Genetic Algorithm based Solution Model for Multi-Depot Vehicle Routing Problem with Time Windows

Abstract

Abstract-We present a novel Ordered Distance Vector (ODV) based Equi-begin with Variable-diversity (EV) Technique for the exact solution of a variation of the vehicle routing problem with time windows in which the transportation fleet is made by vehicles with different capacities and fixed costs, based on different depots. The MultiDepot Vehicle Routing Problem with Time Windows (MDVRPTW) is a generalization of the standard Vehicle Routing Problem (VRP). The VRPTW is NP-Complete. The MDVRPTW problem is addressed using an efficient Genetic Algorithm (GA). Genetic algorithm is a powerful optimization technique to solve NP-Complete problems. In GA different initial population seeding techniques were used to find out the performance of an individual. In this paper, we are analyzing the performance of Gene Bank technique with a proposed novel Ordered Distance Vector (ODV) based EV Technique in terms of convergence rate (%), quality solution and convergence diversity. Different authors provided different bench mark instances for MDVRPTW in neo research group. In order to compare the effectiveness and performance of the proposed population seeding technique, we are using Cordeau"s benchmark instances; it contains 20 different instances of MDVRPTW obtained from VRPLIB were experimented using MATLAB software.

Жадные И Адаптивный Алгоритмы Решения Задачи Маршрутизации Транспортных Средств С Несколькими Центрами С Чередованием Объектов

“…Представлены результаты расчетов, в которых разработанный авторами алгоритм на всех предложенных тестовых данных дает лучшее решение по сравнению с другими алгоритмами. В [6] предлагается генетический алгоритм решения ЗМТС с несколькими депо с временными окнами со специально разработанной техникой инициализации популяции. В [7] представлен подход, объединяющий в себе генетические алгоритмы и алгоритмы муравьиной колонии.…”

A Review of Single and Population-Based Metaheuristic Algorithms Solving Multi Depot Vehicle Routing Problem

“…C / / Average improvement = less than 3% (Mirabi, Shokri, and Sadeghieh, 2016) TW / / Average improvement = less than 10% (Yalian, 2016) MD / / Average improvement = less than 20% (Chávez et al, 2016) B / / Average improvement = 0.0157% and 7.4%. (Yao et al, 2014) MD / / Average improvement = less than 3% (Zeng, He, and Zheng, 2014) MD / The algorithm reaching optimum result when solving the low dimension (Ramalingam and Vivekanandan , 2014) MD / / Average improvement =96.61 to 94.99 (Dharmapriya et al, 2014) TW / / Average improvement = 10.8% (Salhi, Imran, & Wassan, 2014) HF / / Cut 80% from the original amount of runtime (Y. et al, 2014) HF / / Average improvement=3.49% (Geetha et al, 2013) MD / / Improvement =The lowest deviation 1.79% and the highest is 23.99%. (Benslimane and Benadada, 2013) HF / / Execution time decline once reaches large size instances of the problem.…”