<?xml version="1.0" encoding="UTF-8"?>
<feed xmlns="http://www.w3.org/2005/Atom" xmlns:dc="http://purl.org/dc/elements/1.1/">
  <title>EconStor Community:</title>
  <link rel="alternate" href="https://hdl.handle.net/10419/188366" />
  <subtitle />
  <id>https://hdl.handle.net/10419/188366</id>
  <updated>2026-04-28T14:09:23Z</updated>
  <dc:date>2026-04-28T14:09:23Z</dc:date>
  <entry>
    <title>Special Issue: Smart manufacturing for sustainability:trends and research challenges</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/261784" />
    <author>
      <name>Di Pasquale, Valentina</name>
    </author>
    <author>
      <name>Franciosi, Chiara</name>
    </author>
    <author>
      <name>Iannone, Raffaele</name>
    </author>
    <author>
      <name>Miranda, Salvatore</name>
    </author>
    <id>https://hdl.handle.net/10419/261784</id>
    <updated>2023-12-30T02:57:26Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Special Issue: Smart manufacturing for sustainability:trends and research challenges
Authors: Di Pasquale, Valentina; Franciosi, Chiara; Iannone, Raffaele; Miranda, Salvatore</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Optimization strategies for the integrated management of perishable supply chains: A literature review</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/261787" />
    <author>
      <name>Mirabelli, Giovanni</name>
    </author>
    <author>
      <name>Solina, Vittorio</name>
    </author>
    <id>https://hdl.handle.net/10419/261787</id>
    <updated>2023-12-05T02:22:54Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Optimization strategies for the integrated management of perishable supply chains: A literature review
Authors: Mirabelli, Giovanni; Solina, Vittorio
Abstract: Purpose: The main purpose of this article is to systematically review the papers published in the period 2005-2020 about the integration of production, inventory and distribution activities in perishable supply chains. Design/methodology/approach: The proposed research methodology is based on several steps. First, database and keywords are selected, with the aim to search and collect the main papers, dealing with the integration of production, inventory, distribution activities in perishable supply chains. Then, a bibliometric analysis is carried out, to detect: the main publishing sources, the chronological distribution, the most used keywords, the featured authors, about the selected papers. A five-dimension classification framework is proposed to carry out a content analysis, where the papers of the literature review are classified and discussed, according to: supply chain structure, objective, perishability type, solution approach, approach validation. Findings: Interest in the application of optimization models for integrated decision-making along perishable supply chains is strongly growing. Integrating multiple stages of the supply chain into a single framework is complex, especially when referring to perishable products. The vast majority of the problems addressed are then NP-Hard. Only a limited quantity of the selected papers aims to solve real-life case studies. There is a need for further research, which is capable of modeling and quantitatively improving existing supply chains. The potentials of Industry 4.0 are currently little explored. Originality/value: Based on the analysis of the papers published, this article outlines the current state of the art on the optimization strategies for the integrated management of perishable supply chains, which are very complex to be managed. Research trends and gaps are discussed, future challenges are presented.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/261786" />
    <author>
      <name>Drakaki, Maria</name>
    </author>
    <author>
      <name>Karnavas, Yannis L.</name>
    </author>
    <author>
      <name>Tziafettas, Ioannis A.</name>
    </author>
    <author>
      <name>Linardos, Vasilis</name>
    </author>
    <author>
      <name>Tzionas, Panagiotis</name>
    </author>
    <id>https://hdl.handle.net/10419/261786</id>
    <updated>2023-12-07T02:22:34Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Machine learning and deep learning based methods toward industry 4.0 predictive maintenance in induction motors: State of the art survey
Authors: Drakaki, Maria; Karnavas, Yannis L.; Tziafettas, Ioannis A.; Linardos, Vasilis; Tzionas, Panagiotis
