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Dear Colleagues,

Advanced industrial engineering (AIE) is a major direction of current and future technological development, and also a strategy of European development and research. AIE arises from the need to apply new innovative technologies to create a better position for production companies in global and knowledge-based society. Special attention is continuously given to the problems of new manufacturing technologies and modern conceptions of manufacturing systems that allow us to make high-quality products with a high level of effectiveness and a proper level of flexibility. Given the importance of sustainable development and the rapid development of new literature in this field, this Special Issue will enrich the literature with a wide range of discussions, contributing to the development of further research in the field of advanced industrial engineering, sustainable development in industry and other relevant topics.

Therefore, we would like to invite you to submit a research paper for the Special Issue “Advanced Industrial Engineering: Innovation, Risk and Flexible Manufacturing”.

This Special Issue seeks high-quality works focusing on the following topics:

Management;
Sustainable development;
Industrial assembly technologies;
Manufacturing engineering of composite materials;
Manufacturing systems design for industrial applications;
Paradigms of modern manufacturing system designs;
Flexible and focused manufacturing systems;
Reconfigurable manufacturing systems and other manufacturing concepts of the future;
Advanced industrial engineering;
Manufacturing system capacity balancing;
Sustainable material-handling systems.

I/We look forward to receiving your contributions.

Dr. Vladimíra Biňasová
Prof. Dr. Branislav Micieta
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

sustainable development
Industry 4.0
innovation
management
industrial engineering
green knowledge
advanced industrial engineering
management
economics

Published Papers (2 papers)

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Research

25 pages, 939 KiB

Open AccessArticle

The Innovation Plight and Operational Efficiency of Chinese Manufacturing Enterprises: From the Perspective of Risk Tolerance, Expectation, and Profitability

by Yanfei Shu and Yaxin Yang

Sustainability 2024, 16(12), 4916; https://doi.org/10.3390/su16124916 - 7 Jun 2024

Viewed by 156

Abstract

With uncertainty intensifying the international technological innovation environment, the innovation situation of Chinese manufacturing enterprises has been impacted. Based on 12,781 micro panel data in 2011–2020 of 2347 Chinese A-share manufacturing listed enterprises, this paper empirically analyzes the impact of innovation plight faced by enterprises on operational efficiency. The innovation plight in this article refers to the degree to which the actual innovation performance of enterprises has not reached the expected innovation performance or is introduced by an innovation gap, measured by the difference when actual innovation performance is lower than the expected innovation performance. The empirical results show that the innovation plight of manufacturing enterprises significantly inhibits operational efficiency by reducing their risk tolerance, development ability expectation, and profitability. After using a series of tests, such as the instrumental variable method, replacing the dependent variable, and changing the parameters for measuring the independent variable, the conclusion is still robust. In addition, the results illustrate that the inhibitory effect of innovation plight on operational efficiency is more obvious for non-state-owned enterprises, small and medium-sized enterprises, high-tech enterprises, and enterprises in the eastern region. Finally, we formulate some relevant management suggestions. Full article

(This article belongs to the Special Issue Advanced Industrial Engineering: Innovation, Risk and Flexible Manufacturing 2nd Edition)

29 pages, 7985 KiB

Open AccessArticle

Towards Sustainable Production: An Adaptive Intelligent Optimization Genetic Algorithm for Solid Wood Panel Manufacturing

by Jingzhe Yang, Yili Zheng and Jian Wu

Sustainability 2024, 16(9), 3785; https://doi.org/10.3390/su16093785 - 30 Apr 2024

Viewed by 599

Abstract

Optimizing production processes to conserve resources and reduce waste has become crucial in pursuing sustainable manufacturing practices. The solid wood panel industry, marked by substantial raw materials and energy consumption, stands at the forefront of addressing this challenge. This research delves into production scheduling and equipment utilization inefficiencies, offering innovative solutions for the solid wood panel processing line aimed at achieving environmental sustainability and operational efficiency. The study is articulated through two main segments: (1) an exhaustive analysis and the development of a simulation system for the solid wood panel processing line, delineating all production elements and operational logic, furnished with a user-friendly simulation interface, and (2) a comprehensive evaluation and enhancement of various scheduling algorithms specific to the Flexible Job-Shop Scheduling Problem (FJSP) encountered in solid wood panel workshops. A significant leap forward is made with the introduction of the Adaptive Intelligent Optimization Genetic Algorithm (AIOGA), an evolved version of the standard Genetic Algorithm (GA) engineered for optimal scheduling within the solid wood panel processing line. AIOGA incorporates advanced features such as encoding strategy, population initialization, objective function setting, selection strategy, crossover operation, and mutation operation, demonstrating the methodological depth of the study. We applied AIOGA in a designed FJSP, and AIOGA substantially reduced the maximum completion time to 90 min. It evidenced an improvement of 39.60% over the conventional GA, enhancing the equilibrium of the equipment workload across the system. This research presents a multifaceted strategy to address the scheduling complications inherent in solid wood panel production and highlights the extensive applicability of adaptive intelligent optimization in diverse industrial settings. This study establishes a new paradigm in manufacturing optimization, underlining the valuable integration of sustainability and efficiency in production methodologies. Full article

(This article belongs to the Special Issue Advanced Industrial Engineering: Innovation, Risk and Flexible Manufacturing 2nd Edition)

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