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Computational Intelligence for Sustainability
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Computational Intelligence for Sustainability

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4718

Special Issue Editors

Dr. Zhebin Xue

E-Mail Website
Guest Editor
College of Textile and Clothing Engineering, Soochow University, Suzhou 215021, China
Interests: Artificial Intelligence applied to fashion industry (intelligent fashion design, intelligent modeling of uncertainty problems in fashion, virtual reality, etc.); design technologies and supply chain management for sustainable fashion; human-centered fashion design and development, sensory analysis, kansei engineering; smart textile and clothing technologies
Prof. Dr. Xianyi Zeng

E-Mail Website
Guest Editor
Ecole Nationale Supérieure des Arts et Industries Textiles, Roubaix, France
Interests: artificial intelligence; fashion digitalization; modelling; optimization; decision support systems; wearable management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the growing concerns for environmental and climate changes, together with issues of poverty, waste, uses of energy, water, resources and human capital, have placed sustainable development under the spotlight. The overarching goal of sustainable research is understanding how to ensure the well-being of current and future generations within the limits of the natural world from the perspectives of social and economic governance, environmental management, public value cultivation and sustainability-oriented innovations in various industrial sectors.

The emergence of Computational Intelligence (CI) has helped to transform business practices and industries, and address various major societal problems including sustainability. Computational intelligence for sustainability is the computer scientific branch of the interdisciplinary field of sustainability research, an applied science about the research in sustainable solutions and their implementation. In this context, we will organize a special issue in Computer Intelligence for Sustainability in order to offer a systematic overview of this emerging research field and provide innovative interdisciplinary approaches. It will provide a leading forum for disseminating the latest results of studies, development, and applications of sustainability research with a strong involvement of computer intelligence.

Scope:

  • Intelligent sustainable product design
  • Intelligent sustainable production system modeling, simulation and optimization
  • Sustainability-oriented decision support systems
  • Sustainability-oriented supply chain optimization
  • Environmental and sustainability assessment
  • Circular economy – computational models
  • Sustainable consumption – computational models
  • Interactions between health and environment

and others, all as related to the application of computer intelligence to sustainability research.

Dr. Zhebin Xue
Prof. Dr. Xianyi 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.

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

11 pages, 1655 KiB  
Article
Grape Maturity Estimation for Personalized Agrobot Harvest by Fuzzy Lattice Reasoning (FLR) on an Ontology of Constraints
by Chris Lytridis, George Siavalas, Theodore Pachidis, Serafeim Theocharis, Eirini Moschou and Vassilis G. Kaburlasos
Sustainability 2023, 15(9), 7331; https://doi.org/10.3390/su15097331 - 28 Apr 2023
Cited by 2 | Viewed by 1493
Abstract
Sustainable agricultural production, under the current world population explosion, calls for agricultural robot operations that are personalized, i.e., locally adjusted, rather than en masse. This work proposes implementing such operations based on logic in order to ensure that a reasonable operation is applied [...] Read more.
Sustainable agricultural production, under the current world population explosion, calls for agricultural robot operations that are personalized, i.e., locally adjusted, rather than en masse. This work proposes implementing such operations based on logic in order to ensure that a reasonable operation is applied locally. In particular, the interest here is in grape harvesting, where a binary decision has to be taken regarding the maturity of a grape in order to harvest it or not. A Boolean lattice ontology of inequalities is considered regarding three grape maturity indices. Then, the established fuzzy lattice reasoning (FLR) is applied by the FLRule method. Comparative experimental results on real-world data demonstrate a good maturity prediction. Other advantages of the proposed method include being parametrically tunable, as well as exhibiting explainable decision-making with either crisp or ambiguous input measurements. New mathematical results are also presented. Full article
(This article belongs to the Special Issue Computational Intelligence for Sustainability)
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21 pages, 7826 KiB  
Article
A Machine Learning-Enhanced 3D Reverse Design Approach to Personalized Garments in Pursuit of Sustainability
by Zhujun Wang, Xuyuan Tao, Xianyi Zeng, Yingmei Xing, Zhenzhen Xu and Pascal Bruniaux
Sustainability 2023, 15(7), 6235; https://doi.org/10.3390/su15076235 - 4 Apr 2023
Cited by 5 | Viewed by 2639
Abstract
The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth [...] Read more.
The fashion industry is facing increasing pressure to move toward sustainable development, especially with concern to cost and environmental sustainability. Innovative digital technologies are regarded as a promising solution for fashion companies to resolve this issue. In this context, this paper put forth a new 3D reverse garment design approach embedded with a garment fit prediction and structure self-adaptive adjustment mechanism, using machine learning (ML) techniques. Initially, the 3D basic garment was drawn directly on the scanned mannequin of a specific consumer. Next, a probabilistic neural network (PNN) was employed to predict the garment’s fit. Afterwards, genetic algorithms (GA) and support vector regression (SVR) were utilized to estimate and control the garment structural parameters following the feedback of fit evaluation and the consumer’s personalized needs. Meanwhile, a comprehensive evaluation was constructed to characterize the quantitative relationships between the consumer profile and the designed garment profile (garment fit and styles). Ultimately, the desired garment which met the consumer’s needs was obtained by performing the routine of “design–fit evaluation–pattern adjustment–comprehensive evaluation”, iteratively. The experimental results show that the proposed approach provides a new solution to develop quality personalized fashion products (garments) more accurately, economically, and in an environmentally friendly way. It is feasible to facilitate the sustainable development of fashion companies by simultaneously reducing costs and negative impacts on the environment. Full article
(This article belongs to the Special Issue Computational Intelligence for Sustainability)
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