Asynchronous Cooperative Optimization of a Capacitated Vehicle Routing Problem Solution

2025-11-16

AI Summary

This paper addresses the Capacitated Vehicle Routing Problem (CVRP) by proposing FILO2$^x$, a parallel, shared-memory asynchronous cooperative optimization framework. Unlike conventional approaches, FILO2$^x$ avoids explicit problem decomposition and global synchronization; instead, multiple solvers concurrently perform local search within a shared solution space, each optimizing distinct solution regions—enabling iteration-level parallelism. Its key innovations are a single-trajectory parallelization design and a lightweight asynchronous coordination mechanism, drastically reducing synchronization overhead. Experimental evaluation on CVRP instances ranging from hundreds to hundreds of thousands of customers demonstrates that FILO2$^x$ achieves substantial speedups over the original FILO2 while maintaining comparable solution quality. Thus, FILO2$^x$ establishes a new, efficient, and scalable paradigm for parallel metaheuristic optimization of large-scale CVRPs.

Technology Category

• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Metareasoning and Metaheuristics

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: The sharing economy

Abstract

We propose a parallel shared-memory schema to cooperatively optimize the solution of a Capacitated Vehicle Routing Problem instance with minimal synchronization effort and without the need for an explicit decomposition. To this end, we design FILO2$^x$ as a single-trajectory parallel adaptation of the FILO2 algorithm originally proposed for extremely large-scale instances and described in Accorsi and Vigo (2024). Using the locality of the FILO2 optimization applications, in FILO2$^x$ several possibly unrelated solution areas are concurrently asynchronously optimized. The overall search trajectory emerges as an iteration-based parallelism obtained by the simultaneous optimization of the same underlying solution performed by several solvers. Despite the high efficiency exhibited by the single-threaded FILO2 algorithm, the computational results show that, by better exploiting the available computing resources, FILO2$^x$ can greatly enhance the resolution time compared to the original approach, still maintaining a similar final solution quality for instances ranging from hundreds to hundreds of thousands customers.

Problem

• Optimizing capacitated vehicle routing solutions asynchronously
• Minimizing synchronization in parallel shared-memory cooperative optimization
• Enhancing resolution time while maintaining solution quality

Innovation

• Parallel shared-memory schema for cooperative optimization
• Single-trajectory parallel adaptation of FILO2 algorithm
• Asynchronous concurrent optimization of unrelated solution areas