Lights-Out Logistics

A special issue of Logistics (ISSN 2305-6290).

Deadline for manuscript submissions: closed (1 February 2023) | Viewed by 9440

Special Issue Editors


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Guest Editor
1. Research Group on Logistics and Defense Technology Management, General Jonas Žemaitis Military Academy of Lithuania, Šilo st. 5A, LT-10322 Vilnius, Lithuania
2. Business Management, Vilnius Gediminas Technical University (VILNIUS TECH), Sauletekio al. 11, LT-10233 Vilnius, Lithuania
Interests: logistics; supply chain management; modelling; integrating processes; 3 PL
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
1. Faculty of Economics and Administrative Sciences, Gaziantep University, 27310 Gaziantep, Turkey
2. Faculty of Economics, Management and Law, Khoja Akhmet Yassawi International Kazakh-Turkish University, 161200 Turkestan, Kazakhstan
Interests: supply chain management; logistics; industry 4.0; digitalization; 3 PL

E-Mail Website
Guest Editor
1. Research Group on Logistics and Defense Technology Management, General Jonas Žemaitis Military Academy of Lithuania, Šilo st. 5A, LT-10322 Vilnius, Lithuania
2. Business Management, Vilnius Gediminas Technical university (VILNIUS TECH), Sauletekio al. 11, LT-10233 Vilnius, Lithuania
Interests: logistics; sustainable transport development; sustainable urban development; social security; qualitative decision methods; quantitative decision methods
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

We invite you to contribute to a Special Issue of Logistics entitled "Lights-Out Logistics".

As technology advances, fully automated production processes are becoming more and more feasible as the norm, as many manufacturers' advantages outweigh the disadvantages. Driven by the need to produce faster, better, and cheaper, manufacturers of all sizes embrace various forms of automation in their quest to reduce costs, increase productivity and reduce response times. Automation is changing the face of manufacturing in ways previously unimaginable, from extensive production facilities almost entirely driven by robots to small workshops looking to improve a few key manufacturing processes. Fully self-driving plants are still not viable for most manufacturers, but more and more people are embracing automation on a smaller scale that makes sense for their business. This trend towards automation introduced us to a new concept: "Lights-Out." Automation in production, in particular, led to the development of "Lights-Out" manufacturing, which could operate 24 hours a day with minimal staff in the second and third shifts, and the concept was often associated with manufacturing. Considering that the term "Lights-Out" refers to a digitized, automated process that does not involve any human intervention, it can have an impact beyond manufacturing throughout supply chains. Many studies emphasize, albeit indirectly, the necessity of maximizing the intelligent use of technology, from automation to data analytics, especially in logistics processes. Practitioners use automated systems to make warehouses and distribution centers more flexible, provide optimum space utilization, and have similar efficiency concerns. Instead of trying to build a complete lighting plant, they are focusing on areas best suited to "Lights-Out" automation, such as material handling tasks. For example, no staff commuting with forklifts, no-load carriers inspecting products and picking up items on pallets, not even an electric light—just self-navigation with laser guidance systems, where high shelves, shuttles, elevators, robots, conveyors, and autonomous vehicles choose their path in the dark. They set up warehouses where they build, perceive obstacles and pass silently. At this point, the concept of 'Lights-Out Logistics' emerges. Furthermore, Lights-Out Logistics can help supply chains become more resilient in times of great uncertainty and disruption, such as financial crises and pandemics like COVID-19.

Of course, some questions arise and need to be investigated:

- Is the current technological development sufficient for lights-out logistics operations?

- Is the technological infrastructure, digital culture, level of expertise, and management approach in businesses suitable for lights-out logistics operations?

- Where does Lights-out logistics stand in industry 4.0 applications, and what could be its effects on supply chain processes?

These questions and many more are the reason why we are organizing this Special Issue that aims to discuss Lights-Out Logistics processes.

