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• W3015917329 abstract "Sensitivity analysis of selected parameters in simulation models of logistics facilities is one of the key aspects in functioning of self-conscious and efficient management. In order to develop simulation models adequate of real logistics facilities’ processes, it is important to input actual data connected to material flows on entry to models, whereas most models assume unified load units as default. To provide such data, pseudorandom number generators (PRNGs) are used. The original generator described in the paper was employed in order to generate picking lists for order picking process (OPP). This ensures building a hypothetical, yet close to reality process in terms of unpredictable customers’ orders. Models with applied PRNGs ensure more detailed and more understandable representation of OPPs in comparison to analytical models. Therefore, the author’s motivation was to present the original model as a tool for enterprises’ managers who might control OPP, devices and means of transport employed therein. The outcomes and implications of the contribution are connected to presentation of selected possibilities in OPP analyses, which might be developed and solved within the model. The presented model has some limitations. One of them is assumption that one mean of transport per one aisle is taken into consideration. Another limitation is the indirectly randomization of certain model’s parameters." @default.
• W3015917329 created "2020-04-17" @default.
• W3015917329 creator A5031839774 @default.
• W3015917329 date "2020-04-09" @default.
• W3015917329 modified "2023-10-10" @default.
• W3015917329 title "Sensitivity Analysis of Selected Parameters in the Order Picking Process Simulation Model, with Randomly Generated Orders" @default.
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• W3015917329 doi "https://doi.org/10.3390/e22040423" @default.
• W3015917329 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7516899" @default.
• W3015917329 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33286197" @default.
• W3015917329 hasPublicationYear "2020" @default.
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