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Enabling Technologies and Methods for Sustainable Remanufacturing System
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Enabling Technologies and Methods for Sustainable Remanufacturing System

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 September 2023) | Viewed by 10803

Special Issue Editors

Prof. Dr. Zhigang Jiang

E-Mail Website
Guest Editor
Department of Industrial Engineering, School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: green manufacturing and remanufacturing; energy efficient manufacturing; low carbon design and manufacturing
Special Issues, Collections and Topics in MDPI journals
Dr. Chao Ke

E-Mail
Guest Editor
School of Mechanical Automation, Wuhan University of Science and Technology, Wuhan 430081, China
Interests: green manufacturing and remanufacturing; intelligent design; low carbon design
Dr. Shuo Zhu

E-Mail Website
Guest Editor
Department of Industrial Engineering, School of Mechanical Automation,Wuhan University of Science and Technology, Wuhan 430081, China
Interests: modeling and optimization control for green manufacturing and remanufacturing system; energy efficient manufacturing
Dr. Yan Wang

E-Mail Website
Guest Editor
School of Computing, Engineering and Mathematics, University of Brighton, Brighton BN2 4GJ, UK
Interests: intelligent and sustainable manufacturing; sustainable design; remanufacturing; process and operation management; computer-aided design and manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the introduction of Industry 4.0 and the Chinese dual carbon goal, sustainable manufacturing has become the focus of widespread global attention. Remanufacturing is an effective way to achieve energy saving and emission reduction in manufacturing systems, and remanufacturing systems (RS) include many components, such as design for remanufacturing (DfRem), remanufacturing process planning (RPP), logistics and reverse supply chain, etc. However, remanufacturing systems will also consume large amounts of energy and materials, as well as produce significant carbon emissions. Therefore, it is necessary to apply the advanced technologies and methods to optimize and improve the DfRem, remanufacturing process and logistics, so as to improve the sustainability of the remanufacturing system, and this mission can be better achieved through the Internet-of-Things (IoT) and artificial intelligence (AI) for optimization and control in remanufacturing systems. This research topic focuses on the advanced enabling technologies and methods for sustainable remanufacturing system.

DfRem, remanufacturing process and reverse logistics are important components of the remanufacturing system, which also directly affect the energy and material consumption of the remanufacturing system. For example, DfRem directly affects the remanufacturability of the product and determines the reusable value of the used product, and the remanufacturing process planning directly affects the remanufacturing performance indicators, such as production efficiency, resource consumption, waste generation, etc. The reverse supply chain directly determines the recycling efficiency and utilization rate of the used products. As we all know, remanufacturing is an effective way to save energy and reduce emissions; however, in the era of mass customization production, how to quickly develop low-carbon and sustainable product design schemes, remanufacturing process solutions and reverse logistics path has become an urgent problem to be solved. With rapid development of machine intelligence, including deep learning, swarm intelligence and cognitive science, it plays a more and more important role to utilize the machine intelligence models and algorithms in RS.

The goal of this research topic is to explore scientific models, methods and technologies, with both solid theoretical development and practical importance to reshape remanufacturing systems, and transform remanufacturing modes. The central theme of the proposed research topic is on the enabling technologies and methods for sustainable remanufacturing systems, where information technology-based modelling, analysis, control and optimization are the focus areas, and broad aspects and issues will be well discussed. Topics to be covered include, but are not limited to, the following:

  • Low carbon product design method for remanufacturing;
  • Intelligent product design method for remanufacturing based on knowledge and data;
  • Low carbon manufacturing;
  • Data-driven remanufacturing scheme generation and optimization;
  • Low carbon remanufacturing process planning;
  • Intelligent remanufacturing process design;
  • Green logistics;
  • Green reverse supply chain.

