Apply information technology for effective vehicle routing management

Abstract

Vehicle Routing is a well-known, NP-Hard problem in Logistics. Many researches with a wide range of algorithms are conducted to solve different variants of Vehicle Routing Problem. Algorithms proposed are ranged from exact solution methods to heuristics and metaheuristics methods. However, there are very few researches conducted using a flexible algorithm and programming style for multiple variants of Vehicle Routing Problem. In this thesis, we propose a different approach to solve the problem. By applying Information Technology, using Genetic Algorithm as the base, Object Oriented Programming as the programming style and Python as the programming language, we are able to build a flexible solver embedded in an application for easier management. The solver compatibles with both single and multiple depot Vehicle Routing Problem. Also, in this thesis, our application supports a large range of input data, in-app route adjustment and real direction gotten from google maps api. Keywords: Multiple depot, Genetic Algorithm, application, Object Oriented Programming, Python