Year: 2025
Type: conference-paper
Abstract: Optimizing picking operations in warehouses often involves allocating Stock-Keeping Units (SKUs) based on their co-occurrence patterns in order lists. Discrete event simulation is a powerful method for evaluating the effectiveness of these ‘correlated storage’ strategies. However, achieving reliable simulation results requires a robust demand generator capable of producing order lists that accurately reflect both the marginal and conditional probabilities of SKUs’ occurrences. Developing such a generator is challenging due to the complexity of modeling real-world co-occurrence relationships while maintaining probabilistic consistency. This paper presents an algorithm that generates synthetic order lists by integrating both marginal and conditional SKU dependencies. The proposed method ensures that the generated lists closely mirror realistic demand patterns observed in practical settings, making it a valuable tool for simulation-based studies.
DOI: 10.7148/2025-0077
Institution: University of Parma
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4413082396