Erel-Ozcevik M.Ozcevik Y.Bozkaya E.Bilen T.2024-07-222024-07-22202323784873http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12157Warehouses, 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. © 2023 IEEE.English3D modelingDigital storageDronesElectronic commerceGenetic algorithmsIndustry 4.0LocationSupply chain managementWarehousesBlock location problemCritical componentData drivenDigital supply chainEnergy-consumptionGrid modelLocation assignmentLocation problemsOptimal locationsSpace utilizationEnergy utilizationConference paper10.1109/CAMAD59638.2023.10478382