Prof. Dr. Zhigang Jiang
Dr. Chao Ke
Dr. Shuo Zhu
Dr. Yan Wang
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 remanufacturing system
  • intelligent
  • low carbon product design
  • remanufacturing process
  • green logistics
  • green reverse supply chain

Published Papers (7 papers)

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Research

23 pages, 2449 KiB  
Article
A Framework of a Blockchain-Supported Remanufacturing Trading Platform through Gap Analysis
by Zhaohui Feng, Wei Li, Hua Zhang and Xumei Zhang
Sustainability 2023, 15(16), 12120; https://doi.org/10.3390/su151612120 - 08 Aug 2023
Viewed by 980
Abstract
Considering that consumers are more willing to buy products online, companies are increasingly selling remanufactured products online through e-commerce platforms. Notwithstanding the high attention it elicits from researchers and companies, the current study on the remanufacturing trading platform is still in its infancy. [...] Read more.
Considering that consumers are more willing to buy products online, companies are increasingly selling remanufactured products online through e-commerce platforms. Notwithstanding the high attention it elicits from researchers and companies, the current study on the remanufacturing trading platform is still in its infancy. Thus, we investigate 20 remanufacturing trading platforms and make a gap analysis among them in terms of (i) business model, (ii) product display, (iii) delivery products, (iv) quality assurance and after-sales service, (v) product review and star rate, and (vi) transaction and payment. On this basis, we analyze features for the development of remanufacturing trading platforms and propose six key applications aimed at filling the identified gaps. The consortium blockchain has the characteristics of security and transparency, high credibility, traceability and unfalsifiability, low cost, and strong scalability, which can provide effective support for the six key applications. Then, we construct the technical framework and the model of a consortium blockchain-supported remanufacturing trading platform. Further, we analyze the coupling mechanism between the consortium blockchain and the remanufacturing trading platform to explain how the remanufacturing trading platform supported by the consortium blockchain achieves the development characteristics. This study provides important guidance for the development, construction, and operation management of remanufacturing trading platforms. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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21 pages, 6692 KiB  
Article
Dynamic Allocation of Manufacturing Resources in IoT Job Shop Considering Machine State Transfer and Carbon Emission
by Xuan Su, Wenquan Dong, Jingyu Lu, Chen Chen and Weixi Ji
Sustainability 2022, 14(23), 16194; https://doi.org/10.3390/su142316194 - 04 Dec 2022
Cited by 2 | Viewed by 1726
Abstract
The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper [...] Read more.
The optimal allocation of manufacturing resources plays an essential role in the production process. However, most of the existing resource allocation methods are designed for standard cases, lacking a dynamic optimal allocation framework for resources that can guide actual production. Therefore, this paper proposes a dynamic allocation method for discrete job shop resources in the Internet of Things (IoT), which considers the uncertainty of machine states, and carbon emission. First, a data-driven job shop resource status monitoring framework under the IoT environment is proposed, considering the real-time status of job shop manufacturing resources. A dynamic configuration mechanism of manufacturing resources based on the configuration threshold is proposed. Then, a real-time state-driven multi-objective manufacturing resource optimization allocation model is established, taking machine tool energy consumption and tool wear as carbon emission sources and combined with the maximum completion time. An improved imperialist competitive algorithm (I-ICA) is proposed to solve the model. Finally, taking an actual production process of a discrete job shop as an example, the proposed algorithm is compared with other low-carbon multi-objective optimization algorithms, and the results show that the proposed method is superior to similar methods in terms of completion time and carbon emissions. In addition, the practicability and effectiveness of the proposed dynamic resource allocation method are verified in a machine failure situation. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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18 pages, 2605 KiB  
Article
A Decision-Making Model for Remanufacturing Facility Location in Underdeveloped Countries: A Capacitated Facility Location Problem Approach
by Raoul Fonkoua Fofou, Zhigang Jiang, Qingshan Gong and Yihua Yang
Sustainability 2022, 14(22), 15204; https://doi.org/10.3390/su142215204 - 16 Nov 2022
Cited by 1 | Viewed by 1432
Abstract
