arXiv AI
Decomposability-Guided Cooperative Coevolution for Large-Scale Itinerary Planning
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arXiv AI
Jul 8
Reward-Density Heuristic for Dynamic Multi-Vehicle Routing: Performance and Computational Efficiency
arXiv:2607. 06066v1 Announce Type: new Abstract: The Vehicle Routing Problem (VRP) and its variants represent some of the most practically consequential optimization challenges in modern logistics and urban mobility.
arXiv AI
Jul 20
From Feasibility to Desirability: Plan, Learn, Adapt (PLA) Framework for Personalized On-Device Itinerary Generation
arXiv:2607. 15552v1 Announce Type: cross Abstract: Generating personalized trip itineraries is a complex planning task and involves a tension between hard combinatorial feasibility and soft latent desirability.
arXiv AI
Jul 7
Robust Feasible Route Construction through Collaborative Partition Optimization
arXiv:2607. 03694v1 Announce Type: new Abstract: Large-scale Capacitated Vehicle Routing Problems (CVRPs) are commonly solved by partitioning customers into smaller routing problems that can be optimized independently.
arXiv AI
Oct 28
WCC: A Learning-Based Framework for Deciding When to Apply Cooperative Coevolution in Large-Scale Optimization
WCC is a learning‑based framework that decides dynamically when to apply Cooperative Coevolution (CC) versus Non‑Decomposition Algorithms (NDAs) in large‑scale global optimization. It models this decision as a Markov decision process, using a tailored feature set, reward scheme, and network architecture to enable fine‑grained, adaptive switching and conditional budget allocation without expert input. Experiments show WCC competes strongly and generalizes across problems, horizons, and baseline optimizers, supporting a low‑budget "train‑once and reuse" approach while providing new practical insights into CC deployment.
arXiv AI
Jul 1
Adaptive Cluster-First Route-Second Decomposition for Industrial-Scale Vehicle Routing
arXiv:2606. 31820v1 Announce Type: new Abstract: Large-scale capacitated vehicle routing problems (CVRPs) are commonly addressed using cluster-first route-second (CFRS) approaches that split a routing instance into smaller, computationally tractable subproblems.
arXiv Machine Learning
Aug 11
OD-Gear: Online Decomposition and Group Sampling for Expert-Guided Adversarial Routing in Scalable Capacitated Vehicle Routing
arXiv:2602. 00488v3 Announce Type: replace Abstract: Solving large-scale capacitated vehicle routing problems (CVRP) is hindered by the high complexity of classical heuristics and the limited generalization of neural solvers.