Review on multi-criteria decision analysis in sustainable manufacturing decision making

Jamwal, Anbesh; Agrawal, Rajeev; Sharma, Monica; Kumar, Vikas

doi:10.1080/19397038.2020.1866708

Cited by 98 publications

(45 citation statements)

References 127 publications

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“…Recent studies on SM with ML algorithms are covering areas in SM that include production planning, total energy consumption, job shop scheduling, product design and sustainable machining (Tayal et al, 2020). The inclusion of emerging technologies such as IoT, artificial intelligence, data analytics, digital delivery services is influencing SM practices in the Industry 4.0 era (Jamwal et al, 2021a;Machado et al, 2020). Some studies have reported that the use of these advanced technologies in sustainable manufacturing results in minimizing total energy consumptions, reduced labours inputs, and better condition-based maintenance prediction (Wu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Jamwal

Agrawal

Sharma

et al. 2021

JEIM

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PurposeThe role of data analytics is significantly important in manufacturing industries as it holds the key to address sustainability challenges and handle the large amount of data generated from different types of manufacturing operations. The present study, therefore, aims to conduct a systematic and bibliometric-based review in the applications of machine learning (ML) techniques for sustainable manufacturing (SM).Design/methodology/approachIn the present study, the authors use a bibliometric review approach that is focused on the statistical analysis of published scientific documents with an unbiased objective of the current status and future research potential of ML applications in sustainable manufacturing.FindingsThe present study highlights how manufacturing industries can benefit from ML techniques when applied to address SM issues. Based on the findings, a ML-SM framework is proposed. The framework will be helpful to researchers, policymakers and practitioners to provide guidelines on the successful management of SM practices.Originality/valueA comprehensive and bibliometric review of opportunities for ML techniques in SM with a framework is still limited in the available literature. This study addresses the bibliometric analysis of ML applications in SM, which further adds to the originality.

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Section: Introductionmentioning

confidence: 99%

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Jamwal

Agrawal

Sharma

et al. 2021

JEIM

Self Cite

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“…MCDM techniques come in a variety of forms such as Analytic hierarchy process (Saaty, 1980), Analytic network process , Best worst method (Rezaei, 2015), Technique for Order of Preference by Similarity to Ideal Solution (Hwang & Yoon, 1981), VlseKriterijumska Optimizacija I Kompromisno Resenje(VIKOR) that means: Multicriteria Optimization and Compromise Solution (Opricovic & Tzeng, 2007), ELimination Et Choix Traduisant la REalité (ELECTR) ( Roy, 1968), Preference ranking organization method for enrichment evaluation (Brauers, 2003), Multi-Objective Optimization based on Ratio Analysis ( Zavadskas, 2007), etc. and these methods can be used in many different areas such as manufacturing industry (Nallusamy et al, 2016;Vigneshvaran & Vinodh, 2021;Jamwal et al, 2021), Logistics and Transport (Tzeng & Huang, 2012;Kumru & Kumru, 2014;Mardani et al, 2016), electronic industry(Aravind et al, 2014Asante et al, 2019), shipping industry (Seker et al, 2017;Gavalas et al, 2021), hydrology and water management(de Castro-Pardo et al, 2021;Dey et al, 2021), telecommunication industry (Lin et al, 2010;Hussain et al, 2019), energy management (Ecer, 2021;Kannan et al, 2021)…”

Section: Literature Reviewmentioning

confidence: 99%

Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique

Sahoo¹,

Choudhury²

2022

10.5267/j.dsl

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The decision to purchase the best available electric power wheelchair (EPWC) for a person with a disability in a low-resource context is very stressful, whether it is based on financial circumstances or the availability of medical solutions. The study's objective is to assess the EPWC options available on the market, focused on a set of conflicting criteria. In this research, three multi-criteria decision-making (MCDM) approaches are used to make decisions. ENTROPY method for weightage calculation of various parameters, COPRAS and EDAS methods for evaluating and ranking alternatives are applied. Both COPRAS and EDAS are applied separately for ranking of selected wheelchair models, and to check the robustness of the applied method, sensitivity analysis on cost criterion is carried out. The result shows that for both methods, EPWC-1 is the top priority model to buy, whereas EPWC-7 is the worst model for COPRAS, and EPWC-10 is the worst model for EDAS among the ten alternatives.

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“…In [75], the authors point out that the AHP method offers advantages when compared to other classical decision-making methods, such as the possibility to decompose the decision problem hierarchically and use subjective and verbal expressions to determine the relative importance of the established criteria [76]. Furthermore, Jamwal et al 2021 identified as strengths of AHP: ease of being adapted to the objectives, suitable and flexible to support decision, can be applied in numerous areas and can measure consistency based on expert opinion and provide a simple and flexible model to analyze problems [77].…”

Section: Ahp-based Procedures For Selecting An Appropriate Eco-efficiency Modelmentioning

confidence: 99%

“…Thus, AHP is a widely employed method due to its flexibility [78] and is considered a simple and flexible technique when it is necessary to analyze criteria quantitatively and qualitatively [77].…”

Section: Ahp-based Procedures For Selecting An Appropriate Eco-efficiency Modelmentioning

confidence: 99%

An AHP-Based Procedure for Model Selection for Eco-Efficiency Assessment

et al. 2021

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Modelling eco-efficiency is becoming a wide field of research shown by a large body of scientific literature on the subjects concerned. This paper, after performing a systematic literature review (SLR) to identify existing models for process eco-efficiency assessments, provides a methodology, based on the analytic hierarchy process (AHP) method, for choosing the eco-efficiency assessment model to be used for a given application (process, product, or service). For the SLR, papers from the databases Scopus, Web of Science and Science Direct were used. Forty articles were considered for this study, using as the main selection criterion articles that present an eco-efficiency assessment model, since the purpose was to survey the types of existing models that are used to assess processes, products or services for eco-efficiency. With the systematic review carried out, it was possible to identify the types of models that exist and how they are used in different sectors, always aiming to identify if what was analyzed is eco-efficient and what points need to be improved. The proposed AHP-based methodology was applied to a numerical model to outline how to apply the methodology. The approach was easy to use and effective in identifying the proper eco-efficiency model.

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Review on multi-criteria decision analysis in sustainable manufacturing decision making

Cited by 98 publications

References 127 publications

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Machine learning applications for sustainable manufacturing: a bibliometric-based review for future research

Optimal selection of an electric power wheelchair using an integrated COPRAS and EDAS approach based on Entropy weighting technique

An AHP-Based Procedure for Model Selection for Eco-Efficiency Assessment

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