Underdeveloped countries are gradually opening remanufacturing facilities to recover end-of-life products (EOL). Locating these facilities in underdeveloped countries is quite challenging because many factors related to the environment, economics, and ethics have to be considered. This paper proposes a decision-making model for locating [...] Read more.
Underdeveloped countries are gradually opening remanufacturing facilities to recover end-of-life products (EOL). Locating these facilities in underdeveloped countries is quite challenging because many factors related to the environment, economics, and ethics have to be considered. This paper proposes a decision-making model for locating remanufacturing facilities, a critical factor in implementing remanufacturing in underdeveloped countries. Our principal objective is to obtain the capacity, number, and geographical locations for newly established remanufacturing facilities using a Capacitated Facility Location Problem (CFLP) approach. The mathematical model helps us find the number of facilities that will need to be opened to fully recover the EOL products and the total cost during the entire process. A case study on the establishment of SEVALO Remanufacturing Machinery Co., Ltd. in Cameroon is used to demonstrate the CFLP approach. The results and analyses show that the successful establishment of SEVALO in Cameroon will significantly help to reduce the quantity of construction machinery parts dumped into the environment. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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14 pages, 1990 KiB  
Article
A Green Design Method for a Rust-Off Machine Based on QFDE and Function Analysis
by Qingshan Gong, Chen Chen, Zhigang Jiang, Yurong Xiong, Mingmao Hu and Jinghong Yang
Sustainability 2022, 14(16), 9979; https://doi.org/10.3390/su14169979 - 12 Aug 2022
Cited by 1 | Viewed by 1132
Abstract
Green design pursues maximum economic efficiency and minimum environmental impact. Green design of mechanical equipment can ensure environmentally friendly design and manufacturing. A rust-off machine is a crucial piece of equipment in remanufacturing. As attention to remanufacturing grows, the demand for rust-off machines [...] Read more.
Green design pursues maximum economic efficiency and minimum environmental impact. Green design of mechanical equipment can ensure environmentally friendly design and manufacturing. A rust-off machine is a crucial piece of equipment in remanufacturing. As attention to remanufacturing grows, the demand for rust-off machines is gradually increasing, but their green characteristics have not attracted attention. There is a need to carry out a green design for a rust-off machine that can improve its economy and environmental friendliness. In response to this need, in this study, a green design method for a rust-off machine was developed, combining the strengths of quality function deployment for environment (QFDE) and function analysis. In this method, functional analysis was used to determine the mapping relationship between functions and components. QFDE was used not only to determine the relationship between customer requirements and engineering metrics, but also to establish the relationship between engineering metrics and components and to obtain optimal structural solutions. A green design of a steel plate surface rust-off machine was taken as a case study. The results show that this method can achieve a win-win design that achieves maximum economic benefit and environmental protection. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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12 pages, 984 KiB  
Article
Timing Decision for Active Remanufacturing Based on 3E Analysis of Product Life Cycle
by Qingshan Gong, Yurong Xiong, Zhigang Jiang, Jinghong Yang and Chen Chen
Sustainability 2022, 14(14), 8749; https://doi.org/10.3390/su14148749 - 18 Jul 2022
Cited by 5 | Viewed by 1250
Abstract
Active remanufacturing is an important technique that is used to reduce the uncertainty of the quality of remanufactured cores. However, the implementation of active remanufacturing too early or late will lead to a reduction in economic benefits and an increase in environmental impact [...] Read more.
Active remanufacturing is an important technique that is used to reduce the uncertainty of the quality of remanufactured cores. However, the implementation of active remanufacturing too early or late will lead to a reduction in economic benefits and an increase in environmental impact during the whole life cycle of the product. To this end, an active-remanufacturing-timing decision method is proposed based on an economic, energy and environmental (3E) analysis of product life cycle. In this method, the quantitative function of the cost, energy consumption and environmental emissions of used products in the manufacturing stage, service stage, and remanufacturing stage are firstly constructed based on life-cycle assessment (LCA) and life-cycle cost (LCC). Then, a multi-objective optimization method and the particle swarm algorithm are utilized to obtain active-remanufacturing timing with the optimal economic and environmental benefits of remanufacturing. Finally, a case study on remanufacturing on used engines is demonstrated to validate the proposed method. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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18 pages, 4408 KiB  
