Core Concepts
Efficiently solving the vehicle routing problem by leveraging clustering techniques.
Abstract
The content explores the relationship between Capacitated Vehicle Routing Problem (CVRP) and Constrained Centroid-Based Clustering (CCBC). It delves into theoretical connections, conducts exploratory analysis on small-sized examples, and generalizes the connection. The study highlights how solving a CCBC can lead to optimal solutions for CVRP by selecting centroids effectively.
Les Cahiers du GERAD publication details.
Abstract: Solving VRP efficiently is crucial for delivery management companies.
Introduction: Importance of VRP in various domains, recent involvement of machine learning community.
Literature Review: Overview of VRP variants, operations research techniques, clustering-based approaches.
Problem Statement: Mathematical notation for CVRP and centroid-based clustering.
Exploratory Analysis: Small-sized examples to show the connection between CCBC and CVRP.
Generalization: Formulating a model to find nearest centroids from CCBC that provide optimal CVRP solutions.
Stats
Efficiently solving a vehicle routing problem (VRP) is crucial for delivery management companies.
The paper explores a connection between Capacitated Vehicle Routing Problem (CVRP) and Constrained Centroid-Based Clustering (CCBC).
Reducing CVRP to CCBC can transition from exponential to polynomial complexity using known algorithms like K-means.
Quotes
"Reducing a CVRP to a CCBC is a synonym for transitioning from exponential to polynomial complexity."
"The objective is to design a CVRP solver using a CCBC technique for good quality solutions within reasonable runtime."