Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.3
Journals
Sustainability
Special Issues
Advances in Industrial Risk Analysis and Management
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Advances in Industrial Risk Analysis and Management

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

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 3439

Special Issue Editors

Dr. Qingqing Zhai

E-Mail Website
Guest Editor
School of Management, Shanghai University, Shanghai, China
Interests: industrial statistics; reliability engineering; degradation modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Zhiguo Zeng

E-Mail Website
Guest Editor
Centrale Supélec, Laboratory of Industrial Engineering, University of Paris-Saclay, 91190 Gif-sur-Yvette, France
Interests: characterization and modeling of the failure/repair/maintenance behavior of components; complex systems and their reliability; maintainability; prognostics; safety; vulnerability and security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The rapid development of industrial systems and techniques, including the Industrial Internet, the 5G technique, and automatic factories, has substantially changed the production mode and led to more sustainable manufacturing. Meanwhile, the more complicated techniques and systems also bring a significant challenge to the managers and operators on how to reliably and safely operate systems in an effective and efficient way. To avoid unexpected catastrophic failures and improve the sustainability of industrial production, the managers have to monitor, diagnose, and predict the operating state of the system, and schedule preventive and corrective maintenance plans. The recent development of sensor techniques, PHM methods, and artificial intelligence provides a feasible path towards this target. In particular, the emergence of Big Data in industrial applications, including system operation data, maintenance data, and warranty data, can help enhance risk identification, mitigation, and management. Therefore, we would like to invite the scholars in this area to contribute their recent works that deal with the risk identification, modeling, and management of industrial systems and applications. We welcome studies on data-driven methods equipped with intelligent algorithms, new real industrial data analysis, and new applications of existing methods.

Topics to be discussed in this Special Issue include (but are not limited to) the following:

  • Artificial intelligence in industrial applications;
  • Big Data and IoT applications;
  • Diagnostics and prognostics;
  • Life cycle cost analysis and optimization;
  • Maintenance models and strategies;
  • Prognostics and health management;
  • Risk analysis and management;
  • Security and dependability analysis;
  • Supply chain reliability and management;
  • Statistical quality control;
  • Warranty data analysis and management.

Dr. Qingqing Zhai
Dr. Zhiguo Zeng
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

  • Industrial risk analysis
  • data-driven methods
  • IoT
  • prognostics and health management
  • industrial statistics

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

15 pages, 754 KiB  
Article
Truck-Drone Pickup and Delivery Problem with Drone Weight-Related Cost
by Yang Xia, Tingying Wu, Beixin Xia and Junkang Zhang
Sustainability 2023, 15(23), 16342; https://doi.org/10.3390/su152316342 - 27 Nov 2023
Viewed by 1072
Abstract
Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system [...] Read more.
Truck-drone delivery is widely used in logistics distribution for achieving sustainable development, in which drone weight greatly affects transportation cost. Thus, we consider a new combined truck-drone pickup and delivery problem with drone weight-related cost in the context of last-mile logistics. A system of integer programming is formulated with the objective of minimizing the total cost of the drone weight-related cost, fixed vehicle cost and travel distance cost. An improved adaptive large neighborhood search algorithm (IALNS) is designed based on the characteristics of the problem, several effective destroy and repair operators are designed to explore the solution space, and a simulated annealing strategy is introduced to avoid falling into the local optimal solution. To evaluate the performance of the IALNS algorithm, 72 instances are randomly generated and tested. The computational results on small instances show that the proposed IALNS algorithm performs better than CPLEX both in efficiency and effectiveness. When comparing the truck-drone pickup and delivery problem with drone weight-related cost to the problem without drone weight-related cost, it is found that ignoring the drone weight constraints leads to an underestimate of the total travel cost by 12.61% based on the test of large instances. Full article
(This article belongs to the Special Issue Advances in Industrial Risk Analysis and Management)
►▼ Show Figures

Figure 1

16 pages, 2311 KiB  
Article
Design for Optimally Routing and Scheduling a Tow Train for Just-in-Time Material Supply of Mixed-Model Assembly Lines
by Beixin Xia, Mingyue Zhang, Yan Gao, Jing Yang and Yunfang Peng
Sustainability 2023, 15(19), 14567; https://doi.org/10.3390/su151914567 - 8 Oct 2023
Cited by 1 | Viewed by 922
Abstract
With the increase in product varieties, the combination of supermarkets and tow trains is being adopted by more automobile manufacturers for part feeding, especially in mixed-flow assembly lines. This paper focuses on the routing, scheduling, and loading problems of a single towed train [...] Read more.
With the increase in product varieties, the combination of supermarkets and tow trains is being adopted by more automobile manufacturers for part feeding, especially in mixed-flow assembly lines. This paper focuses on the routing, scheduling, and loading problems of a single towed train that transports parts from one supermarket to the workstation buffer in a mixed-flow assembly line and aims to optimize the loading of the tow train, the optimal delivery schedule and route, and the appropriate departure time to minimize shipping and line inventory costs. To enable part feeding in line with the just-in-time (JIT) principle, a new mixed-integer mathematical model from nonlinearity to linearity and a novel artificial immune genetic algorithm-based heuristic are proposed. Both methods can provide reasonable solutions compared by minimizing the route length and inventory level in terms of speed, and the genetic algorithm shows better performance on a large scale. Full article
(This article belongs to the Special Issue Advances in Industrial Risk Analysis and Management)
►▼ Show Figures

Figure 1

17 pages, 9509 KiB  
Article
An Accelerated-Based Evaluation Method for Corrosion Lifetime of Materials Considering High-Temperature Oxidation Corrosion
by Hongbin Zhang, Shuqiang Liu, Peibo Liang, Zhipeng Ye and Yaqiu Li
Sustainability 2023, 15(11), 9102; https://doi.org/10.3390/su15119102 - 5 Jun 2023
Viewed by 1044
Abstract
In the realm of industrial automation, corrosion represents one of the primary modes of failure, especially in the case of armored thermocouples exposed to temperatures ranging between 1073.15–1373.15 K. In this context, the selection of metal materials that can withstand high-temperature oxidation and [...] Read more.
In the realm of industrial automation, corrosion represents one of the primary modes of failure, especially in the case of armored thermocouples exposed to temperatures ranging between 1073.15–1373.15 K. In this context, the selection of metal materials that can withstand high-temperature oxidation and corrosion is of paramount importance. Typically, the corrosion resistance of a given metal material is assessed by measuring the “annual corrosion rate” or “corrosion depth”, which can provide an estimated life expectancy value. However, such an approach fails to account for the individual characteristics of the material, and thus does not conform to objective laws. Rather, the corrosion life of a batch of metallic materials should follow the Weibull distribution, or possibly a normal distribution, as per recent studies that have examined the high-temperature oxidation corrosion mechanism of machine or core components. This investigation effectively combines the standard approach for evaluating metal corrosion resistance in the field of materials with the method of assessing component life in the domain of reliability. Furthermore, we consider the individual differences among materials and provide the life distribution function of metals in corrosive environments and thereby refine the evaluation of metal corrosion resistance. This study ultimately establishes a thermocouple accelerated life evaluation model that enhances the accuracy and efficiency of life evaluations for related products. Full article
(This article belongs to the Special Issue Advances in Industrial Risk Analysis and Management)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop