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Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms

by Janaki Wanigasooriya, T G I Fernando
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 64 - Number 20
Year of Publication: 2013
Authors: Janaki Wanigasooriya, T G I Fernando
10.5120/10747-5712

Janaki Wanigasooriya, T G I Fernando . Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms. International Journal of Computer Applications. 64, 20 ( February 2013), 1-9. DOI=10.5120/10747-5712

@article{ 10.5120/10747-5712,
author = { Janaki Wanigasooriya, T G I Fernando },
title = { Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { February 2013 },
volume = { 64 },
number = { 20 },
month = { February },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume64/number20/10747-5712/ },
doi = { 10.5120/10747-5712 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:17:06.096626+05:30
%A Janaki Wanigasooriya
%A T G I Fernando
%T Multi-Vehicle Passenger Allocation and Route Optimization for Employee Transportation using Genetic Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 64
%N 20
%P 1-9
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Design of optimal solutions to real world problems are quite complicated and optimizing vehicle routing is significant in today's world. Vehicle routing problems are combinatorial and NP hard. This research discusses about employee transportation optimization which uses split deliveries when the employees' demand of a city greater than the vehicle capacities where vehicle capacities may be homogeneous or heterogeneous. The problem is purely multi-objective and the objectives considered in the problem are minimizing travel time, minimizing total distance, and minimizing no of vehicles which are the most concerned by companies and employees. The proposed algorithms for the employee transport optimization run efficiently and provide invaluable support to the decision maker for taking right routing decisions.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Split Delivery Vehicle Routing Problem (SDVRP) Genetic Algorithm Multi-Objective Optimization (MOO) Employee transport optimization

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