INFORMATION CHANGE THE WORLD

International Journal of Modern Education and Computer Science (IJMECS)

ISSN: 2075-0161 (Print), ISSN: 2075-017X (Online)

Published By: MECS Press

IJMECS Vol.6, No.5, May. 2014

Traveling Transportation Problem Optimization by Adaptive Current Search Method

Full Text (PDF, 905KB), PP.33-45


Views:81   Downloads:1

Author(s)

Supaporn Suwannarongsri, Tika Bunnag, Waraporn Klinbun

Index Terms

Traveling transportation problem;adaptive current search;metaheuristic optimization

Abstract

The adaptive current search (ACS) is one of the novel metaheuristic optimization search techniques proposed for solving the combinatorial optimization problems. This paper aimed to present the application of the ACS to optimize the real-world traveling transportation problems (TTP) of a specific car factory. The total distance of the selected TTP is performed as the objective function to be minimized in order to decrease the vehicle’s energy. To perform its effectiveness, four real-world TTP problems are conducted. Results obtained by the ACS are compared with those obtained by genetic algorithm (GA), tabu search (TS) and current search (CS). As results, the ACS can provide very satisfactory solutions superior to other algorithms. The minimum total distance and the minimum vehicle’s energy of all TTP problems can be achieved by the ACS with the distant error of no longer than 3.05%.

Cite This Paper

Supaporn Suwannarongsri, Tika Bunnag, Waraporn Klinbun,"Traveling Transportation Problem Optimization by Adaptive Current Search Method", IJMECS, vol.6, no.5, pp.33-45, 2014.DOI: 10.5815/ijmecs.2014.05.05

Reference

[1]S. Kikuchi and P. Chakroborty, Place of possibility theory in transportation analysis, Transportation Research, Part B, vol.40, pp.595-615, 2006.

[2]G. Reinelt, The Traveling Salesman: Computational Solutions for TSP Applications. Springer-Verlag, 1994. 

[3]M. Bellmore and G. L. Nemhauser, “The traveling salesman problem: a survey,” Operation Research, vol.16, 1986, pp.538-558.

[4]E. L. Lawler, J. K. Lenstra, A. H. G. Rinnooy Kan and D. B. Shmoys, The Traveling Salesman Problem: A Guided Tour of Combinatorial Optimization, John-Wiley & Sons, 1986.

[5]M. Held and R. Karp, “A dynamic programming approach to sequencing problems,” SIAM J, vol.1, 1962, pp.196-210.

[6]J. Little, K. Murty, D. Sweeney and C. Karel, “An algorithm for the traveling salesman problem,” Operation Research, vol.12, 1963, pp.972-989.

[7]M. Padberg and G. Rinaldi G, “A branch-and-cut algorithm for the resolution of large-scale traveling salesman problem,” SIAM Review, vol.33, 1991, pp.60-100.

[8]G. B. Dantzig, D. R. Fulkerson, and S. M. Johnson, “Solution of a large scale traveling salesman problem,” Operation Research, vol.2, 1954, pp.393-410.

[9]E. H. L. Aarts, J. H. M. Korst and P. J. M. Laarhoven, “A quantitative analysis of the simulated annealing algorithm: a case study for the traveling salesman problem,” J. of Stats Phys, vol. 50, 1988, pp.189-206.

[10]J. V. Potvin, “The traveling salesman problem: a neural network perspective,” INFORMS J. of Computing, vol. 5, 1993, pp. 328-348.

[11]C. N. Fiechter, “A parallel tabu search algorithm for large scale traveling salesman problems,” Discrete Applied Mathematics, vol. 51(3), 1994, pp. 243-267.

[12]J. V. Potvin, “Genetic algorithms for the traveling salesman problem,” Annuals of Operation Research, vol.63, 1996, pp.339-370.

[13]Y.-H. Liu, “A hybrid scatter search for the probabilistic traveling salesman problem,” Computers & Operations Research, vol.34, 2007, pp.2949-2963.

[14]X. C. Shi, Y. C. Liang, H. P. Lee, C. Lu and Q. X. Wang, “Particle swarm optimization-based algorithms for TSP and generalized TSP,” Information Processing Letters, vol.103, 2007, pp.169-176.

[15]B. Bontoux and D. Feillet, “Ant colony optimization for the traveling purchaser problem,” Computers & Operations Research, vol.35, 2008, pp.628-637.

[16]S. Suwannarongsri and D. Puangdownreong, “Adaptive tabu search for traveling salesman problems,” International Journal of Mathematics and Computers in Simulation, iss.2, vol.6, 2012, pp.274-281.

[17]S. Suwannarongsri, T. Bunnag and W. Klinbun, “Energy resource management of assembly line balancing problem using modified current search method,” International Journal of Intelligent Systems and Applications, vol.6, no.3, 2014, pp.1- 11.

[18]S. Suwannarongsri, T. Bunnag and W. Klinbun, “Optimization of energy resource management for assembly line balancing using adaptive current search,” American Journal of Operations Research, vol.4, no.1, 2014, pp.8-21.

[19]A. Sukulin and D. Puangdownreong, “A novel meta-heuristic optimization algorithm: current search,” Proceeding of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '12), 2012, pp.125-130. 

[20]A. Sukulin and D. Puangdownreong, “Control synthesis for unstable systems via current search,” Proceeding of the 11th WSEAS International Conference on Artificial Intelligence, Knowledge Engineering and Data Bases (AIKED '12), 2012, pp.131-136. 

[21]Sammitr Motors Manufacturing Public Co., Ltd., 2013. http://www.sammitr.com.

[22]A. C. Madrigal, “How google builds its maps-and what it means for the future of everything,” The Atlantic, 6 September, 2012, retrieved on 9 February 2013.

[23]B. Liebert, “Create, collaborate and share advanced custom maps with google maps engine lite (Beta),” Google Maps. Google, Inc., retrieved on 20 May 2013.

[24]MathWorks, Genetic Algorithm and Direct Search Tool-box: For Use with MATLAB. User’s Guide, vol.1, MathWorks, Natick, Mass, 2005.