Optimal Location Assignment for Data-Driven Warehouse Towards Digital Supply Chain Twin
dc.contributor.author | Erel-Ozcevik M. | |
dc.contributor.author | Ozcevik Y. | |
dc.contributor.author | Bozkaya E. | |
dc.contributor.author | Bilen T. | |
dc.date.accessioned | 2024-07-22T08:03:09Z | |
dc.date.available | 2024-07-22T08:03:09Z | |
dc.date.issued | 2023 | |
dc.description.abstract | Warehouses, 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. | |
dc.identifier.DOI-ID | 10.1109/CAMAD59638.2023.10478382 | |
dc.identifier.issn | 23784873 | |
dc.identifier.uri | http://akademikarsiv.cbu.edu.tr:4000/handle/123456789/12157 | |
dc.language.iso | English | |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
dc.subject | 3D modeling | |
dc.subject | Digital storage | |
dc.subject | Drones | |
dc.subject | Electronic commerce | |
dc.subject | Genetic algorithms | |
dc.subject | Industry 4.0 | |
dc.subject | Location | |
dc.subject | Supply chain management | |
dc.subject | Warehouses | |
dc.subject | Block location problem | |
dc.subject | Critical component | |
dc.subject | Data driven | |
dc.subject | Digital supply chain | |
dc.subject | Energy-consumption | |
dc.subject | Grid model | |
dc.subject | Location assignment | |
dc.subject | Location problems | |
dc.subject | Optimal locations | |
dc.subject | Space utilization | |
dc.subject | Energy utilization | |
dc.title | Optimal Location Assignment for Data-Driven Warehouse Towards Digital Supply Chain Twin | |
dc.type | Conference paper |