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Novel Decision Technology Analytics for Evaluating Sustainable Strategies and Environmental Operations

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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 3821

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Special Issue Editor

Prof. Dr. Julian Scott Yeomans

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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
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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 (4 papers)

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Research

18 pages, 309 KiB

Open AccessArticle

Healthcare Waste Management through Multi-Stage Decision-Making for Sustainability Enhancement

by Mohd Anjum, Hong Min and Zubair Ahmed

Sustainability 2024, 16(11), 4872; https://doi.org/10.3390/su16114872 - 6 Jun 2024

Viewed by 254

Abstract

The possible threats that healthcare waste management (HWM) poses to the environment and public health are making it more and more crucial for medical facility administrators to be worried about it. This is in line with the global trend towards firms giving sustainability more of a priority. Many organizations, including the World Health Organization (WHO) and other organizations, as well as national and state laws, have mandated the proper disposal of infectious and hazardous healthcare waste. To effectively address the complex problem of selecting the best treatment option for HWM, a multi-criteria decision-making (MCDM) procedure must be used. The alternative ranking order method accounting for two-step normalization (AROMAN) methodology is provided in the context of q-rung orthopair fuzzy environment. This method comprises two steps of normalization and is based on the criteria importance through intercriteria correlation (CRITIC) paradigm. Whereas the AROMAN methodology uses vector and linear normalization techniques to improve the accuracy of the data for further computations, the CRITIC method assesses the intercriteria correlations and scores the significance of each criterion. The ranking from the proposed method is

A l_{5} > A l_{4} > A l_{3} > A l_{1} > A l_{2}

. The study’s conclusions indicate that recycling (

A l_{5}

) is the best option since it lessens trash production, aids in resource recovery, and protects the environment. Using this method helps decision makers deal with subjectivity and ambiguity more skillfully, promotes consistency and transparency in decision making, and streamlines the process of choosing the best waste management system. Sustainable waste management practices have been implemented in the biomedical industry with some success. The proposed technique is a helpful tool for legislators and practitioners seeking to improve waste management systems. Full article

(This article belongs to the Special Issue Novel Decision Technology Analytics for Evaluating Sustainable Strategies and Environmental Operations)

17 pages, 958 KiB

Open AccessArticle

A Short-Term Air Pollutant Concentration Forecasting Method Based on a Hybrid Neural Network and Metaheuristic Optimization Algorithms

by Hossein Jalali, Farshid Keynia, Faezeh Amirteimoury and Azim Heydari

Sustainability 2024, 16(11), 4829; https://doi.org/10.3390/su16114829 - 5 Jun 2024

Viewed by 275

Abstract

In the contemporary era, global air quality has been adversely affected by technological progress, urban development, population expansion, and the proliferation of industries and power plants. Recognizing the urgency of addressing air pollution consequences, the prediction of the concentration levels of air pollutants has become crucial. This study focuses on the short-term prediction of nitrogen dioxide (NO₂) and sulfur dioxide (SO₂), prominent air pollutants emitted by the Kerman Combined Cycle Power Plant, from May to September 2019. The proposed method utilizes a new two-step feature selection (FS) process, a hybrid neural network (HNN), and the Coot optimization algorithm (COOT). This combination of FS and COOT selects the most relevant input features while eliminating redundant ones, leading to improved prediction accuracy. The application of HNN for training further enhances the accuracy significantly. To assess the model’s performance, two datasets, including real data from two different parts of Combined Cycle Power Plant in Kerman, Iran, from 1 May 2019 to 30 September 2019 (namely dataset A and B), are utilized. Subsequently, mean square error (MSE), mean absolute error (MAE), root mean square deviation (RMSE), and mean absolute percentage error (MAPE) were employed to obtain the accuracy of FS-HNN-COOT. Experimental results showed MSE of FS-HNN-COOT for NO₂ ranged from 0.002 to 0.005, MAE from 0.016 to 0.0492, RMSE from 0.0142 to 0.0736, and MAEP from 4.21% to 8.69%. Also, MSE, MAE, RMSE, and MAPE ranged from 0.0001 to 0.0137, 0.0108 to 0.0908, 0.0137 to 0.1173, and 9.03% to 15.93%, respectively, for SO₂. Full article

(This article belongs to the Special Issue Novel Decision Technology Analytics for Evaluating Sustainable Strategies and Environmental Operations)

17 pages, 2223 KiB

Open AccessArticle

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 531

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, 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

(This article belongs to the Special Issue Novel Decision Technology Analytics for Evaluating Sustainable Strategies and Environmental Operations)

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30 pages, 1437 KiB

Open AccessArticle

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 2102

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 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 K_i and CC_i 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

(This article belongs to the Special Issue Novel Decision Technology Analytics for Evaluating Sustainable Strategies and Environmental Operations)

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