Browsing by Author "Chuang, Yen-Ching"

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A DATA-DRIVEN MADM MODEL FOR PERSONNEL SELECTION AND IMPROVEMENT
(2020-01-01)
Chuang, Yen-Ching
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Hu, Shu-Kung
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Liou, James J. H.
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Tzeng, Gwo-Hshiung
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科技管理研究所
Personnel selection and human resource improvement are characteristically multiple-attribute decision-making (MADM) problems. Previously developed MADM models have principally depended on experts' judgements as input for the derivation of solutions. However, the subjectivity of the experts' experience can have a negative influence on this type of decision-making process. With the arrival of today's data-based decision-making environment, we develop a data-driven MADM model, which integrates machine learning and MADM methods, to help managers select personnel more objectively and to support their competency improvement. First, RST, a machining learning tool, is applied to obtain the initial influential significance-relation matrix from real assessment data. Subsequently, the DANP method is used to derive an influential significance-network relation map and influential weights from the initial matrix. Finally, the PROMETHEE-AS method is applied to assess the gap between the aspiration and current levels for every candidate. An example was carried out using performance data with evaluation attributes obtained from the human resource department of a Chinese food company. The results revealed that the data-driven MADM model could enable human resource managers to resolve the issues of personnel selection and improvement simultaneously, and can actually be applied in the era of big data analytics in the future.
A fuzzy integral-based model for supplier evaluation and improvement
(2014-05-10)
Liou, James J. H.
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Chuang, Yen-Ching
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Tzeng, Gwo-Hshiung
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科技管理研究所
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Institute of Management of Technology
Decisions related to supplier improvement and selection are inherently multiple criteria decision making (MCDM) problems and are strategically important to companies. Although efforts have been made to discover systematic methods to select the proper supplier, these efforts have assumed that the criteria are independent, which is not actually the case. Some studies that have treated the criteria as interdependent use additive models to obtain aggregate performance. We propose a novel fuzzy integral-based model that addresses the interdependence among the various criteria and employs the non-additive gap-weighted analysis. The structure of the relationships among the criteria and the criteria weights are developed using Decision Making Trial and Evaluation Laboratory (DEMATEL) combined with a fundamental concept of an analytic network process (ANP) called DANP. The fuzzy integral is then used to aggregate the gaps using the weights obtained from the DANP. The proposed model addresses the shortcomings of prior models and provides a more reasonable representation of the real world. The method is demonstrated using supplier evaluation and improvement data from a Taiwanese company. (C) 2013 Published by Elsevier Inc.
Combining Hybrid MADM with Fuzzy Integral for Exploring the Smart Phone Improvement in M-Generation
(2012-06-01)
Hu, Shu-Kung
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Chuang, Yen-Ching
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Yeh, Ya-Fan
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Tzeng, Gwo-Hshiung
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交大名義發表
With the advancements of mobile technologies, internet connection can be conveniently accessed by the fast-growing number of smart phones. This is a relevant feature, making smart phone devices highly attractive to the M-era. In order to understand the end-users' needs and elevate competitive advantage of the enterprises, this study has set up an evaluation model for the smart phone improvement by using Fuzzy Hybrid MADM method, which integrated DANP in the Fuzzy integral. Analysis of DANP revealed that Mobile convenience (D-3) has the highest impact to improvement of Customer Equity (D-1), while Mobile multimedia services (C-34) showed as the most influential criteria of D-3. In the case of performance evaluation, it was found out that non-additive (fuzzy integral method, FI) could represent more the real behavior than additive (simple additive weighting method, SAW). This study was able to provide the enterprises a recommended direction which can enhance competitiveness of the smart phone according to network relation map (NRM) and performance evaluation.
Data-driven hybrid multiple attribute decision-making model for green supplier evaluation and performance improvement
(2019-12-20)
Liou, James J. H.
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Chuang, Yen-Ching
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Zavadskas, Edmundas Kazimieras
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Tzeng, Gwo-Hshiung
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科技管理研究所
Multi-attribute decision-making (MADM) is the most commonly used methodology for green supplier evaluation and performance improvement. Previous MADM models have mainly relied upon the knowledge and opinions of domain-experts as the starting point for making decisions. However, the results are affected by the subjectivity of these judgments and knowledge limitations. This study develops a data-driven MADM model that utilizes potential rules/patterns derived from a large amount of historical data to help decision-makers objectively select suitable green suppliers and provide systemic improvement strategies to help reach the aspiration level. First, the random forest (RF) algorithm is applied to explore the pairwise influential strength relations among attributes derived from real audit data. The influence matrix derived using the RF algorithm is used as input for decision-making trial and evaluation laboratory (DEMATEL)-based analytical network process analysis which is carried out to obtain the influential strength weights of the attributes. Then, multi-objective optimization on the basis of ratio analysis to the aspiration level (MOORA-AS) is utilized to evaluate the gap between the current and aspiration levels for each green supplier. The developed critical influence strength route (CISR) can help managers derive various strategies for improving green supplier performance. The functioning of the proposed model is illustrated using data obtained from the green supplier management department of a Taiwanese electronics company. The results reveal that the proposed model can effectively help decision-makers to solve the problem of green supplier selection and devise strategies for improvement. (C) 2019 Elsevier Ltd. All rights reserved.