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Logistics, Volume 3, Issue 4 (December 2019) – 2 articles

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23 pages, 9293 KiB  
Article
Tracking Tagged Inventory in Unstructured Environments through Probabilistic Dependency Graphs
by Mabaran Rajaraman, Glenn Philen and Kenji Shimada
Logistics 2019, 3(4), 21; https://doi.org/10.3390/logistics3040021 - 20 Sep 2019
Cited by 2 | Viewed by 4041
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 [...] Read more.
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. Full article
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20 pages, 1384 KiB  
Article
Are We Ready to Ride Autonomous Vehicles? A Pilot Study on Austrian Consumers’ Perspective
by Sophie Wintersberger, Muhammad Azmat and Sebastian Kummer
Logistics 2019, 3(4), 20; https://doi.org/10.3390/logistics3040020 - 20 Sep 2019
Cited by 32 | Viewed by 10310
Abstract
Automotive manufacturers are competing to be the first to introduce customer-ready autonomous vehicles. Some manufacturers are claiming to launch their first self-driving cars as early as 2020. Which all sounds very good and futuristic; however, the question arises, are customers even ready to [...] Read more.
Automotive manufacturers are competing to be the first to introduce customer-ready autonomous vehicles. Some manufacturers are claiming to launch their first self-driving cars as early as 2020. Which all sounds very good and futuristic; however, the question arises, are customers even ready to adopt this new technological advancement? Therefore, this pilot study is aimed at finding out the answer to this question in the Austrian market. This study discovers the standpoint of Austrian consumers concerning the acceptance of self-driving cars for daily usage and gives an overview of the current point of view regarding autonomous vehicles (AVs). The data for this study was collected using an online, user-friendly, Likert scale survey. The collected data were processed and analyzed for empirical significance in SPSS using Spearman’s rank correlation and the Mann–Whitney U test supported by descriptive analysis. The results of the study indicate that Austrian consumers are well aware of autonomous vehicles and their technology. However, they have specific concerns about reliability, cybersecurity, and futuristic car-sharing models. Therefore, these concerns about AVs should be addressed by auto manufactures in order to gain consumers’ trust and sell them a new form of mobility. Full article
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