The Special Issue is oriented toward (but not limited to) the following topics: 

  • lights-out systems
  • warehouse automation systems
  • Industry 4.0
  • logistics collaboration
  • security aspects of logistics activities
  • challenges of networking
  • risk management
  • quality management systems
  • quality of customer service
  • sustainable success of business
  • changes in logistics structures
  • modelling
  • COVID-19 challenges

Prof. Dr. Ieva Meidutė-Kavaliauskienė
Dr. Şemsettin Çiğdem
Dr. Renata Činčikaitė
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Logistics is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • automatization
  • digitalization
  • robotization
  • new technologies
  • optimization
  • supply chain
  • logistics services
  • quality management
  • risk management
  • change management
  • warehousing
  • transportation

Published Papers (2 papers)

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Research

18 pages, 778 KiB  
Article
Industry 4.0 and Industrial Robots: A Study from the Perspective of Manufacturing Company Employees
by Şemsettin Çiğdem, Ieva Meidute-Kavaliauskiene and Bülent Yıldız
Logistics 2023, 7(1), 17; https://doi.org/10.3390/logistics7010017 - 15 Mar 2023
Cited by 4 | Viewed by 6735
Abstract
Background: Human–robot collaboration is essential for efficient manufacturing and logistics as robots are increasingly used. Using industrial robots as part of an automation system results in many competitive benefits, including improved quality, efficiency, productivity, and reduced waste and errors. When robots are used [...] Read more.
Background: Human–robot collaboration is essential for efficient manufacturing and logistics as robots are increasingly used. Using industrial robots as part of an automation system results in many competitive benefits, including improved quality, efficiency, productivity, and reduced waste and errors. When robots are used in production, human coworkers’ psychological factors can disrupt operations. This study aims to examine the effect of employees’ negative attitudes toward robots on their acceptance of robot technology in manufacturing workplaces. Methods: A survey was conducted with employees in manufacturing companies to collect data on their attitudes towards robots and their willingness to work with them. Data was collected from 499 factory workers in Istanbul using a convenience sampling method, which allowed for the measurement of variables and the analysis of their effects on each other. To analyze the data, structural equation modeling was used. Results: The results indicate that negative attitudes towards robots have a significant negative effect on the acceptance of robot technology in manufacturing workplaces. However, trust in robots was found to be a positive predictor of acceptance. Conclusions: These findings have important implications for manufacturing companies seeking to integrate robot technology into their operations. Addressing employees’ negative attitudes towards robots and building trust in robot technology can increase the acceptance of robots in manufacturing workplaces, leading to improved efficiency and productivity. Full article
(This article belongs to the Special Issue Lights-Out Logistics)
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16 pages, 3710 KiB  
Article
NARX Neural Network for Safe Human–Robot Collaboration Using Only Joint Position Sensor
by Abdel-Nasser Sharkawy and Mustafa M. Ali
Logistics 2022, 6(4), 75; https://doi.org/10.3390/logistics6040075 - 18 Oct 2022
Cited by 4 | Viewed by 1796
Abstract
Background: Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods: In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of [...] Read more.
Background: Safety is the very necessary issue that must be considered during human-robot collaboration in the same workspace or area. Methods: In this manuscript, a nonlinear autoregressive model with an exog-enous inputs neural network (NARXNN) is developed for the detection of collisions between a manipulator and human. The design of the NARXNN depends on the dynamics of the manipulator’s joints and considers only the signals of the position sensors that are intrinsic to the manipulator’s joints. Therefore, this network could be applied and used with any conventional robot. The data used for training the designed NARXNN are generated by two experiments considering the sinusoidal joint motion of the manipulator. The first experiment is executed using a free-of-contact motion, whereas in the second experiment, random collisions by human hands are performed with the robot. The training process of the NARXNN is carried out using the Levenberg–Marquardt algorithm in MATLAB. The evaluation and the effectiveness (%) of the developed method are investigated taking into account different data and conditions from the used data for training. The experiments are executed using the KUKA LWR IV manipulator. Results: The results prove that the trained method is efficient in estimating the external joint torque and in correctly detecting the collisions. Conclusions: Eventually, a comparison is presented between the proposed NARXNN and the other NN architectures presented in our previous work. Full article
(This article belongs to the Special Issue Lights-Out Logistics)
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