Optimal Location Assignment for Data-Driven Warehouse Towards Digital Supply Chain Twin

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dc.contributor.authorErel-Özçevik, M
dc.contributor.authorÖzçevik, Y
dc.contributor.authorBozkaya, E
dc.contributor.authorBilen, T
dc.date.accessioned2025-04-10T10:31:54Z
dc.date.available2025-04-10T10:31:54Z
dc.description.abstractWarehouses, as one of the critical components of supply chain management in Industry 4.0, play an important role in e-commerce operational efficiency. A crucial prerequisite for managing warehouses is to decide the locations of products (blocks) that can maximize overall space utilization, called a Block Location Problem (BLP). BLP basically determines the product locations to achieve maximum space utilization. One of the most innovative approaches to solving BLP is the use of drones as a block transportation strategy. Existing works have been mainly focused on 2D grid models while 3D flight movement is ignored. Thus, in this paper, we develop a novel data-driven warehouse model for digital supply chain twins. For this purpose, a warehouse digital twin (WDT) architecture is defined by creating a virtual replica of a warehouse that contains the features and interactions of its real-world counterpart. Then, we formalize the BLP in a 3D grid model to decide the location of blocks in a warehouse and to provide efficient space utilization by minimizing the energy consumption of drone cargo equipment. Finally, we propose a genetic algorithm-based solution to solve the storage location assignment. Performance evaluation results demonstrate that our proposed algorithm achieves more block utilization and less energy consumption when compared to the greedy solution.
dc.identifier.e-issn2378-4873
dc.identifier.issn2378-4865
dc.identifier.urihttp://hdl.handle.net/20.500.14701/38354
dc.language.isoEnglish
dc.titleOptimal Location Assignment for Data-Driven Warehouse Towards Digital Supply Chain Twin
dc.typeProceedings Paper

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