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    <title>EconStor Collection:</title>
    <link>https://hdl.handle.net/10419/209186</link>
    <description />
    <pubDate>Sat, 02 May 2026 17:39:45 GMT</pubDate>
    <dc:date>2026-05-02T17:39:45Z</dc:date>
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      <title>Workload forecasting of a logistic node using Bayesian neural networks</title>
      <link>https://hdl.handle.net/10419/267188</link>
      <description>Title: Workload forecasting of a logistic node using Bayesian neural networks
Authors: Nakilcioğlu, Emin; Rizvanolli, Anisa; Rendel, Olaf
Abstract: Purpose: Traffic volume in empty container depots has been highly volatile due to external factors. Forecasting the expected container truck traffic along with having a dynamic module to foresee the future workload plays a critical role in improving the work efficiency. This paper studies the relevant literature and designs a forecasting model addressing the aforementioned issues. Methodology: The paper develops a forecasting model to predict hourly work and traffic volume of container trucks in an empty container depot using a Bayesian Neural Network based model. Furthermore, the paper experiments with datasets with different characteristics to assess the model's forecasting range for various data sources. Findings: The real data of an empty container depot is utilized to develop a forecasting model and to later verify the capabilities of the model. The findings show the performance validity of the model and provide the groundwork to build an effective traffic and workload planning system for the empty container depot in question. Originality: This paper proposes a Bayesian deep learning-based forecasting model for traffic and workload of an empty container depot using real-world data. This designed and implemented forecasting model offers a solution with which every actor in the container truck transportation benefits from the optimized workload.</description>
      <pubDate>Sat, 01 Jan 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/267188</guid>
      <dc:date>2022-01-01T00:00:00Z</dc:date>
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    <item>
      <title>Drone based delivery system: Restrictions and limitations</title>
      <link>https://hdl.handle.net/10419/267192</link>
      <description>Title: Drone based delivery system: Restrictions and limitations
Authors: Seidakhmetov, Azamat; Valilai, Omid Fatahi
Abstract: Purpose: Considering the idea of drone application in last mile delivery, this paper has examined literature studies about the restrictions and pitfalls which the organizations will face with for using drones in the last mile delivery. Moreover, the recent drone tests for commercial purposes in last mile delivery industry and challenges in these practices are investigated. Methodology: A survey-based approach has been applied to both the potential customers of the drone delivery service and also literature review for discovery of latest practices for drone enabled delivery projects has been targeted to identify the limitations and restrictions. Findings: The study demonstrates the problems that arise when the delivery drone crushes while in the air and the approach of self-exploding drones does not seem to make positive effect on this problem. Also, issues like special area for landing, noise of drone activities, safety and security of citizens in urban area are found to be main concerns. Originality: Very few research studies have been conducted in evaluation of using drones for last mile delivery operations focusing on current limitations and forthrightly downsides of drones of current state of the art. Therefore, this paper has tried to elaborate the limitations and restrictions from two perspectives of potential customers and technology developers.</description>
      <pubDate>Sat, 01 Jan 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/267192</guid>
      <dc:date>2022-01-01T00:00:00Z</dc:date>
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    <item>
      <title>Concept for material supply in fluid manufacturing systems</title>
      <link>https://hdl.handle.net/10419/267180</link>
      <description>Title: Concept for material supply in fluid manufacturing systems
Authors: Hagg, Manuel; Schulz, Robert
Abstract: Purpose: Through increasing individualization, volatile market demands or shorter product and innovation cycles, existing assembly systems in the automotive industry reach their limits. Different approaches are designed to address the need for adaptable systems. The Fluid Manufacturing System (FLMS), which has been developed within the research campus 'Active Research Environment for the Next generation of Automobiles' (ARENA2036), aims to enable flexible and dynamic material flows. However, material supply in this environment is challenging due to new degrees of freedom or volatile demands. Therefore, the purpose of this paper is to describe a concept for short-term-oriented material supply in FLMS. The concept focuses on checking and ensuring material availability. Methodology: The methodology that is used to derive the concept for material supply is based on a step-by-step procedure allowing a systematic concept development using a problem-solving-oriented approach. The concept procedure includes different steps like concept initiation or selection of a solution. Findings: The result of this paper outlines a concept that supports short-term-oriented material supply in FLMS. More detailed, the concept supports checking and ensuring material availability. Originality: The presented concept contributes to check and ensure material availability during the operation of assembly systems under changing conditions.</description>
      <pubDate>Sat, 01 Jan 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/267180</guid>
      <dc:date>2022-01-01T00:00:00Z</dc:date>
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    <item>
      <title>Structured literature review of transport networks and Supply Chain Resilience</title>
      <link>https://hdl.handle.net/10419/267196</link>
      <description>Title: Structured literature review of transport networks and Supply Chain Resilience
Authors: Gast, Johannes; Kirkach, Evelina; Ivanov, Dmitry
Abstract: Purpose: The literature on Supply Chain Resilience faces a steep and significant interest in recent years owing to the pandemic and disruptions in global trade. As the literature amplifies due, this paper aims to provide transparency about the intersection of Supply Chain Resilience and transport networks. Existing literature reviews do not consider this aspect. Methodology: This paper conducts a Systematic Literature Review using the keywords "Supply Chain Resilience" combined with "Transport/Infrastructure networks" to identify the relationship between transport networks and Supply Chain Resilience. The method, as a result, identifies about 251 articles from 2004 to 2022, of which 36 relevant papers are included. Findings: Excluded overview papers address Supply Chain Resilience and transport independently. As a result of the full-text screening, a shift to quantitative methods can be observed. Network theory and mathematical programming models stick out. However, a list of specific research gaps for future research proposed in the literature remains. In particular, a dedicated transport network like highways is seldomly considered. Originality: This research improves the understanding of the relationship between Supply Chain Resilience and transport networks with the interrelation of transport and supply chain disruptions.</description>
      <pubDate>Sat, 01 Jan 2022 00:00:00 GMT</pubDate>
      <guid isPermaLink="false">https://hdl.handle.net/10419/267196</guid>
      <dc:date>2022-01-01T00:00:00Z</dc:date>
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