Genetic Algorithm Based Storage and Retrieval System Optimization Considering Operational Constraints in a Multidimensional Warehouse

Year 2020, Volume: 8 Issue: 4, 148 - 153, 31.12.2020

Hüseyin Yılmaz , Adem Tuncer

https://doi.org/10.18100/ijamec.802125

Cited By: 4

Abstract

Efficient use of warehouse resources is an important issue that makes them more manageable and useful, also helps product flow faster. In multidimensional warehouses with many constraints such as weight, volume, product compatibility, etc., storage and retrieval processes are complex optimization problems that need to be solved. Considering the number of constraints, the solution to the storage and retrieval problems with traditional algorithms take a long time. Meta-heuristic algorithms are frequently used in the solution of many complex optimization problems as they can provide acceptable solutions in a short time. In this study, the Genetic algorithm which is one of the popular meta-heuristic methods was used to solve this problem, and the A-star algorithm was used to travel the shortest path between the shelves. A three-dimensional warehouse with operational constraints was designed. Storage and retrieval orders containing a different number of pallets were produced randomly to perform warehouse product flow, and some of these orders were assumed as storage requests and the remainder were retrieval requests. Results show that the proposed approach is capable of finding effective solutions for storage and retrieval problems with operational constraints in a short time.

Keywords

A-star algorithm, Genetic algorithm, Operational constraints, Storage and retrieval system

References

• S. T. Hackman, M. J. Rosenblatt, and J. M. Olin, “Allocating items to an automated storage and retrieval system,” IIE Trans, vol. 22, pp. 7-14, 1990. DOI: (10.1080/07408179008964152)
• S. Trab, E. Bajic, A. Zounkhi, M. N. Abdelkerim, H. Chekir, and L. T. Ltaief, “Product allocation planning with safety compatibility constraints in IoT-based warehouse,” Procedia Comput. Sci., vol. 73, pp. 290-297, December 2015. DOI: (10.1016/j.procs.2015.12.033)
• A. Ramaa, K. N. Subramanya, and T. M. Rangaswamy, “Impact of warehouse management system in a supply chain,” Int. J. Comput. Appl., vol. 54, pp. 15-20, September 2020. DOI: (10.5120/8530-2062)
• K. J. Roodbergen and I. F. A. Vis, “A survey of literature on automated storage and retrieval systems,” Eur. J. Oper. Res., vol. 194 pp. 343-362, April 2009. DOI: (10.1016/j.ejor.2008.01.038)
• M. Kazemi, A. Asef-vaziri, and T. Shojaei, “Concurrent optimization of shared location assignment and storage/retrieval scheduling in multi-shuttle automated storage and retrieval systems,” IFAC-PapersOnLine, vol. 52, pp. 2531-2536, December 2019. DOI: (10.1016/j.ifacol.2019.11.587)
• P. Yang, L. Miao, Z. Xue, and B. Ye, “Variable neighborhood search heuristic for storage location assignment and storage/retrieval scheduling under shared storage in multi-shuttle automated storage/retrieval systems,” Transportation Research Part E: Logistics and Transportation Review, vol. 79, pp. 164-177, July 2015. DOI: (10.1016/j.tre.2015.04.009)
• J. P. V. D. Berg, A. J. R. M. N. Gademann, “Optimal routing in an automated storage/retrieval system with dedicated,” IIE Trans, vol. 31, pp. 407-415, May 1999. DOI: (10.1080/07408179908969844)
• C. Kasemset and J. Sudphan, “Warehouse storage assignment: The case study of a plastic bag manufacturer,” in 2014 IEEE Int. Conf. on Industrial Engineering and Engineering Management, 2014, Bandar Sunway, Malaysia, December 9-12, pp. 219-222. DOI: (10.1109/IEEM.2014.7058632)
• S. S. Heragu, L. Du, R. J. Mantel, and P. C. Schuur, “Mathematical model for warehouse design and product allocation,” Int. J. Prod. Res., vol. 43, pp. 327-338, January 2005. DOI: (10.1080/00207540412331285841)
• F. Guerriero, R. Musmanno, O. Pisacane, and F. Rende, “A mathematical model for the multi-levels product allocation problem in a warehouse with compatibility constraints,” Appl. Math. Model, vol. 37, pp. 4385-4398, March 2013. DOI: (10.1016/j.apm.2012.09.015)
• O. Sanei, V. Nasiri, M. R. Marjani, and S. M. Moattar Husseini, “A heuristic algorithm for the warehouse space assignment problem considering operational constraints: with application in a case study,” in 2011 Int. Conf. on Industrial Engineering and Operations Management, 2011, Kuala Lumpur, Malaysia, January 22-24, pp. 258-264.
• W. Hu, Y. Wang, and J. Zheng, “Research on warehouse allocation problem based on the artificial bee colony inspired particle swarm optimization (ABC-PSO) algorithm,” Fifth Int. Symp. on Computational Intelligence and Design, vol. 1, pp. 173-176, October 2012. DOI: (10.1109/ISCID.2012.51)
• M. E. Hiri, A. Ennadi, and A. Chafi, “Order allocation using genetic algorithm,” J. Eng. Sci. Technol. Rev., vol. 13 pp. 127-134, April 2020. DOI: (10.25103/jestr.132.17)
• A. Tuncer, M. Yildirim, and K. Erkan, “A hybrid implementation of genetic algorithm for path planning of mobile robots on FPGA,” in Computer and Information Sciences III, October 2013, pp. 459-465, Springer, London, United Kingdom. DOI: (10.1007/978-1-4471-4594-3_47)
• A. Tuncer and M. Yildirim, “Chromosome coding methods in genetic algorithm for path planning of mobile robots,” in Computer and Information Sciences II, September 26-28, 2011, London, United Kingdom, pp. 377-383, Springer, London January 2011. DOI: (10.1007/978-1-4471-2155-8-48)
• D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning. Boston: Addison-Wesley Longman Publishing Company Inc., 1989.