Capacitated Vehicle Routing Problem Using Conventional and Approximation Method

TL;DR

This paper addresses the Capacitated Vehicle Routing Problem by combining clustering with DBSCAN and routing with Christofide's approximation algorithm to generate feasible delivery routes considering multiple constraints.

Contribution

It introduces a hybrid approach integrating clustering and approximation algorithms for solving CVRP, applicable to real-world delivery systems.

Findings

Effective clustering of demand nodes using DBSCAN

Feasible routes generated with Christofide's algorithm

Applicable to real-life delivery scenarios

Abstract

This paper attempts to solve the famous Vehicle Routing Problem by considering multiple constraints including capacitated vehicles, single depot, and distance using two approaches namely, cluster first and route the second algorithm and using integer linear programming. A set of nodes are provided as input to the system and a feasible route is generated as output, giving clusters of nodes and the route to be traveled within the cluster. For clustering the nodes, we have adopted the DBSCAN algorithm, and the routing is done using the approximation algorithm, Christofide's algorithm. The solution generated can be employed for solving real-life situations, like delivery systems consisting of various demand nodes.