Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs
Logging and tracking raw materials, workpieces and engineered products for seamless and quick pulls is a complex task in the construction and shipbuilding industries due to lack of structured storage solutions. Additional uncertainty is introduced if workpieces are stacked and moved by multiple stak...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-09-01
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| Series: | Logistics |
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| Online Access: | https://www.mdpi.com/2305-6290/3/4/21 |
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| author | Mabaran Rajaraman Glenn Philen Kenji Shimada |
| author_facet | Mabaran Rajaraman Glenn Philen Kenji Shimada |
| author_sort | Mabaran Rajaraman |
| collection | DOAJ |
| description | Logging and tracking raw materials, workpieces and engineered products for seamless and quick pulls is a complex task in the construction and shipbuilding industries due to lack of structured storage solutions. Additional uncertainty is introduced if workpieces are stacked and moved by multiple stakeholders without maintaining an active and up-to-date log of such movements. While there are frameworks proposed to improve workpiece pull times using a variety of tracking modes based on deterministic approaches, there is little discussion of cases wherein direct observations are sparse due to occlusions from stacking and interferences. Our work addresses this problem by: logging visible part locations and timestamps, through a network of custom designed observation devices; and building a graph-based model to identify events that highlight part interactions and estimate stack formation to search for parts that are not directly observable. By augmenting the site workers and equipment with our wearable devices, we avoid adding additional cognitive effort for the workers. Native building blocks of the graph-based model were evaluated through simulations. Experiments were also conducted in an active shipyard to validate our proposed system. |
| format | Article |
| id | doaj-art-b51fd9b44ef741e69fadccdc4ce0fc0d |
| institution | Matheson Library |
| issn | 2305-6290 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Logistics |
| spelling | doaj-art-b51fd9b44ef741e69fadccdc4ce0fc0d2025-08-02T17:20:17ZengMDPI AGLogistics2305-62902019-09-01342110.3390/logistics3040021logistics3040021Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency GraphsMabaran Rajaraman0Glenn Philen1Kenji Shimada2Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USADepartment of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USALogging and tracking raw materials, workpieces and engineered products for seamless and quick pulls is a complex task in the construction and shipbuilding industries due to lack of structured storage solutions. Additional uncertainty is introduced if workpieces are stacked and moved by multiple stakeholders without maintaining an active and up-to-date log of such movements. While there are frameworks proposed to improve workpiece pull times using a variety of tracking modes based on deterministic approaches, there is little discussion of cases wherein direct observations are sparse due to occlusions from stacking and interferences. Our work addresses this problem by: logging visible part locations and timestamps, through a network of custom designed observation devices; and building a graph-based model to identify events that highlight part interactions and estimate stack formation to search for parts that are not directly observable. By augmenting the site workers and equipment with our wearable devices, we avoid adding additional cognitive effort for the workers. Native building blocks of the graph-based model were evaluated through simulations. Experiments were also conducted in an active shipyard to validate our proposed system.https://www.mdpi.com/2305-6290/3/4/21inventory managementwearable devicesindustry 4.0smart manufacturingconstruction technologyprobabilitygraphs |
| spellingShingle | Mabaran Rajaraman Glenn Philen Kenji Shimada Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs Logistics inventory management wearable devices industry 4.0 smart manufacturing construction technology probability graphs |
| title | Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs |
| title_full | Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs |
| title_fullStr | Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs |
| title_full_unstemmed | Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs |
| title_short | Tracking Tagged Inventory in Unstructured Environments Through Probabilistic Dependency Graphs |
| title_sort | tracking tagged inventory in unstructured environments through probabilistic dependency graphs |
| topic | inventory management wearable devices industry 4.0 smart manufacturing construction technology probability graphs |
| url | https://www.mdpi.com/2305-6290/3/4/21 |
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