3-D Packing in Container using Teaching Learning Based Optimization Algorithm
DOI:
https://doi.org/10.21123/bsj.2022.6568Keywords:
Container, Gravitational Search Algorithm, Mathematics, Teaching Learning Based Optimization Algorithm, 3-D PackingAbstract
The paper aims to propose Teaching Learning based Optimization (TLBO) algorithm to solve 3-D packing problem in containers. The objective which can be presented in a mathematical model is optimizing the space usage in a container. Besides the interaction effect between students and teacher, this algorithm also observes the learning process between students in the classroom which does not need any control parameters. Thus, TLBO provides the teachers phase and students phase as its main updating process to find the best solution. More precisely, to validate the algorithm effectiveness, it was implemented in three sample cases. There was small data which had 5 size-types of items with 12 units, medium data which had 10 size-types of items with 106 units, and large data which had 20 size-types of items with 110 units. Moreover, it was also compared with another algorithm called Gravitational Search Algorithm (GSA). According to the computational results in those example cases, it can be concluded that higher number of population and iterations can bring higher chances to obtain a better solution. Finally, TLBO shows better performance in solving the 3-D packing problem compared with GSA.
Received 23/9/2021
Accepted 19/4/2022
Published Online First 20/7/2022
References
Sarder MD. Chapter 1 Overview of transportation logistics. Logistics Transportation Systems. Elsevier Inc, Netherlands; 2021; 1-35.
Sheng, NL, Arbaiy N, Wen CC, Lin PC. Delivery Route Management based on Dijkstra Algorithm. Baghdad Sci J. 2021:18(1) (Suppl.March); 728-736. Available from: http://dx.doi.org/10.21123/bsj.2021.18.1(Suppl.).0728.
Sheng L, Xiuqin S, Changjian C, Hongxia Z, Dayong S, Feiyue W. Heuristics algorithm for the container loading problem with multiple constraints. Comput Ind Eng. 2017; 108: 149-164. Available from : https://doi.org/10.1016/j.cie.2017.04.021.
Zheng JN, Chien CF, Gen M. Multi-Objective Multi-Population Biased Random-Key Genetic Algorithm for the 3-D Container Loading Problem. Comput Industrial Eng. 2015; 89: 80-87. Available from: https://doi.org/10.1016/j.cie.2014.07.012.
Alvarez-Valdes R, Carravilla MA, Oliveira JF. Cutting and packing. Handbook of Heuristics. Springer Cham, Switzerland; 2018; 1-46
Zhuo Q, Liu X. A swarm optimization algorithm for practical container loading problem. Proceedings IECON 2017 – 43rd Annual Conference of the IEEE Industrial Electronics Society. 2017; 133535. Available from: https://doi.org/10.1109/IECON.2017.8216987.
Rao RV, Savsani V, Balic J. Teaching-Learning-Based Optimization Algorithm for Unconstrained and Constrained Real-Parameter Optimization Problem. Eng Optim. 2012; 44(12): 1447-1462. Avaliable from: https://doi.org/10.1080/0305215X.2011.652103.
Joshi PM, Verma HK. An improved TLBO based economic dispatch of power generation through distributed energy resources considering enviromental constraints. Sustain Energy Grids Netw. 2019; 18: 100207. Available from: https://doi.org/10.1016/j.segan.2019.100207.
Farahani HF, Rashidi F. An improved teaching-learning-based optimization with differential evolustion algorithm for optimal power flow considering HVDC system. J Renew Sustain Energy. 2017; 9: 035505. Available from: https://doi.org/10.1063/1.4989828.
Rao RV, Savsani VJ, Vakharia DP. An Optimization Method for Continous Non-Linear Large Scale Problem. Inf Sci. 2012; 183(1): 1-15. Available from: https://doi.org/10.1016/j.ins.2011.08.006.
Yasear SA, Ku-Mahamud KR. Taxonomy of Memory Usage in Swarm Intelligence-Based Metaheuristics. Baghdad Sci J. 2019:16(Special Issue); 445-452. Available from: https://doi.org/10.21123/bsj.2019.16.2(SI).0445.
Rashedi E, Rashedi E, Nezamabadi-pour H. A comprehensive survey on gravitational search algorithm. Swarm Evol. Comput. 2018; 41: 141-158. Available from: https://doi.org/10.1016/j.swevo.2018.02.018.
Siddique N, Adeli, H. Chapter 2 Gravitational Search Algorithm. Nature-Inspired Computing Physics and Chemistry-Based Algorithms. Taylor and Francis Group, London; 2017; 51-110.
Parreno F, Alvarez-Valdes R, Oliveira JF, Tamarit JM. Neighborhood Structures for the Container Loading Problem: A VNS Implementation. J Heuristics. 2010; 16: 1-22. Available from: https://doi.org/10.1007/s10732-008-9081-3.
Dereli T, Sena Das G. A Hybrid Simulated-Annealing Algorithm for Two-Dimensional Strip Packing Problems. International Conference on Adaptive and Natural Computing Algorithms. 2007; 4431: 508-516. Available from: https://doi.org/10.1007/978-3-540-71618-1_56.
Rao RV. Chapter 2 Teaching-Learning-Based Optimization Algorithm. Teaching Learning Based Optimization Algorithm and Its Engineering Applications. Springer Cham. Switzerland. 2016; 9-39.
Mittal H, Tripathi A, Pandey AC, Pal R. Gravitational search algorithm: a comprehensive analysis of recent variants. Multimed Tools Appl. 2021; 80: 7581-7608. Available from: https://doi.org/10.1007/s11042-020-09831-4.
Beasley JE. Available from: OR-library: http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/wtpack2.txt. [12 January 2020]. OR-Library: distributing test problems by electronic mail. J Oper Res Soc. 1990; 41 (11): 1069-1072.
Beasley JE. Available from: OR-library: http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/wtpack4.txt. [12 January 2020]. OR-Library: distributing test problems by electronic mail. J Oper Res Soc. 1990; 41 (11): 1069-1072.
Beasley JE. Available from: OR-library: http://people.brunel.ac.uk/~mastjjb/jeb/orlib/files/wtpack7.txt. [12 January 2020]. OR-Library: distributing test problems by electronic mail. J Oper Res Soc. 1990; 41 (11): 1069-1072.
Sahu RK, Panda S, Padhan S. Optimal Gravitational Search Algorithm for Automatic Generation Control of Interconnected Power Systems. Electr Eng. 2014: 5(3); 721-733. Available from: https://doi.org/10.1016/j.asej.2014.02.004.
Downloads
Published
Issue
Section
License
Copyright (c) 2022 Baghdad Science Journal
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
