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Tracking Tagged Inventory in Unstructured Environments through Probabilistic Dependency Graphs

Author

Listed:
  • Mabaran Rajaraman

    (Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • Glenn Philen

    (Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

  • Kenji Shimada

    (Department of Mechanical Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA)

Abstract

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.

Suggested Citation

  • Mabaran Rajaraman & Glenn Philen & Kenji Shimada, 2019. "Tracking Tagged Inventory in Unstructured Environments through Probabilistic Dependency Graphs," Logistics, MDPI, vol. 3(4), pages 1-23, September.
    • Handle: RePEc:gam:jlogis:v:3:y:2019:i:4:p:21-:d:269275
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    References listed on IDEAS

    as
    1. R. Navon & O. Berkovich, 2006. "An automated model for materials management and control," Construction Management and Economics, Taylor & Francis Journals, vol. 24(6), pages 635-646.
    2. Zakaria Dakhli & Zoubeir Lafhaj, 2018. "Considering Materials Management in Construction: An Exploratory Study," Logistics, MDPI, vol. 2(1), pages 1-13, March.
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    Cited by:

    1. Mabaran Rajaraman & Kyle Bannerman & Kenji Shimada, 2020. "Inventory Tracking for Unstructured Environments via Probabilistic Reasoning," Logistics, MDPI, vol. 4(3), pages 1-29, July.

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