A Decision Support System for Multi-Objective Porcelain Container Loading Problem Based on Genetic Algorithm

Abstract

The multi-objective genetic algorithm approach for solving the Porcelain Container Loading Problem (PCLP) has a vital role in the global logistics industry. In this paper, a logistical problem with one constrained container loading problem that has to be filled with a set of boxes has been the focus. This study addresses a real-life problem that exports departments in the international porcelain industry face. Our objective is to maximize product profitability and delivery priority. Since the CLP is known as an NP-hard problem, the Genetic Algorithm (GA) approach is proposed to solve the problem based on these objective functions. The parameters of the GA affect the obtained results. We made tuning by using an experimental design in order to determine the appropriate parameters. The main contribution of the study is to present a new decision support system taking into account the objectives of the delivery time and profit rate priority of the manufacturer in the porcelain sector. Thus, loading according to the company’s priority and distribution in the shortest distance has been successfully achieved. The results show the efficiency of the proposed decision support system, which solves the CLP with up to 12 different products in boxes of different volumes.

Keywords

• Decision support system for porcelain industry
• container loading problem
• experimental design
• multi-objective problem

References

1. H. Y. Sener, “Determining new markets using Analytic Hierarchy Process: case study in Güral Porcelain,” Int. J. Mark. Stud., vol. 6, no. 5, p. 149, 2014.
2. E. Sánchez, J. García-Ten, V. Sanz, and A. Moreno, “Porcelain tile: almost 30 years of steady scientific-technological evolution,” Ceram. Int., vol. 36, no. 3, pp. 831–845, 2010.
3. B. Feng, Y. Li, and Z.-J. M. Shen, “Air cargo operations: Literature review and comparison with practices,” Transp. Res. Part C Emerg. Technol., vol. 56, pp. 263–280, 2015.
4. A. Galrão Ramos, J. F. Oliveira, J. F. Gonçalves, and M. P. Lopes, “A container loading algorithm with static mechanical equilibrium stability constraints,” Transp. Res. Part B Methodol., vol. 91, pp. 565–581, 2016.
5. J. Olsson, T. Larsson, and N.-H. Quttineh, “Automating the planning of container loading for Atlas Copco: Coping with real-life stacking and stability constraints,” Eur. J. Oper. Res., vol. 280, no. 3, pp. 1018–1034, 2020.
6. A. Cuzzocrea, M. Nolich, and W. Ukovich, “A Big-Data-Analytics Framework for Supporting Logistics Problems in Smart-City Environments,” Procedia Comput. Sci., vol. 159, pp. 2589–2597, 2019.
7. J. Mar-Ortiz, N. Castillo-García, and M. D. Gracia, “A decision support system for a capacity management problem at a container terminal,” Int. J. Prod. Econ., no. September, p. 107502, 2019.
8. S. Fazi, J. C. Fransoo, and T. Van Woensel, “A decision support system tool for the transportation by barge of import containers: A case study,” Decis. Support Syst., vol. 79, pp. 33–45, 2015.

9. P. Legato and R. M. Mazza, “A decision support system for integrated container handling in a transshipment hub,” Decis. Support Syst., vol. 108, pp. 45–56, 2018.
10. T. Fan, Q. Pan, F. Pan, W. Zhou, and J. Chen, “Intelligent logistics integration of internal and external transportation with separation mode,” Transp. Res. Part E Logist. Transp. Rev., vol. 133, p. 101806, 2020.
11. M. A. Salido, M. Rodriguez-Molins, and F. Barber, “A decision support system for managing combinatorial problems in container terminals,” Knowledge-Based Syst., vol. 29, pp. 63–74, 2012.
12. S. Ozcan and D. T. Eliiyi, “A reward-based algorithm for the stacking of outbound containers,” Transp. Res. procedia, vol. 22, pp. 213–221, 2017.
13. F. Feng, Y. Pang, G. Lodewijks, and W. Li, “Collaborative framework of an intelligent agent system for efficient logistics transport planning,” Comput. Ind. Eng., vol. 112, pp. 551–567, 2017.
14. A. G. Ramos, J. F. Oliveira, J. F. Gonçalves, and M. P. Lopes, “Dynamic stability metrics for the container loading problem,” Transp. Res. Part C Emerg. Technol., vol. 60, pp. 480–497, 2015.
15. T. Jamrus and C.-F. Chien, “Extended priority-based hybrid genetic algorithm for the less-than-container loading problem,” Comput. Ind. Eng., vol. 96, pp. 227–236, 2016.
16. T.-N. Chuang, C.-T. Lin, J.-Y. Kung, and M.-D. Lin, “Planning the route of container ships: A fuzzy genetic approach,” Expert Syst. Appl., vol. 37, no. 4, pp. 2948–2956, 2010.
17. J.-N. Zheng, C.-F. Chien, and M. Gen, “Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem,” Comput. Ind. Eng., vol. 89, pp. 80–87, 2015.
18. P. B. Castellucci, F. M. B. Toledo, and A. M. Costa, “Output maximization container loading problem with time availability constraints,” Oper. Res. Perspect., vol. 6, p. 100126, 2019.