EODVGA: An Enhanced ODV Based Genetic Algorithm for Multi-Depot Vehicle Routing Problem

Prabu, U and Ravisasthiri, P and Sriram, R and Malarvizhi, N and Amudhavel, J (2019) EODVGA: An Enhanced ODV Based Genetic Algorithm for Multi-Depot Vehicle Routing Problem. EAI Endorsed Transactions on Scalable Information Systems, 6 (21): e6. ISSN 2032-9407

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Abstract

Multi-Depot Vehicle Routing Problem (MDVRP) is a familiar combinative optimization problem that simultaneously determines the direction for different vehicles from over one depot to a collection of consumers. Researchers have suggested variety of meta-heuristic and heuristic algorithms to elucidate MDVRP, but none of the existing technique has improved the fitness of the solution at the time of initial population generation. This motivates to propose an enhanced ODV based population initialization for Genetic Algorithm (GA) to solve MDVRP effectively. The Ordered Distance Vector (ODV) based population seeding method is a current and effective population initialization method for Genetic Algorithm to produce an early population with quality, individual diversity and randomness. In the proposed model, the customers are first grouped based on distance to their nearest depots and then routes are scheduled and optimized using enhanced ODV based GA. The experiments are performed based on different types of instances of Cordeau. From the experimental results, it is very clear that the proposed technique outperforms the existing techniques in terms of convergence rate, error rate and convergence diversity.

Item Type: Article
Uncontrolled Keywords: Multi-Depot Vehicle Routing Problem (MDVRP), Ordered Distance Vector (ODV), Genetic Algorithm (GA)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
QA75 Electronic computers. Computer science
Depositing User: EAI Editor II.
Date Deposited: 08 Oct 2020 13:56
Last Modified: 08 Oct 2020 13:56
URI: https://eprints.eudl.eu/id/eprint/711

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