We propose SCoTT, a wireless-aware path planning framework.<n>We show that SCoTT achieves path gains within 2% of DP-WA* while consistently generating shorter trajectories.<n>We also show the practical viability of our approach by deploying SCoTT as a ROS node within Gazebo simulations.
  arXiv  Detail & Related papers  (2024-11-27T10:45:49Z)
• DynamicRouteGPT: A Real-Time Multi-Vehicle Dynamic Navigation Framework Based on Large Language Models [13.33340860174857]
  Real-time dynamic path planning in complex traffic environments presents challenges, such as varying traffic volumes and signal wait times. Traditional static routing algorithms like Dijkstra and A* compute shortest paths but often fail under dynamic conditions. This paper proposes a novel approach based on causal inference for real-time dynamic path planning, balancing global and local optimality.
  arXiv  Detail & Related papers  (2024-08-26T11:19:58Z)
• A Bi-Objective Approach to Last-Mile Delivery Routing Considering Driver Preferences [42.16665455951525]
  The Multi-Objective Vehicle Routing Problem (MOVRP) is a complex optimization problem in the transportation and logistics industry. This paper proposes a novel approach to the MOVRP that aims to create routes that consider drivers' and operators' decisions and preferences. We evaluate two approaches to address this objective: visually attractive route planning and data mining of historical driver behavior to plan similar routes.
  arXiv  Detail & Related papers  (2024-05-25T04:25:00Z)
• An Efficient Learning-based Solver Comparable to Metaheuristics for the Capacitated Arc Routing Problem [67.92544792239086]
  We introduce an NN-based solver to significantly narrow the gap with advanced metaheuristics. First, we propose direction-aware facilitating attention model (DaAM) to incorporate directionality into the embedding process. Second, we design a supervised reinforcement learning scheme that involves supervised pre-training to establish a robust initial policy.
  arXiv  Detail & Related papers  (2024-03-11T02:17:42Z)
• Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration [87.53543137162488]
  We propose an easy-to-implement online reinforcement learning (online RL) framework called textttMEX. textttMEX integrates estimation and planning components while balancing exploration exploitation automatically. It can outperform baselines by a stable margin in various MuJoCo environments with sparse rewards.
  arXiv  Detail & Related papers  (2023-05-29T17:25:26Z)
• Continuous Trajectory Generation Based on Two-Stage GAN [50.55181727145379]
  We propose a novel two-stage generative adversarial framework to generate the continuous trajectory on the road network. Specifically, we build the generator under the human mobility hypothesis of the A* algorithm to learn the human mobility behavior. For the discriminator, we combine the sequential reward with the mobility yaw reward to enhance the effectiveness of the generator.
  arXiv  Detail & Related papers  (2023-01-16T09:54:02Z)
• Predicting Drivers' Route Trajectories in Last-Mile Delivery Using A Pair-wise Attention-based Pointer Neural Network [7.595170785628867]
  In last-mile delivery, drivers deviate from planned routes because of their tacit knowledge of the road and curbside infrastructure.<n>Being able to predict the actual stop sequence that a human driver would follow can help to improve route planning in last-mile delivery.<n>This paper proposes a pair-wise attention-based pointer neural network for this prediction task using drivers' historical delivery trajectory data.
  arXiv  Detail & Related papers  (2023-01-10T06:11:20Z)
• Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization [49.207538634692916]
  We propose a new algorithm for circuit routing, named Ranking Cost, to form an efficient and trainable router. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration.
  arXiv  Detail & Related papers  (2021-10-08T07:22:45Z)
• DeepAltTrip: Top-k Alternative Itineraries for Trip Recommendation [4.727697892741763]
  We propose a deep learning-based framework, called DeepAltTrip, that learns to recommend top-k alternative itineraries for given source and destination POIs. For the route generation step, we propose a novel sampling algorithm that can seamlessly handle a wide variety of user-defined constraints.
  arXiv  Detail & Related papers  (2021-09-08T10:36:59Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.