Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.8
3.9
Journals
Sustainability
Special Issues
Scheduling, Optimization of Production Systems and Equipment Maintenance: Towards Efficient...
sustainability-logo
Submit to Sustainability Review for Sustainability Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Scheduling, Optimization of Production Systems and Equipment Maintenance: Towards Efficient and Sustainable Manufacturing

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 2348

Special Issue Editors

Dr. Jiawen Hu

E-Mail Website
Guest Editor
School of Aeronautics and Astronautics, University of Electronic Science and Technology of China, Chengdu 611731, China
Interests: maintenance planning; job scheduling; degradation modeling
Dr. Piao Chen

E-Mail Website
Guest Editor
Department of Applied Mathematics, Delft University of Technology, 2600 AA Delft, The Netherlands
Interests: complex system modelling; maintenance planning; reliability and resilience
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Efficient and sustainable manufacturing is an effective method for achieving energy conservation and green production. It has received significant attention from both academia and industry in recent decades. Job scheduling and maintenance planning both play an important role in efficient and sustainable manufacturing. This Special Issue aims to attract the latest ideas and emerging research related to job scheduling and maintenance planning. It aims not only to advance the state-of-the-art methodologies but also their novel applications in solving real world problems, which is fully within the scope of the journal.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Production planning;
  • Job scheduling;
  • Smart factory;
  • Industry 4.0;
  • Maintenance and reliability modelling;
  • Reliability and maintenance engineering;
  • Degradation modelling and reliability assessment;
  • Diagnostics and prognostics;
  • Fault tolerance and safety critical systems;
  • Physical models for systems reliability;
  • Prognostics and health management.

We look forward to receiving your contributions.

Dr. Jiawen Hu
Dr. Piao Chen
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

  • job scheduling
  • maintenance planning
  • degradation modeling
  • reliability modeling
  • prognostics and health management

Published Papers (2 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

13 pages, 1771 KiB  
Article
Leveraging Classical Statistical Methods for Sustainable Maintenance in Automotive Assembly Equipment
by Juan Bucay-Valdiviezo, Pedro Escudero-Villa, Jenny Paredes-Fierro and Manuel Ayala-Chauvin
Sustainability 2023, 15(21), 15604; https://0-doi-org.brum.beds.ac.uk/10.3390/su152115604 - 03 Nov 2023
Cited by 1 | Viewed by 845
Abstract
Predictive maintenance management plays a crucial role in ensuring the reliable operation of equipment in industry. While continuous monitoring technology is available today, equipment without sensors limits continuous equipment state data recording. Predictive maintenance has been effectively carried out using artificial intelligence algorithms [...] Read more.
Predictive maintenance management plays a crucial role in ensuring the reliable operation of equipment in industry. While continuous monitoring technology is available today, equipment without sensors limits continuous equipment state data recording. Predictive maintenance has been effectively carried out using artificial intelligence algorithms for datasets with sufficient data. However, replicating these results with limited data is challenging. This work proposes the use of time series models to implement predictive maintenance in the equipment of an automotive assembly company with few records available. For this purpose, three models are explored—Holt–Winters Exponential Smoothing (HWES), Autoregressive Integrated Moving Average (ARIMA), and Seasonal Autoregressive Integrated Moving Average (SARIMA)—to determine the most accurate forecasting of future equipment downtime and advocate the use of SAP PM for effective maintenance process management. The data were obtained from five equipment families from January 2020 to December 2022, representing 36 registers for each piece of equipment. After data fitting and forecasting, the results indicate that the SARIMA model best fits seasonal characteristics, and the forecasting offers valuable information to help in decision-making to avoid equipment downtime, despite having the highest error. The results were less favorable when handling datasets with random components, requiring model recalibration for short-term forecasting. Full article
(This article belongs to the Special Issue Scheduling, Optimization of Production Systems and Equipment Maintenance: Towards Efficient and Sustainable Manufacturing)
►▼ Show Figures

Figure 1

19 pages, 3797 KiB  
Article
Hybrid Flow-Shop Scheduling Problems with Missing and Re-Entrant Operations Considering Process Scheduling and Production of Energy Consumption
by Hongtao Tang, Jiahao Zhou, Yiping Shao and Zhixiong Yang
Sustainability 2023, 15(10), 7982; https://0-doi-org.brum.beds.ac.uk/10.3390/su15107982 - 13 May 2023
Viewed by 1074
Abstract
A hybrid flow shop scheduling model with missing and re-entrant operations was designed to minimize the maximum completion time and the reduction in energy consumption. The proposed dual-population genetic algorithm was enhanced with a range of improvements, which include the design of a [...] Read more.
A hybrid flow shop scheduling model with missing and re-entrant operations was designed to minimize the maximum completion time and the reduction in energy consumption. The proposed dual-population genetic algorithm was enhanced with a range of improvements, which include the design of a three-layer gene coding method, hierarchical crossover and mutation techniques, and the development of an adaptive operator that considered gene similarity and chromosome fitness values. The optimal and worst individuals were exchanged between the two subpopulations to improve the exploration ability of the algorithm. An orthogonal experiment was performed to obtain the optimal horizontal parameter set of the algorithm. Furthermore, an experiment was conducted to compare the proposed algorithm with a basic genetic algorithm, particle swarm optimization algorithm, and ant colony optimization, which were all performed on the same scale. The experimental results show that the fitness value of the proposed algorithm is above 15% stronger than the other 4 algorithms on a small scale, and was more than 10% stronger than the other 4 algorithms on a medium and large scale. Under the condition close to the actual scale, the results of ten repeated calculations showed that the proposed algorithm had higher robustness. Full article
(This article belongs to the Special Issue Scheduling, Optimization of Production Systems and Equipment Maintenance: Towards Efficient and Sustainable Manufacturing)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop