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A Constructive Algorithm For Order List Generation Based On SKUs Co-Occurrence

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Název: A Constructive Algorithm For Order List Generation Based On SKUs Co-Occurrence
Autoři: Francesco Zammori, Giovanni Romagnoli, Francesco Moroni
Zdroj: ECMS 2025 Proceedings edited by Marco Scarpa, Salvatore Cavalieri, Salvatore Serrano, Fabrizio De Vita. :77-83
Informace o vydavateli: ECMS, 2025.
Rok vydání: 2025
Popis: 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.
Druh dokumentu: Article
DOI: 10.7148/2025-0077
Přístupové číslo: edsair.doi...........e013f6d02f48a072eae9400a4519b28e
Databáze: OpenAIRE
Popis
Abstrakt: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