With rapid growth of demands, transportation of goods today becomes much busier. Finding the optimal transport job assignment for the requested demands is essential for transportation providers. This paper develops a multi-objective model using priori preference approaches such as lexicographic and goal programming (GP) to complete the satisfactory level of all objectives for a transshipment problem which presents variant of vehicle types and routing. The problem focuses to determine the number of vehicles owned by different clients (origin points) that will be transferred to a meeting point area to process a transport service or upload goods in order to be transferred to a destination point. The proposed model is tested using a real-life data from a transportation company located in the state of Kuwait. Perhaps, no previous work has been conducted in the state of Kuwait that  refers to solve a transshipment problem caused by vehicles as an optimization problem. By adopting the proposed model, we can expect to minimize traveling time from origin point to the meeting point and total transportation cost from the meeting point to the destination point while satisfying all the demands.

multi-objective problem, transportation, transshipment, lexicographic method, goal programming.

Received: June 1, 2022; Accepted: July 15, 2022; Published: August 24, 2022

How to cite this article: Ahmad T. Al-Sultan and Ahmad Alsaber, Solving vehicle transshipment problem using multi-objective optimization, Far East Journal of Applied Mathematics 114 (2022), 65-82. http://dx.doi.org/10.17654/0972096022015

This Open Access Article is Licensed under Creative Commons Attribution 4.0 International License

References:

[1] D. Alexiou and S. Katsavounis, A multi-objective transportation routing problem, Operational Research 15(2) (2015), 199-211.
[2] P. Anukokilaa, B. Radhakrishnan and M. Rajeshwaria, Multi-objective transportation problem by using goal programming approach, Int. J. Pure Appl. Math. 117(11) (2017), 393-403.
[3] J. S. Arora, Chapter 18-multi-objective optimum design concepts and methods, Introduction to Optimum Design, 4th ed., Academic Press, Boston, MA, USA, 2017, pp. 771-794.
[4] D. J. Bertsimas and G. Van Ryzin, Stochastic and dynamic vehicle routing in the Euclidean plane with multiple capacitated vehicles, Oper. Res. 41(1) (1993), 60-76.
[5] H. I. Calvete, C. Galé, M. J. Oliveros and B. Sánchez-Valverde, A goal programming approach to vehicle routing problems with soft time windows, European J. Oper. Res. 177(3) (2007), 1720-1733.
[6] A. Charnes and W. W. Cooper, Goal programming and multiple objective optimizations: Part 1, European J. Oper. Res. 1(1) (1977), 39-54.
[7] N. Danloup, H. Allaoui and G. Goncalves, A comparison of two meta-heuristics for the pickup and delivery problem with transshipment, Comput. Oper. Res. 100 (2018), 155-171.
[8] C. Friedrich and R. Elbert, Adaptive large neighborhood search for vehicle routing problems with transshipment facilities arising in city logistics, Comput. Oper. Res. 137 (2022), Paper No. 105491, 20 pp.
[9] H. Garg, A. Mahmoodirad and S. Niroomand, Fractional two-stage transshipment problem under uncertainty: application of the extension principle approach, Complex and Intelligent Systems 7(2) (2021), 807-822.
[10] K. Ghoseiri and S. F. Ghannadpour, Multi-objective vehicle routing problem with time windows using goal programming and genetic algorithm, Applied Soft Computing 10(4) (2010), 1096-1107.
[11] P. K. Giri, M. K. Maiti and M. Maiti, Fuzzy stochastic solid transportation problem using fuzzy goal programming approach, Computers and Industrial Engineering 72 (2014), 160-168.
[12] N. Gupta, I. Ali and A. Bari, A compromise solution for multi-objective chance constraint capacitated transportation problem, ProbStat Forum 26 (2013), 60-67.
[13] R. S. Hemaida and N. K. Kwak, A linear goal programming model for transshipment problems with flexible supply and demand constraints, Journal of the Operational Research Society 45(2) (1994), 215-224.
[14] K. B. Jagtap and S. V. Kawale, Optimizing transportation problem with multiple objectives by hierarchical order goal programming model, Glob. J. Pure Appl. Math. 13(9) (2017), 5333-5339.
[15] L. Li and K. K. Lai, A fuzzy approach to the multiobjective transportation problem, Comput. Oper. Res. 27(1) (2000), 43-57.
[16] Y. Z. Mehrjerdi, A multiple objective stochastic approach to vehicle routing problem, The International Journal of Advanced Manufacturing Technology 74(5-8) (2014), 1149-1158.
[17] M. A. Nomani, I. Ali and A. Ahmed, A new approach for solving multi-objective transportation problems, International Journal of Management Science and Engineering Management 12(3) (2017), 165-173.
[18] W. Ogryczak, K. Studziński and K. Zorychta, A solver for the multi-objective transshipment problem with facility location, European J. Oper. Res. 43(1) (1989), 53-64.
[19] A. Quddoos, S. Javaid and M. M. Khalid, A new method to solve bi-objective transportation problem, Int. J. Appl. Math. 26(5) (2013a), 555-563.
[20] A. Quddoos, S. Javaid, I. Ali and M. M. Khalid, A lexicographic goal programming approach for a bi-objective transportation problem, International Journal of Scientific and Engineering Research 4(7) (2013b), 1084-1089.
[21] A. Rais, F. Alvelos and M. S. Carvalho, New mixed integer-programming model for the pickup-and-delivery problem with transshipment, European J. Oper. Res. 235(3) (2014), 530-539.
[22] M. Wen, J. F. Cordeau, G. Laporte and J. Larsen, The dynamic multi-period vehicle routing problem, Comput. Oper. Res. 37(9) (2010), 1615-1623.