A Conceptual Framework for Adaptive Storage Location Assignment Considering Order Characteristics

Year 2020, Ejosat Special Issue 2020 (ARACONF), 610 - 614, 01.04.2020

Polina Gorbe , Tamás Bódıs János Botzheım

https://doi.org/10.31590/ejosat.araconf74

Abstract

Nowadays, the fragmented orders, the shrinking time windows, the special customer requirements, and the inventory reduction requirements are the biggest challenges in warehouse logistics and production supply. Research studies have already examined different type of order picking routing optimization and considered optimal storage location assignment (SLA). The several SLA solutions take into consideration several factors, however, these usually require the repositioning of the stored products.
Based on our industrial experiments there is a lack of industrial application and support of the systems to keep up the ideal SLA. The aim of this paper is to define a potential concept to keep up a nearly optimal SLA during order picking position replenishment with minimized number of time consuming and labor-intensive product repositioning tasks.

Keywords

Storage location assignment, Adaptive, Conceptual framework

Thanks

P. Görbe and T. Bódis acknowledge the financial support of this research by the Project EFOP-3.6.1-16-2016-00017. Internationalisation, initiatives to establish a new source of researchers and graduates, and development of knowledge and technological transfer as instruments of intelligent specialisations at Széchenyi István University. J. Botzheim was supported by the János Bolyai Research Scholarship of the Hungarian Academy of Sciences.

References

• Accorsi, R., Baruffaldi, G., & Manzini, R. (2018). Picking efficiency and stock safety: A bi-objective storage assignment policy for temperature-sensitive products. Computers & Industrial Engineering Volume 115, 240-252.
• Bódis, T., & Botzheim, J. (2018). Bacterial Memetic Algorithms For Order Picking Routing Problem With Loading Constraints. Expert Systems with Applications, 105, 196-220.
• Botzheim, J., Cabrita, C., Koczy, L. T., & Ruano, A. (2009). Fuzzy rule extraction by bacterial memetic algorithms. International Journal of Intelligent Systems, 24(3), 312–339.
• Carlo, H. J., & Giraldo, G. E. (2012). Toward perpetually organized unit-load warehouses. Computers & Industrial Engeneering Volume 63, 1003-1012.
• Chmiel, W. (2019). Evolutionary algorithm using conditional expectation value for quadratic assignment problem. Swarm and Evolutionary Computation, 46, 1-27.
• De Koster, R., Le-Duc, T., & Roodbergen, K. J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182, 481-501.
• Dijkstra, A. S., & Roodbergen, K. J. (2017). Exact route-length formulas and a storage location assignment heuristic for picker-to-parts warehouses. Transportation Research Part E, 38-59.
• Földesi, P., Botzheim, J., & Kóczy, L. T. (2011). EUGENIC BACTERIAL MEMETIC ALGORITHM FOR FUZZY ROAD TRANSPORT TRAVELING SALESMAN PROBLEM. International Journal of Innovative Computing, 5(2775-2798), 7.
• Gils, T. v., Caris, A., Ramaekers, K., Braekers, K., & de Koster, R. B. (2019). Designing efficient order picking systems: The effect of real-life features on the relationship among planning problems. Transportation Research Part E 125, 47-73.
• Gils, T. v., Ramaekers, K., Braekers, K., Depaire, B., & Caris, A. (2018). Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal pf Production Economics, 243-261.
• Koopmans, T. C., & Beckmann, M. (1957). Assignment Problems and the Location of Economic Activities. Econometrica, 25(1), 53-76.
• Manzini, R., Accorsi, R., Gamberi, M., & Penazzi, S. (2015). Modeling class-based storage assignment over life cycle picking patterns. Int. J. Production Economics Volume 170, Part C, 790-800.
• Micale, R., La Fata, C., & La Scalia, G. (2019). A combined interval-valued ELECTRE TRI and TOPSIS approach for solving the storage location assignment problem. Computers & Industrial Engineering 135, 199-210.
• Quader, S., & Castillo-Villar, K. K. (2018). Design of an enhanced multi-aisle order-picking system considering storage assignments and routing heuristics. Robotics and Computer–Integrated Manufacturing 50, 13-29.
• Sahni, S., & Gonzalez, T. (1967). P-Complete Approximation Problems. Journal of the ACM (JACM), 23(3), 555-565.
• Wang, M., & Zhang, R.-Q. (2019). A dynamic programming approach for storage location assignment planning problem. Procedia CIRP 83, 513-516.
• Žulj, I., Glock, C. H., Grosse, E. H., & Schneider, M. (2018). Picker routing and storage-assignment strategies for precedence-constrained order picking. Computers & Industrial Engineering Volume 123, 338-347.