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Analytical and simulation determination of order picking time in a low storage warehouse for shared storage systems

100%
DMYTRÓW K.
Operations Research and Decisions
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2022
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vol. 32
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issue 2
34-51
EN
The aim of the research is comparison between average order picking times obtained using the analytical model and simulation methods for shared storage systems. We also compare the results obtained with the results obtained for dedicated storage. We assume the random and ABC-class storage (with within and across aisle storage policies). We select the locations by means of the TOPSIS method for two take-out strategies: quantity adjustment (QA) and priority of partial units (PPU). We determine the route by using s-shape and return heuristics. In most cases, the simulated average order picking times are shorter than the analytical ones. It results from not considering the criteria’ weights in calculation of the analytical order picking time. Also, the results for shared storage with QA strategy are in most cases better than for dedicated storage. This might imply an advantage of shared over dedicated storage, but needs further confirmation.
2
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Symulacyjna analiza wpływu liczby i rozmieszczenia luk niesystematycznych na dokładność prognoz

100%
Oesterreich M.
Econometrics. Ekonometria. Advances in Applied Data Analytics
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2015
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issue 2 (48)
78-88
EN
In this paper there was conducted a statistical analysis of the impact of the distribution of unsystematic gaps on the accouracy of inter- and extrapolative forecasts in the seasonal time series. In the analysis, as variable, there was used the average period of stay of tourists in accommodation establishments in the West Pomeranian Voivodeship in the years 2008-2013. In calculations there were used simulation methods to generate ten thousand sets of gaps for the three variants, differed in the number of gaps. For all the set and variants of gaps, there were estimated time series models with exponential trend and relatively-fixed seasonality. In the next step there were built inter- and extrapolative forecasts and calculated their relative errors (MAPE). In the analysis there were used R program and Statistica 10.
3
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Comparison of Several Linear Ordering Methods for Selection of Locations in Order‑picking by Means of the Simulation Methods

51%
Dmytrów K.
Acta Universitatis Lodziensis. Folia Oeconomica
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2018
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vol. 5
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issue 338
81-96
PL
Przy przechowywaniu współdzielonym wybór lokalizacji, z których należy pobrać produkty podczas procesu kompletacji, nie jest sprawą oczywistą. Każdą lokalizację, w której znajduje się produkt do skompletowania zamówienia, można opisać za pomocą wielu zmiennych, na przykład: czasu przechowywania produktu, odległości od punktu odkładczego, stopnia zaspokojenia zapotrze­bowania czy liczby innych produktów w zleceniu, znajdujących się w sąsiedztwie badanej lokalizacji. Tak więc „atrakcyjność” każdej lokalizacji z punktu widzenia kompletacji badanego zlecenia można opisać za pomocą zmiennej syntetycznej, na podstawie której tworzymy ranking tych lokalizacji. Dla każdego produktu wybiera się lokalizacje będące najwyżej w rankingu, a następnie wyznacza się tra­sę, którą ma pokonać magazynier. W artykule zostały porównane wyniki uzyskane za pomocą kilku metod klasyfikacji: Taksonomicznej Miary Atrakcyjności Lokalizacji, opartej na Syntetycznym Mierniku Rozwoju Hellwiga, metody TOPSIS oraz Uogólnionej Miary Odległości.
EN
When a company uses a shared storage system, selection of locations during the order‑picking process is not an obvious task. Every location where the picked product is placed, can be described by means of several variables, such as: storage time, distance from the I/O point, degree of demand satisfaction, or the number of other picked products in the order. Therefore, the “attractiveness” of each location from the point of view of a certain order can be described by means of synthetic variable, on the basis of which a ranking is created. For each product, the decision‑maker selects the highest‑ranking locations and designates a route for the picker. In the article, by means of the simulation methods, results obtained by several classification methods will be compared. These methods are: Taxonomic Measure of Location’s Attractiveness (based on the Hellwig’s Composite Measure of Development), the TOPSIS method with the Euclidean and GDM distances and the Gen­eralised Distance Measure used as the composite measure of development.
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