A Comparative Analysis of Genetic Algorithm and LINGO for an Inbound Transportation Model

Humaira Nafisa Ahmed^1, , Sayem Ahmed¹, Md. Nazmus Sakib¹ and M. M. Mahbubur Rahman¹

¹Department of Mechanical & Production Engineering, Ahsanullah University of Science & Technology, Dhaka-1208, Bangladesh

Cite this paper:
Humaira Nafisa Ahmed, Sayem Ahmed, Md. Nazmus Sakib and M. M. Mahbubur Rahman. A Comparative Analysis of Genetic Algorithm and LINGO for an Inbound Transportation Model. American Journal of Industrial Engineering. 2019; 6(1):13-18. doi: 10.12691/ajie-6-1-2

Abstract

Supply chain management (SCM) has become a topic of critical importance for both companies and researchers today. Supply chain optimization problems are formulated as linear programing problems with costs of transportation that arise in several real-life applications. While optimizing supply chain problems, inbound logistic segment has been considered as one of the most neglected area in SCM. Very few studies have focused on utilizing optimization model on SCM that only accounts for inbound logistic system. This study has identified the research gap and proposed method attempts to minimize the total transportation costs of inbound logistic system with reference to available resources at the plants, as well as at each depot. Genetic algorithm and Lingo were approached to help the top management in ascertaining how many units of a particular product should be transported from plant to each depot so that the total prevailing demand for the company’s products satisfied, while at the same time the total transportation costs are minimized. Finally, a case study involving a Bangladeshi renowned retail super shop is used to validate the performance of the algorithm. In order to evaluate the performance of the proposed genetic algorithm, the obtained result was compared with the outputs of LINGO 17.0. Computational analysis shows that the GA has result very close to optimal solution in very large-sized problems, and in case of small problems, LINGO that means exact method works better than heuristics.

Keywords:
supply chain genetic algorithm LINGO inbound transportation cost

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	R.H. Ballou, “Business Logistics Management: Planning, Organizing, and Controlling the Supply Chain”, fourth ed., Prentice Hall, New Jersey, 1999.
[2]	Sadigh A. Naimi, H. Fallah, and N. Nahavandi, “A multi-objective supply chain model integrated with location of distribution centers and supplier selection decisions,” Int. J. Adv. Manuf. Technol., vol. 69, no. 1-4, pp. 225-235, 2013.
[3]	Anand, G. & Ward, T. Fit, “flexibility and performance in Manufacturing: Coping with dynamic environments. Production and Operations Management”, vol. 13(4), pp. 369-385, 2004.
[4]	Dantzig, G.B., “Linear Programming and Extensions”, Princeton University Press, 1963.
[5]	Hamdy A. Taha, “Operations Research, An Introduction eight edition”, Prentice – Hall, Inc. Upper Saddle River, New Jersey U.S.A, 2008.
[6]	Ali, S.M., G.Kabir, A. Ali, S. Shake, and M.A. Moktadir. “Transportation mode selection framework for Apparel Industries: A Fuzzy AHP Approach”, Selected Studies on Economics and Finance, Cambridge Scholar Publishing, England, 2017.
[7]	Prichanont, K., Prichanont, S., Buransri, N., “Improvement guidelines for sugar cane delivery systems”, Proceedings of the 35th International Conference on Computers, 2005.
[8]	Arntzen, B.C., & Trafton, L.L., “Global supply chain at digital equipment corporation”, Interfaces, vol. 25(1), pp. 69-63, 2005.
[9]	Hong. J., & Liu, B., “Logistics outsourcing by manufacturers in China: A survey of the industry”, Transportation Journal, vol. 43(1), pp. 17-25, 2004.
[10]	Atkinson W., “How e-logistics changes shipper-carrier relationships”, Logistics Management & Distribution Report, The state of E-logistics, 2001.
[11]	Mansidao, R., & Coelho, L., “Logistics performance: a theoretical conceptual model for small and medium enterprises”, Economic Papers, pp. 1-4, 2014.
[12]	Stock and D. Lambert, Strategic Logistics Management. Irwin series in marketing. McGraw-Hill Companies, Incorporated, New York, NY, 4th edition, 2001.
[13]	Green, K. W., Whitten, D., & Imman, R.A., “The Impact of logistics on organizational performance in a supply chain, Supply chain management”, An Internauonal Journal, vol. 13(1), pp. 317-327, 2008.
[14]	Ko M. and A. Tiwari, “Review article A review of soft computing applications in supply chain management,’ Appl. Soft Comput. J., vol. 10, no 3, pp. 661-674, 2010.
[15]	Lawrynowicz, A., A survey of evolutionary algorithms for production and logistics optimization.” Research in Logistcis and Production, Vol. 1(2), pp. 57-91.
[16]	Wen C. and R. Eberhart. “Genetic algorithm for logistics scheduling problem”. In Evolutionary Computation, CEC ’02. Proc. of the 2002 Cong. on, vol. 1, pp. 512-516, 2002
[17]	Jones. T., “Evolutionary Algorithms, Fitness Landscapes and Search”, PhD Thesis, University of New Mexico, Albuquerque, 1995.