There are two methods of storing products in a warehouse. First, there is a dedicated storage, for which each product can be stored in only one location and one location is dedicated for just one product. There is also a shared storage, in which each location can store any number of various products and each product can be stored in many, sometimes very distant locations. The advantage of the dedicated storage is that it is very simple to manage and the pickers can quite easily remember, where each product is stored. The disadvantage of such system is that the storing space is used much less efficiently. The shared storage uses the storage space much better, but causes that remembering, where each product is stored is impossible. Therefore, if a company utilises the shared storage system, it must use a specialised system of warehouse management. Such system must manage, where to place replenishment orders and from where products should be taken in order to complete the customers' orders. Therefore, in the case of shared storage, the selection of locations becomes a significant problem in the process of order-picking. It should be noted that there are many automated systems, in which the whole order-picking process is automatic, but still the majority of companies utilise the classical, picker-to-parts system.

The criteria of locations selection can be various (distance from the start, quantity of product in the given location, number of other products in close neighbourhood, or storage time). The decision maker may use one or all of them by creating the synthetic variable, in which particular criteria will have appropriate weights. In the research the author compared the two methods of locations selection. Both of them rank locations in which every completed product is located and select these locations, which have the highest position in the ranking. The first method, named TMAL (after the Polish abbreviation of the Taxonomic Measure of Location's Attractiveness), is based on the classical Synthetic Measure of Development (SMD), created by Hellwig (1968), The second method is the TOPSIS method, created by Hwang and Yoon (1981). In both methods, each location was characterised by means of three variables:

·         the distance from the start,

·         the degree of demand satisfaction,

·         the number of other picked products in the neighbourhood of the analysed location

The author applied seven combinations of weights. During application of each of the two procedures, variables must be normalised. There are many methods of normalisation. In the research the unitarisation method was selected. After ranking locations and selecting them, a route for the picker was designated. The s-shape heuristics was selected. The best method and the best combination was that one that resulted in the shortest route.

One order was analysed. The results showed that the best combination of weights was the combination, in which the number of other picked products in the neighbourhood of the analysed location had weight twice as high as the two remaining variables (0.5 vs 0.25 for the distance from the start and the degree of demand satisfaction). Also, the TOPSIS method generally generated slightly shorter routes. However, it should be analysed for larger number of orders and it will be done in the final version of the paper. Also, the directions for further research are that other methods of normalisation will be used (such, as standardisation and quotient transformation).

References

Bartholdi J.J., Hackman S.T. (2014), Warehouse & Distribution Science, Release 0.96, The Supply Chain and Logistics Institute, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA 30332-0205 USA.

De Koster, R., Le-Duc, T., and Roodbergen, K.J. (2007), Design and control of warehouse order picking: a literature review. European Journal of Operational Research 182(2), s. 481-501.

Gudehus T., Kotzab H. (2012), Comprehensive Logistics, Second Edition, Springer-Verlag Berlin Heidelberg, DOI: 10.1007/978-3-642-24367-7.

Hellwig Z. (1968), Zastosowanie metody taksonomicznej do typologicznego podziału krajów ze względu na poziom rozwoju oraz zasoby i strukturę wykwalifikowanych kadr, Przegląd Statystyczny, nr 15.4 (in Polish).

Hwang, C.L., Yoon, K. (1981), Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.

Nowak E. (1990), Metody taksonomiczne w klasyfikacji obiektów społeczno-gospodarczych, PWE, Warszawa (in Polish).

Tarczyński G. (2012), Analysis of the impact of storage parameters and the size of orders on the choice of the method for routing order picking, „Operations Research and Decisions”, No. 22, s. 105-120.

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