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Actual Trends of Logistics and Industrial Engineering

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Transportation".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 9570

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Special Issue Editors

Prof. Vieroslav Molnár

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Department of Computer Aided Manufacturing Technologies, Faculty of Manufacturing Technologies with a seat in Prešov, Technical University of Košice, Bayerova 1, 080 01 Prešov, Slovak Republic
Interests: logistics; production technologies and technical preparation of production; designing, analyzing, and simulating various components of technical equipment
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Gabriel Fedorko

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Institute of logistics and transport Faculty of Mining, Ecology, Process Control and Geotechnologies of the Technical University of Košice, Letná 9, 040 01 Košice, Slovak Republic
Interests: logistics; computer simulation; FEM; mechanical; production technologies; virtual reality; exponential technologies

Special Issue Information

Dear Colleagues,

This Special Issue is dedicated to sustainability in the field of logistics and production engineering. Actual Trends of Logistics and Industrial Engineering presents the issues that play a significant role in different industries. It reflects the latest knowledge that fully reflects the needs of science and industrial practice. In order to achieve sustainability in the field of logistics and production engineering, it is necessary to reflect on current questions arising from the latest technologies and methodological approaches.

Another major challenge for this area is Industry 4.0 and the associated implementation of its individual approaches into application practice. The exchange of experience and information plays an important role in this context. This Special Issue will create a space that will provide opportunities for the presentation of the selected problem areas related to sustainability in the area of logistics and production engineering. The aim is to create a link between theory and practice.

It is about presenting not only scientific approaches and solutions but also presenting their implications in practice using case studies. This Special Issue welcomes contributions that marginally relate to this field, or have an interdisciplinary character.

It is my pleasure to invite you to publish your existing research works in the Special Issue, ‘Actual Trends of Logistics and Industrial Engineering’, of Sustainability as a full paper, short communication, or review.

The authors are advised to submit innovative applications of research solutions. Trends of logistics and industrial engineering, analyzed in the research, need to be well depicted from an application point of view, and a particular emphasis needs to be given to their economic cost.

Potential topics include but are not limited to the following:

Applied mathematics and operations research methods in terms of logistics and industrial engineering
Innovative optimization methods within industrial engineering
Innovative information systems and smart technologies application within logistics processes
Current trends in industrial engineering
Measurement, sensors, monitoring, and diagnostic systems of industrial engineering
Cost–benefit analysis in the context of regional logistics
Monitoring, maintenance, control, and tests within industrial engineering
Case study of AGV systems
Advances of complementary programs of simulation models of logistics processes
Practical application of logistics processes and their design elements

Prof. Vieroslav Molnár
Prof. Gabriel Fedorko
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. Sustainability is an international peer-reviewed open access semimonthly 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 2400 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

logistics
simulation
production
process
transport
industry 4.0
engineering
optimization
ecology

Published Papers (3 papers)

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Research

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17 pages, 41121 KiB

Open AccessArticle

The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

by Gabriel Fedorko, Vieroslav Molnár and Nikoleta Mikušová

Sustainability 2020, 12(23), 9818; https://0-doi-org.brum.beds.ac.uk/10.3390/su12239818 - 24 Nov 2020

Cited by 6 | Viewed by 2588

Abstract

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity. Full article

(This article belongs to the Special Issue Actual Trends of Logistics and Industrial Engineering)

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16 pages, 807 KiB

Open AccessArticle

Shipment Policy for an Economic Production Quantity Model Considering Imperfection and Transportation Cost

by Mubashir Hayat, Bashir Salah, Misbah Ullah, Iftikhar Hussain and Razaullah Khan

Sustainability 2020, 12(21), 8964; https://0-doi-org.brum.beds.ac.uk/10.3390/su12218964 - 28 Oct 2020

Cited by 2 | Viewed by 1638

Abstract

Determining replenishment lot size and number of shipments in a traditional production setup has been of great interest among researchers during the last decades. In order to survive modern competition, the manufacturer has to make good decisions about the lot size that is to be shipped to the retailer. Recently, several researchers have developed mathematical models for modelling different real-world situations; however, these models are lacking due to a combination of imperfection in process and shipment lot sizing. Therefore, in the proposed research, shipment policy for an imperfect production setup has been developed with transportation costs taken into consideration. The model analyzed lot sizing for manufacturers and retailers with imperfections in terms of equally sized shipments. Furthermore, an all-unit-discount policy for shipment is considered in the proposed research, and at the end, numerical computation and sensitivity analyses are carried out to gain more insight into the specifications of the model. Full article

(This article belongs to the Special Issue Actual Trends of Logistics and Industrial Engineering)

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Other

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20 pages, 2387 KiB

Open AccessEditor’s ChoiceCase Report

Innovative Methods for Small Mixed Batches Production System Improvement: The Case of a Bakery Machine Manufacturer

by Kristina Zgodavova, Peter Bober, Vidosav Majstorovic, Katarina Monkova, Gilberto Santos and Darina Juhaszova

Sustainability 2020, 12(15), 6266; https://0-doi-org.brum.beds.ac.uk/10.3390/su12156266 - 04 Aug 2020

Cited by 39 | Viewed by 4649

Abstract

One of the common problems of organizations with turn-key projects is the high scrap rate. There exist such traditional methods as Lean Six Sigma (LSS) and DMAIC tools that analyze causes and suggest solutions. New emerging intelligent technologies should influence these methods and tools as they affect many areas of our life. The purpose of this paper is to present the innovative Small Mixed Batches (SMB). The standard set of LSS tools is extended by intelligent technologies such as artificial neural networks (ANN) and machine learning. The proposed method uses the data-driven quality strategy to improve the turning process at the bakery machine manufacturer. The case study shows the step-by-step DMAIC procedure of critical to quality (CTQ) characteristics improvement. Findings from the data analysis lead to a change of measurement instrument, training of operators, and lathe machine set-up correction. However, the scrap rate did not decrease significantly. Therefore the advanced mathematical model based on ANN was built. This model predicts the CTQ characteristics from the inspection certificate of the input material. The prediction model is a part of a newly designed process control scheme using machine learning algorithms to reduce the variability even for input material with different properties from new suppliers. Further research will be focused on the validation of the proposed control scheme, and acquired experiences will be used to support business sustainability. Full article

(This article belongs to the Special Issue Actual Trends of Logistics and Industrial Engineering)

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