Abstract: Purpose: Developments in Industry 4.0 technologies and Artificial Intelligence (AI) have enabled data-driven manufacturing. Predictive maintenance (PdM) has therefore become the prominent approach for fault detection and diagnosis (FD/D) of induction motors (IMs). The maintenance and early FD/D of IMs are critical processes, considering that they constitute the main power source in the industrial production environment. Machine learning (ML) methods have enhanced the performance and reliability of PdM. Various deep learning (DL) based FD/D methods have emerged in recent years, providing automatic feature engineering and learning and thereby alleviating drawbacks of traditional ML based methods. This paper presents a comprehensive survey of ML and DL based FD/D methods of IMs that have emerged since 2015. An overview of the main DL architectures used for this purpose is also presented. A discussion of the recent trends is given as well as future directions for research. Design/methodology/approach: A comprehensive survey has been carried out through all available publication databases using related keywords. Classification of the reviewed works has been done according to the main ML and DL techniques and algorithms Findings: DL based PdM methods have been mainly introduced and implemented for IM fault diagnosis in recent years. Novel DL FD/D methods are based on single DL techniques as well as hybrid techniques. DL methods have also been used for signal preprocessing and moreover, have been combined with traditional ML algorithms to enhance the FD/D performance in feature engineering. Publicly available datasets have been mostly used to test the performance of the developed methods, however industrial datasets should become available as well. Multi-agent system (MAS) based PdM employing ML classifiers has been explored. Several methods have investigated multiple IM faults, however, the presence of multiple faults occurring simultaneously has rarely been investigated. Originality/value: The paper presents a comprehensive review of the recent advances in PdM of IMs based on ML and DL methods that have emerged since 2015.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
  <entry>
    <title>Approaches of production planning and control under industry 4.0: A literature review</title>
    <link rel="alternate" href="https://hdl.handle.net/10419/261785" />
    <author>
      <name>Herrmann, Jan-Phillip</name>
    </author>
    <author>
      <name>Tackenberg, Sven</name>
    </author>
    <author>
      <name>Padoano, Elio</name>
    </author>
    <author>
      <name>Gamber, Thilo</name>
    </author>
    <id>https://hdl.handle.net/10419/261785</id>
    <updated>2023-12-10T02:49:13Z</updated>
    <published>2022-01-01T00:00:00Z</published>
    <summary type="text">Title: Approaches of production planning and control under industry 4.0: A literature review
Authors: Herrmann, Jan-Phillip; Tackenberg, Sven; Padoano, Elio; Gamber, Thilo
Abstract: Purpose: Industry 4.0 technologies significantly impact how production is planned, scheduled, and controlled. Literature provides different classifications of the tasks and functions of production planning and control (PPC) like the German Aachen PPC model. This research aims to identify and classify current Industry 4.0 approaches for planning and controlling production processes and to reveal researched and unexplored areas of the model. It extends a reduced version that has been published previously in Procedia Computer Science (Herrmann, Tackenberg, Padoano &amp; Gamber, 2021) by presenting and discussing its results in more detail. Design/methodology/approach: In an exploratory literature review, we review and classify 48 publications on a full-text basis with the Aachen PPC model's tasks and functions. Two cluster analyses reveal researched and unexplored tasks and functions of the Aachen PPC model. Findings: We propose a cyber-physical PPC architecture, which incorporates current Industry 4.0 technologies, current optimization methods, optimization objectives, and disturbances relevant for realizing a PPC system in a smart factory. Current approaches mainly focus on production control using real-time information from the shop floor, part of in-house PPC. We discuss the different layers of the cyber-physical PPC architecture and propose future research directions for the unexplored tasks and functions of the Aachen PPC model. Research limitations/implications: Limitations are the strong dependence of results on search terms used and the subjective eligibility assessment and assignment of publications to the Aachen PPC model. The selection of search terms and the texts' interpretation is based on an individual's assessment. The revelation of unexplored tasks and functions of the Aachen PPC model might have a different outcome if the search term combination is parameterized differently. Originality/value: Using the Aachen PPC model, which holistically models PPC, the findings give comprehensive insights into the current advances of tools, methods, and challenges relevant to planning and controlling production processes under Industry 4.0.</summary>
    <dc:date>2022-01-01T00:00:00Z</dc:date>
  </entry>
</feed>

