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Novel Decision Technology Analytics for Evaluating Sustainable Strategies and Environmental Operations

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Economic and Business Aspects of Sustainability".

Deadline for manuscript submissions: closed (30 April 2024) | Viewed by 2663

Special Issue Editor


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Guest Editor
Schulich School of Business, York University, Toronto, ON M3J 1P3, Canada
Interests: environmental informatics; simulation decomposition; simulation-optimization; machine learning; visual analytics; waste management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue is seeking applied analytics papers that describe either the creation of new methods or provide innovative applications of existing computational methods for assisting with the analysis of sustainability strategies and environmental operations. In practice, environmental analytics is an integration of science, visualization methods, and computational analytics and techniques that combines computers, computational intelligence, information technology, mathematical modeling, and system science into the assessment of “real-world” sustainability and environmental problems. Contributions to this Special Issue should investigate novel computational approaches – be this on the side of modeling, computational solution procedures, optimization, simulation, or various analytical technologies – as applied to sustainability analysis or environmental decision-making. In line with the aims and scope of this Special Issue, manuscripts should emphasize the practical relevance or the methodological contributions of the work to the analysis of sustainability strategies and environmental operations.

Topics can include:

- Decision technology approaches applied to sustainable strategies and environmental operations;

- Applied visual and/or computational analytics procedures;

- Simulation, optimization, and metaheuristic approaches used for sustainable strategy analysis;

- Machine learning, information technology, and expert systems for the analysis of sustainable and environmental operations;

- Methods for guidance and assistance in environmental strategy formulation and decision-making;

- Measures for coping with uncertainty in data, models, and decision-making;

- Multi-criteria decision-making;

- Areas of application including all aspects of sustainable strategy and environmental operations, such as waste, water, energy, climate change, industrial ecology, resource recovery, and recycling.

Prof. Dr. Julian Scott Yeomans
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 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

  • environmental decision-making
  • simulation
  • optimization
  • computational analytics
  • visual analytics
  • sustainability
  • strategy
  • operations
  • analysis
  • waste management
  • water resource planning
  • energy
  • climate change
  • industrial ecology
  • resource recovery
  • recycling

Published Papers (2 papers)

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Research

17 pages, 2223 KiB  
Article
EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management
by David Velásquez, Paola Vallejo, Mauricio Toro, Juan Odriozola, Aitor Moreno, Gorka Naveran, Michael Giraldo, Mikel Maiza and Basilio Sierra
Sustainability 2024, 16(9), 3578; https://doi.org/10.3390/su16093578 - 24 Apr 2024
Viewed by 341
Abstract
Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, [...] Read more.
Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness. Full article
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30 pages, 1437 KiB  
Article
Lean and Sustainable Supplier Selection in the Furniture Industry
by Melike Nur Ince, Cagatay Tasdemir and Rado Gazo
Sustainability 2023, 15(22), 15891; https://doi.org/10.3390/su152215891 - 13 Nov 2023
Cited by 1 | Viewed by 1702
Abstract
The furniture manufacturing sector faces intricate challenges in pioneering sustainable supply chains, particularly with lean and sustainable supplier selection. This study focused on integrating key performance indicators (KPIs) associated with lean philosophy and sustainability into multi-criteria decision-making (MCDM) methodologies. The study methodically evaluated [...] Read more.
The furniture manufacturing sector faces intricate challenges in pioneering sustainable supply chains, particularly with lean and sustainable supplier selection. This study focused on integrating key performance indicators (KPIs) associated with lean philosophy and sustainability into multi-criteria decision-making (MCDM) methodologies. The study methodically evaluated 18 criteria spanning economic, environmental, and social dimensions to discern supplier suitability in both leanness and sustainability realms. Through the ENTROPY method, weights were systematically assigned to these criteria. Subsequently, Fuzzy ARAS and Fuzzy TOPSIS methods were adeptly employed to comparatively assess supplier options. Noteworthy findings included the paramount importance of the distance to the customer and labor practices in supplier selection. The quality level, however, carried the least weight, mainly due to comparable performance scores among alternatives. Consistently, Fuzzy ARAS and Fuzzy TOPSIS results converged to pinpoint Supplier 2 as the optimal choice, reflecting its superior Ki and CCi metrics. Central to this research was the introduction of a structured and holistic framework for lean and sustainable supplier selection, a significant leap forward that promises to be an invaluable asset for practitioners and scholars in the furniture industry, supply chain management, multi-criteria decision-making, and policymaking. Full article
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