Interactive TOPSIS Based Group Decision Making Methodology Using Z-Numbers

Yaakob, Abdul Malek; Gegov, Alexander

doi:10.1080/18756891.2016.1150003

Cited by 101 publications

(60 citation statements)

References 28 publications

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“…Previous research has described the use of TOPSIS method theory for the best employee selection [10], even some previous research on the use of TOPSIS method has been very much with various cases [11], the application of TOPSIS method on web application for the best employee selection is expected to contribute different from previous research that already exist.…”

Section: Introductioncontrasting

confidence: 43%

TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees

Siahaan¹,

Rahim²,

Listyorini³

2018

Preprint

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Abstract. The selection of the best employees is one of the process of evaluating how well the performance of the employees is adjusted to the standards set by the company and usually done by top management such as General Manager or Director. In general, the selection of the best employees is still perform manually with many criteria and alternatives, and this usually make it difficult top managerial making decisions as well as the selection of the best employees periodically into a long and complicated process. Therefore, it is necessary to build a decision support system that can help facilitate the decision maker in determining the best choice based on standard criteria, faster, and more objective. In this research, the computational method of decision-making system used is Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). The criteria used in the selection of the best employees are: job responsibilities, work discipline, work quality, and behaviour. The final result of the global priority value of the best employee candidates is used as the best employee selection decision making tool by top management.

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Section: Introductioncontrasting

confidence: 43%

TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees

Siahaan¹,

Rahim²,

Listyorini³

2018

Preprint

View full text Add to dashboard Cite

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“…The classical TOPSIS normalized the decision matrix by vector normalization. Yaakob and Gegov [36] extended the classical TOPSIS method into the Z-numbers environment and implemented it in the stock selection problem, but that method could not support the MCDM with target criteria. We extend the TOPSIS method into the Z-fuzzy environment by combining it with the target-based vector normalization method (i.e., Equation (14)).…”

Section: Solving the Case By The Z-topsis Methodsmentioning

confidence: 99%

“…Compared with the classical fuzzy set, the Z-number takes into account the uncertainty in information generation process and the reliability of information. At present, it has been combined with many MCDM methods such as TOPSIS [36,37], VIKOR [38], Multi-Objective Optimization by Ratio Analysis (MOORA) [39], COmbinative Distance-based Assessment (CODAS) [40], PROMETHEE [41], TODIM (an acronym in Portuguese of interactive and multicriteria decision-making) [37], AHP [42], BWM [43] and Data Envelopment Analysis (DEA) [44]. 1] are two TFNs, we can convert the Z-number to an ordinary fuzzy number [45].…”

Section: Z-numbermentioning

confidence: 99%

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

et al. 2020

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The sustainable development of cloud service providers (CSPs) is a significant multiple criteria decision making (MCDM) problem, involving the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and decision-experts for the selection of trustworthy CSPs. Most existing MCDM methods for CSP selection incorporated only one normalization technique in benefit and cost criteria, which would mislead the decision results and limit the applications of these methods. In addition, these methods did not consider the reliability of information given by decision-makers. Given these research gaps, this study introduces a Z-number-based double normalization-based multiple aggregation (DNMA) method to tackle quantitative and qualitative criteria in forms of benefit, cost, and target types for sustainable CSP development. We extend the original DNMA method to the Z-number environment to handle the uncertain and unreliability information of decision-makers. To make trade-offs between normalized criteria values, we develop a Gini-coefficient based weighting method to replace the mean-square-based weighting method used in the original DNMA method to enhance the applicability and isotonicity of the DNMA method. A case study is conducted to demonstrate the effectiveness of the proposed method. Furthermore, comparative analysis and sensitivity analysis are implemented to test the stability and applicability of the proposed method.Sustainability 2020, 12, 3410 2 of 17 future generations to meet their own needs" [2]. Sustainability requires sustainable business practices. For doing so, information systems play an important role in the organization transition towards sustainability initiatives. The sustainable information systems have been considered as an opportunity for organizations to improve productivity, reduce costs, and increase profitability [3]. The flexible, elastic and agile nature of cloud computing provides an opportunity for governmental, academic and business organizations to consider migrating their existing applications to cloud environments to make their business processes agile with minimal cost and management effort [4]. Due to the increase in the number of cloud service providers (CSPs) offering functionally similar cloud services with varying cost, features and quality, it becomes extremely complex and burdensome to select a dependable, scalable, sustainable and high cost-effective cloud service for consumers to fulfil and satisfy their requirements and business strategies. There is a pressing need of comprehensive techniques to help them for the sustainable CSP development [5,6].Sustainable CSP development can be formulated as a multicriteria decision-making (MCDM) problem [5]. It involves the intrinsic relations among multiple alternatives, (quantitative and qualitative) decision criteria and experts for selecting the trustworthy CSPs. Moreover, the CSP evaluation data often involves uncertain, unreliability, multi-scale and imprecise weights of quality of service (QoS)...

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“…To reflect and model the uncertainties, several mathematical models have been developed. As a traditional tool, probability theory was introduced to study the quantitative law of random phenomenon . In probability theory, a random event has two situations when making an observation, that is, may or may not occur.…”

Section: Introductionmentioning

confidence: 99%

Linguistic Z‐number fuzzy soft sets and its application on multiple attribute group decision making problems

et al. 2019

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In this study, the concept of linguistic Z-number fuzzy soft set (LZnFSS) is proposed to describe multiple uncertainties in practical decision making problems. LZnFSS combines the concepts of fuzzy soft set, linguistic Z-number, and soft set, which could reflect both of the uncertainty in structure and the uncertainty in detailed evaluations. As an initial idea, the set operations on LZnFSSs are put forward, the properties of such operations are also discussed. With traditional soft set based decision procedure and fuzzy soft set based decision procedure, a novel linguistic Z-number fuzzy soft set based group decision procedure is developed to solve multiattribute group decision making with linguistic Z-numbers. Wherein an extended technique for order preference by similarity to ideal solution is also developed. Finally, a numerical example is shown to illustrate the practicality and effectiveness of the given method. K E Y W O R D S linguistic Z-number, linguistic Z-number fuzzy soft set, multiattribute group decision making, soft set, TOPSIS

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Interactive TOPSIS Based Group Decision Making Methodology Using Z-Numbers

Cited by 101 publications

References 28 publications

TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees

TOPSIS Method Application for Decision Support System in Internal Control for Selecting Best Employees

Sustainable Cloud Service Provider Development by a Z-Number-Based DNMA Method with Gini-Coefficient-Based Weight Determination

Linguistic Z‐number fuzzy soft sets and its application on multiple attribute group decision making problems

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