Parsimonious AHP-DEA Integrated Approach for Efficiency Evaluation of Production Processes

Ammirato, Salvatore; Fattoruso, Gerarda; Violi, Antonio

doi:10.3390/jrfm15070293

Cited by 11 publications

(8 citation statements)

References 58 publications

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“…Only (𝑛 − 1) pairwise comparison required are required in parsimonious AHP (Abastante et al, 2019) and parsimonious BWM (Moslem, 2023) models. Parsimonious AHP and parsimonious BWM are new techniques with only few application thus the question on applicability remains open in terms of limitation and application conditions (Ammirato et al, 2022;Moslem, 2023). Considering 17 criteria, 16 pairwise comparisons will take 4 hours or continuous 13.5 hours (including 7.5 hours physical examination) to evaluate 30 patients a day.…”

Section: Discussionmentioning

confidence: 99%

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Hassan¹,

Umer²,

Hassan³

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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Prioritizing patients is a growing concern in healthcare. Once resources are limited, prioritization is considered an effective and viable solution in provision of healthcare treatment to awaiting patients. Prioritization is a preferred approach that helps clinicians to apportion scarce resources fairly and transparently. In this study, a novel methodology of prioritizing the patient is formulated using fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The objective is based on actual hospital conditions in Pakistan. The proposed methodology has two contributions: objective scoring mechanism that translates the patient’s condition given in human linguistic terms; and second methodology to prioritize patients according to corresponding scores. To validate the proposed methodology, simulation was carried out on actual data collected in real-time by surgeons, while providing consultations to their patients. The proposed methodology outperforms the traditional methodology by reducing average waiting time by 34% (from 4.246 to 2.810 days), minimize wait time and delays by 46.7% (from 15 to 8 days), and number of surgery days by 18%. The majority of the previously presented researched methodologies prioritize the patients subjectively. This study presents an objective methodology to prioritize the patients and decrease wait-times while ensuring transparency and equity.

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Section: Discussionmentioning

confidence: 99%

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Hassan¹,

Umer²,

Hassan³

et al. 2023

Decis. Mak. Appl. Manag. Eng.

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“…Integrating multicriteria decision-making methods, such as the Analytic Hierarchy Process (AHP), the Technique for Order of Preference by Similarity to Ideal Solution (TOP-SIS), or Multi-Attribute Utility Theory (MAUT), might offer a more nuanced approach to understanding and quantifying the impact of user opinions on stock market predictions [83,84]. These multicriteria methods are adept at handling complex decision-making scenarios involving multiple factors.…”

Section: Discussionmentioning

confidence: 99%

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

2023

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Predicting stock market trends is an intriguing and complex problem, which has drawn considerable attention from the research community. In recent years, researchers have employed machine learning techniques to develop prediction models by using numerical market data and textual messages on social networks as their primary sources of information. In this article, we propose User2Vec, a novel approach to improve stock market prediction accuracy, which contributes to more informed investment decision making. User2Vec is a unique method that recognizes the unequal impact of different user opinions on specific stocks, and it assigns weights to these opinions based on the accuracy of their associated social metrics. The User2Vec model begins by encoding each message as a vector. These vectors are then fed into a convolutional neural network (CNN) to generate an aggregated feature vector. Following this, a stacked bi-directional long short-term memory (LSTM) model provides the final representation of the input data over a period. LSTM-based models have shown promising results by effectively capturing the temporal patterns in time series market data. Finally, the output is fed into a classifier that predicts the trend of the target stock price for the next day. In contrast to previous attempts, User2Vec considers not only the sentiment of the messages, but also the social information associated with the users and the text content of the messages. It has been empirically proven that this inclusion provides valuable information for predicting stock direction, thereby significantly enhancing prediction accuracy. The proposed model was rigorously evaluated using various combinations of market data, encoded messages, and social features. The empirical studies conducted on the Dow Jones 30 stock market showed the model’s superiority over existing state-of-the-art models. The findings of these experiments reveal that including social information about users and their tweets, in addition to the sentiment and textual content of their messages, significantly improves the accuracy of stock market prediction.

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“…A case study of an Indian electronic retail chain is presented to reveal the potentiality and suitability of the proposed models. Ammirato et al (2022) proposed an innovative composite indicator to measure and control the performance of production processes. The aim is to provide a tool for controlling the efficiency of the processes, assessed in relation to the number and the impact of occurring "errors", which can take into account the opinion of experts in the specific domain.…”

Section: Data Envelopment Analysismentioning

confidence: 99%

“…A case study of an Indian electronic retail chain is presented to reveal the potentiality and suitability of the proposed models. Ammirato et al. (2022) proposed an innovative composite indicator to measure and control the performance of production processes.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving performance evaluation based on the balanced scorecard with data envelopment analysis and game theory approaches: a case study

Abedian,

Shirouyehzad,

Davoodi

2024

JM2

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Purpose This paper aims to propose an integrated use of balanced scorecard (BSC), data envelopment analysis (DEA) and game theory approach as an enhanced performance measurement technique to determine and rank the importance of manufacturing indicators of a steel company as a real case study. Design/methodology/approach An efficiency change ratio is defined to examine the characteristic function of each coalition which is super-additive. Then, the Shapley value index is used as the solution of the cooperative game to determine the importance of the BSC indicators of the company and rank order them. Findings The results reveal that “profitability rate” is the most important BSC indicator, whereas “customer satisfaction” is the least significant one. The ranking order of the importance of all BSC indicators makes it possible for the senior managers of the organization to realize the importance of each index separately and to improve the profitability and the number of customers by presenting programs according to the budget and time constraints. Originality/value The main contribution of this paper lies in the adoption of a game theory approach to performance measurement in the industrial sector that determines and ranks the importance of manufacturing indicators.

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Parsimonious AHP-DEA Integrated Approach for Efficiency Evaluation of Production Processes

Cited by 11 publications

References 58 publications

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

Application of fuzzy TOPSIS for prioritization of patients on elective surgeries waiting list - A novel multi-criteria decision-making approach

User2Vec: A Novel Representation for the Information of the Social Networks for Stock Market Prediction Using Convolutional and Recurrent Neural Networks

Improving performance evaluation based on the balanced scorecard with data envelopment analysis and game theory approaches: a case study

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