Special Issue "Sustainability and Artificial Intelligence"

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

Deadline for manuscript submissions: 31 August 2021.

Special Issue Editor

Prof. Dr. Peng-Yeng Yin
E-Mail Website
Guest Editor
Department of Information Management, National Chi Nan University, Puli 54561, Taiwan
Interests: artificial intelligence; evolutionary computation; wind and solar energy; metaheuristics; pattern recognition; image processing; machine learning; software engineering; computational intelligence; operations research
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Sustainable development is the core value of human society to create a better world for living. The rapid industrialization and urbanization has significantly changed human activities and left great impacts to the natural environment. Major populations are densely resorting to metropolitan cities, people are trailing on the streets by cars, industry production emits high volume of pollutants into air and rivers. These common everyday activities spoil our environment, contaminate the farms, change the mountain landscape and forest ecosystem. All these have exceeded the capacity of our mother earth. For the past decade, artificial intelligence (AI) has made a great progress in processing the data intelligently and efficiently. The applications of AI, especially deep learning, seep into every piece of our lives, from unmanned aerial vehicle, autonomous driving, resource-constrained production, humanoids and unmanned factory, to green logistics, circular economy, technology agriculture, air quality forecasting, personal digital assistant, and playing chess. We anticipate a next big thing by exploring the possible crossovers between sustainability and AI. The purpose of the special issue is to call attention to the marriage between “sustainability” and “artificial intelligence” and to boost the synergy between the two streams. The publication of our special issue will position itself at the research frontier for promoting sustainability by applying artificial intelligence algorithms. We are soliciting contributions (research and review articles) covering a broad range of topics on sustainability and artificial intelligence, including (though not limited to) the following:

  • AI in Internet of Thing (IoT)
  • AI in computer and social network
  • AI in security
  • AI in agriculture
  • AI in air quality forecasting
  • AI in logistics
  • AI in circular economy
  • AI in resource-constrained production
  • AI in entertainment
  • sustainability and IoT
  • sustainability and security
  • sustainability and network management
  • sustainability and data mining, machine learning, and artificial intelligence
  • sustainability and heuristic and metaheuristic
  • sustainability and food technology
  • sustainability and circular economy
  • sustainability and resource-constrained production
  • sustainability and entertainment

Prof. Dr. Peng-Yeng Yin
Guest Editor

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 papers will be 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 1900 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

  • sustainability
  • artificial intelligence
  • unmanned factory
  • green logistics
  • resource-constrained production
  • circular economy
  • technology agriculture
  • air quality forecasting
  • security

Published Papers (2 papers)

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Research

Article
Minimizing the Makespan in Flowshop Scheduling for Sustainable Rubber Circular Manufacturing
Sustainability 2021, 13(5), 2576; https://doi.org/10.3390/su13052576 - 28 Feb 2021
Viewed by 471
Abstract
It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via [...] Read more.
It is estimated that 1 billion waste tires are generated every year across the globe, yet only 10% are being processed, and much rubber waste is yielded during manufacturing. These waste tires and rubber scraps are poisonous to the environment when processed via incineration and landfill. Rubber circular manufacturing is an effective solution that reduces not only rubber waste but also raw material costs. In this paper we propose a two-line flowshop model for the circular rubber manufacturing problem (CRMP), where the job sequence of two production lines is appropriately aligned to obtain the shortest makespan while guaranteeing that sufficient rubber waste yielded in the first line is ready to be reused for circular production in the second line. A genetic algorithm (GA) is developed, and the design of its genetic operations is customized to the CRMP context to achieve efficient and effective evolution. The experimental results with both real and synthetic datasets show that the GA significantly surpasses two heuristics in the literature by delivering the minimum makespan, which is 3.4 to 11.2% shorter than those obtained by the two competing methods. Full article
(This article belongs to the Special Issue Sustainability and Artificial Intelligence)
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Article
Detection of Potential Controversial Issues for Social Sustainability: Case of Green Energy
Sustainability 2020, 12(19), 8057; https://doi.org/10.3390/su12198057 - 29 Sep 2020
Viewed by 617
Abstract
More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change [...] Read more.
More and more people are involved in sustainability-related activities through social network to support/protect their idea or motivation for sustainable development. Understanding the variety of issues of social pulsation is crucial in development of social sustainability. However, issues in social media generally change overtime. Issues not identified in advance may soon become popular topics discussed in society, particularly controversial issues. Previous studies have focused on the detection of hot topics and discussion of controversial issues, rather than the identification of potential controversial issues, which truly require paying attention to social sustainability. Furthermore, previous studies have focused on issue detection and tracking based on historical data. However, not all controversial issues are related to historical data to foster the cases. To avoid the above-mentioned research gap, Artificial Intelligence (AI) plays an essential role in issue detection in the early stage. In this study, an AI-based solution approach is proposed to resolve two practical problems in social media: (1) the impact caused by the number of fan pages from Facebook and (2) awareness of the levels for an issue. The proposed solution approach to detect potential issues is based on the popularity of public opinion in social media using a Web crawler to collect daily posts related to issues in social media under a big data environment. Some analytical findings are carried out via the congregational rules proposed in this research, and the solution approach detects the attentive subjects in the early stages. A comparison of the proposed method to the traditional methods are illustrated in the domain of green energy. The computational results demonstrate that the proposed approach is accurate and effective and therefore it provides significant contribution to upsurge green energy deployment. Full article
(This article belongs to the Special Issue Sustainability and Artificial Intelligence)
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