Prioritization and decision-making: A brief review of methods

Lipovetsky, Stan

doi:10.3233/mas-230951

Cited by 2 publications

(1 citation statement)

References 37 publications

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“…The decision-making methods proposed in the past 5 years can be classified into three main categories: individual, hybrid, and hybrid fuzzy methods. Individual methods include AHP (Analytic Hierarchy Process), ANP (Analytic Network Process), DEA (Data Envelopment Analysis), GRA (Grey Relation Analysis), ANN (Artificial Neural Network), GP (Genetic Programming), LP (Linear Programming), MOP (Multi-objective Programming), CBR (Case-based Reasoning), GA (Genetic Algorithm) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) (Chai et al, 2013;Zolghadr-Asli et al, 2021;Lipovetsky, 2023). Hybrid methods include hybrid AHP, hybrid ANP, and hybrid ANN (Nasseri et al, 2023;Sina et al, 2023).…”

Section: Literature Reviewmentioning

confidence: 99%

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

Wang,

Zong,

Shao

et al. 2024

Front. Energy Res.

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With rapid social and economic development, the process of population aging has increased the demand for community elderly healthcare service (CEHS) in China. However, the traditional government-oriented service supply cannot meet the various needs of CEHS, and it is critical to select a suitable supplier of CEHS to provide high-quality green services in the community. Therefore, this study focuses on the issue of green supplier selection of CEHS, explores an improved transformation method for processing multi-type data, and proposes an integrated method of multi-attribute group decision-making (MAGDM) which innovatively applies the degree of overall deviation measure (ODM) to determine expert weight. Finally, the effectiveness and accuracy of the new method are verified by experimental analysis. The results show that H2 is the top choice in the green supplier selection of CEHS, followed by H1, H4, H8, H5, H6, H3, H7, H12, H11, H9, and H10. In addition, the authors apply the traditional ED method to calculate expert weights and compare the results of ODM and ED. It is a fact that the improved ODM method should be more efficient and accurate than the traditional ED method.

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Section: Literature Reviewmentioning

confidence: 99%

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

Wang,

Zong,

Shao

et al. 2024

Front. Energy Res.

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Leveraging machine learning for augmenting organizational effectiveness: Analytic hiearchy process framework

Jha,

Arora

2024

MAS

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Analytic Hierarchy Process (AHP) is a unique tool which can help in improvising the usage of machine learning in attaining organizational effectiveness. True, machine learning has emerged as one of the most important tools in enhancing organizational effectiveness through improved strategic decision-making vis-à-vis key performance indicators. It has redefined the way companies can create, and measure value added, and experiences generated for the end users of their products, services, and other offerings. Machine learning algorithms are being leveraged for making more predictive and prescriptive key performance indicators which ultimately contribute towards optimizations of business processes and overall improvement in the competitiveness of the organizations. It also helps the organizations in attaining excellence in execution of strategic decisions through almost accurate predictive insights on various management functions related to HR, marketing, finance, and operations which in turn boost stakeholder satisfaction. In this study, the authors have developed an analytic hierarchy process framework based on review of 166 peer-reviewed research papers to determine how the organizations can priorities management functions and their attributes coupled with machine learning applications for higher levels of efficiencies. Insights from this article may help the practicing managers in prioritizing use of machine learning in management functions for optimizing results and improving overall organizational effectiveness.

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Prioritization and decision-making: A brief review of methods

Cited by 2 publications

References 37 publications

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

An improved multi-attribute group decision-making method for selecting the green supplier of community elderly healthcare service

Leveraging machine learning for augmenting organizational effectiveness: Analytic hiearchy process framework

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