CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

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

by Janaki Wanigasooriya, T G I Fernando
journal cover thumbnail

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
  1. Abdullah Konak, David W. Coit, and Alice E. Smith. Multiobjective Optimization Using Genetic Algorithms: A Tutorial. Reliability Engineering and System Safety 91, 9921007, 2006.
  2. The VRP web, http://neo. lcc. uma. es/radi-aeb/WebVRP/
  3. Padmabati Chand, Bhabani Sankar Prasad Mishra and Satchidananda Dehuri. A Multi-objective genetic algorithm for solving vehicle routing problem. International Journal of Information Technology and Knowledge Management, 503-506, Jul-Dec, 2010.
  4. M. J. Geiger. Genetic Algorithms for Multiple Objective Vehicle Routing. Production and Operations Management, University of Hohenheim, MIC'2001 - 4th Meta-heuristics International Conference, 349-353, 2001.
  5. B. Ombuki, B. J. Ross, and F. Hanshar. Multi-objective Genetic Algorithms for Vehicle Routing Problem with Time Windows. Brock University, Canada, January 2004.
  6. Anna Syberfeldt, Henrik Grimm, Amos Ng, Martin Andersson and Ingemar Karlsson. Simulation- based optimization of a complex mil transportation network. University of Skovde, Sweden, Proceedings of the 2008 Winter Simulation Conference, 2008.
  7. ZHANG Feizhou, CAO Xuejun and YANG Dongkai. Intelligent Scheduling of Public Traf?c Vehicles Based on a Hybrid Genetic Algorithm. Peking University, China, TSINGHUA SCIENCE AND TECHNOLOGY, Volume 13, No. 5, 625-631, October 2008.
  8. Kaisa Miettinen. Introduction to Multi objective Optimization: Non-interactive Approaches in Lecture Notes in Computer Science series. Springer-Verlag Berlin Heidelberg, 126, 2008.
  9. Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, and T. Meyarivan. A Fast and Elitist Multi-objective Genetic Algorithm: NSGA-II. IEEE Transactions on evolutionary computation, vol. 6, no. 2, April 2002.
Index Terms

Computer Science
Information Sciences

Keywords

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

Powered by PhDFocusTM