Article
Analysis and Evaluation of Energy Consumption and Carbon Emission Levels of Products Produced by Different Kinds of Equipment Based on Green Development Concept
by Yongmao Xiao, Renqing Zhao, Wei Yan and Xiaoyong Zhu
Sustainability 2022, 14(13), 7631; https://doi.org/10.3390/su14137631 - 22 Jun 2022
Cited by 7 | Viewed by 1357
Abstract
Energy consumption and carbon emission levels in the production process constitute an important basis for the selection of production equipment. The energy consumption and carbon emission levels of the same product produced by different kinds equipment vary greatly from one tool to another. [...] Read more.
Energy consumption and carbon emission levels in the production process constitute an important basis for the selection of production equipment. The energy consumption and carbon emission levels of the same product produced by different kinds equipment vary greatly from one tool to another. Unfortunately, traditional modes of selection only give qualitative results, so that it is difficult to provide a quantitative reference to enable enterprises to choose appropriate modes of production in the context of the green development concept (GDC). In order to solve this problem, a calculation method for multiple energy consumption and carbon-emission objectives for commodity production is proposed. The focus of this paper is to research the difference between the energy consumption and carbon emission levels of the same product produced by different kinds of equipment. The energy consumption and carbon emissions of different kinds of equipment can be calculated by gray wolf algorithm. The results show that the proposed method can calculate the optimal values of energy consumption and carbon emissions in the same kinds of products produced by different equipment, which can provide assistance for enterprises in choosing the production equipment that best conforms to the green development concept. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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21 pages, 3702 KiB  
Article
ABIDE: A Novel Scheme for Ultrasonic Echo Estimation by Combining CEEMD-SSWT Method with EM Algorithm
by Yingkui Jiao, Zhiwei Li, Junchao Zhu, Bin Xue and Baofeng Zhang
Sustainability 2022, 14(4), 1960; https://doi.org/10.3390/su14041960 - 09 Feb 2022
Cited by 4 | Viewed by 1335
Abstract
Ultrasonic echo estimation has played an important role in industrial non-destructive testing and analysis. The ability to estimate parameters in the ultrasonic echo model is crucial to ensure the effectiveness of practical ultrasonic testing applications. In this paper, a scheme called ABIDE for [...] Read more.
Ultrasonic echo estimation has played an important role in industrial non-destructive testing and analysis. The ability to estimate parameters in the ultrasonic echo model is crucial to ensure the effectiveness of practical ultrasonic testing applications. In this paper, a scheme called ABIDE for identifying both multiple noises in the echo signal and the distribution of the denoised signal is proposed for ultrasonic echo signal parameter estimation. ABIDE integrates complementary ensemble empirical mode decomposition and the synchrosqueezed wavelet transform (CEEMD-SSWT) as well as the expectation maximization (EM) algorithm. The echo signal is split into a series of IMF components and a residual with the help of CEEMD, and then these IMFs are classified into the noise-dominant part and signal-dominant part by analyzing the correlation of each IMF and the echo signal using grey relational analysis. Considering the effect of noise in the signal-dominant part, SSWT is adopted to remove the noise in the signal-dominant part. Lastly, the signal output by the SSWT algorithm is used for reconstructing a denoised signal combined with the residual from CEEMD. Considering the distribution characteristic of the denoised signal, the EM algorithm is used to estimate parameters in the ultrasonic echo model. The relative performance of the proposed scheme was evaluated on synthetic data and real-world data and then compared with the state-of-the-art methods. Simulation results on synthetic data show that ABIDE outperforms the state-of-the-art methods in parameter estimation. Physical results on real-world data show that the proposed scheme has a greater PCC value in estimating echo model parameters. This paper also shows that ABIDE requires less convergence time than competitive methods. Full article
(This article belongs to the Special Issue Enabling Technologies and Methods for Sustainable Remanufacturing System)
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