A Fuzzy-Multi Attribute Decision Making approach for efficient service selection in cloud environments

Gireesha, Obulaporam; Kamalesh, A.B.; Kannan, K.; Sriram, V. S. Shankar

doi:10.1016/j.eswa.2022.117526

Cited by 22 publications

(7 citation statements)

References 76 publications

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“…Liang et al [ 18 ] integrated the EDAS (evaluation based on the distance from average solution) with ELECTRE (elimination and choice translating reality) models for evaluating cleaner production of mines; Wang et al [ 19 ] investigated the picture fuzzy MABAC (multi-attributive border approximation area comparison) technique to solve risk assessment problems of energy contracting projects; Ashraf et al [ 20 ] modified the TOPSIS method for solving decision-making issues in picture fuzzy surroundings; Wei et al [ 21 ] presented a picture fuzzy bidirectional projection approach to assess safety of construction project; Luo et al [ 22 ] selected the site of tailings pond with an extended WDBA (Weighted Distance Based Approximation) method under picture fuzzy circumstances; Gündoğdu et al [ 23 ] assessed public transport service quality with the AHP (Analytic Hierarchy Process) and linear assignment model in picture fuzzy surroundings; Aydoğmuş et al [ 24 ] developed a CODAS (Combinative Distance-based Assessment) algorithm to select suitable ERP (Enterprise Resource Planning) systems within picture fuzzy situations; Korucuk et al [ 25 ] proposed a CoCoSo (combined compromise solution) method with PFNs for evaluating ideal smart network strategies for logistics companies; Gireesha et al [ 26 ] introduced a picture fuzzy MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution) method to assess could service; and Yildirim and Yıldırım [ 27 ] modified VIKOR (VlseKriterijuska Optimizacija I Komoromisno Resenje) approach with picture fuzzy information to assess the satisfaction level of citizens in municipality services.…”

Section: Introductionmentioning

confidence: 99%

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Chen

Luo

2023

PLoS ONE

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The teaching quality evaluation of physical education is an important measure to promote the professional development of physical teachers, improve the quality of school teaching and personnel training. It is helpful for students to achieve all-round development and better meet the needs of modern talents in the new era. This study aims to establish a novel MCDM (multi-criteria decision-making) framework for evaluating teaching quality of physical education. First, PFNs (picture fuzzy numbers) are suggested to reflect dissimilar attitudes or preferences of decision makers. Then, the typical SWARA (Step-wise Weight Assessment Ratio Analysis) model is modified with PFNs to calculate the weights of evaluation criteria. Considering that some criteria are non-compensatory during the evaluation process, the idea of ELECTRE (elimination and choice translating reality) is introduced to obtain the ranking results of alternatives. Specially, the MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method is extended to construct the difference matrix in a picture fuzzy environment. Last, the hybrid MCDM model is utilized to assess the teaching quality of physical education. Its superiority is justified through comparison analyses. Results show that our approach is practicable and can provide instructions for the teaching quality evaluation of physical education.

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Section: Introductionmentioning

confidence: 99%

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Chen

Luo

2023

PLoS ONE

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“…The QoS attribute values are fuzzy sets. With the prevalent issues like vagueness, uncertainty, ambiguity, and inconsistency in the assessment data, recent works in MADM have employed fuzzy concepts like Neutrosophic fuzzy sets [7], Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs) [8], Picture Fuzzy Sets [17], etc. to deal with the same.…”

Section: ) Fuzzy Number Qos Modelmentioning

confidence: 99%

“…In addition to the above-mentioned common MADM methods used in service selection, there are also studies that apply MADM methods such as Elimination and Choice Expressing Reality (ELECTRE) [21], Best Worst Method (BWM) [22], Preference Ranking Organization Method for Enrichment of Evaluations (PROMETHEE) [23] to service selection. There have also been some cloud service MADM methods for interval number QoS model [14], fuzzy number QoS model [7][8] [17], and hybrid QoS model [18]. MADM technology has been proven to be very useful in selecting services with multiple attributes and different attribute weights, especially TOPSIS, which has been successfully applied to real-time decision-making problems.…”

Section: ) Hybrid Qos Modelmentioning

confidence: 99%

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

Zhang,

Bai

2024

IEEE Access

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Selecting the QoS-optimized software-as-a-service (SaaS) from a large number of services with the same functionality and different Quality of Service (QoS) is still a hot issue. Massive QoS feedback forms big QoS data, which exhibits ambiguity and randomness increasing the uncertainty of service selection. Starting from the characterization of big QoS data, the Uncertain Big QoS data-driven Efficient SaaS Decision Making Method (UBQoS_ESDM) is proposed. The method firstly utilizes cloud model to portray QoS in order to solve the problem of inaccurate description of uncertain big QoS data; then draws on the idea of Skyline query to establish uncertain service Skyline set, which reduces the search space and improves the efficiency of QoS-optimal SaaS selection; and draws on the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to design two decision-making algorithms to evaluate alternative SaaSs, and to obtain the QoS-optimal SaaS that reflects user requirements. In addition, two types of backward QoS cloud generators are introduced to convert big QoS data to QoS cloud models, and the QoS cloud model adaptive adjustment mechanism is introduced too, which can adapt to the dynamic changes of QoS. Finally, theoretical proofs and experiments verify the superiority and efficiency of the method.

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“…Liang proposed a deep reinforcement learning (DRL)-based QoS-aware service composition model for cloud manufacturing considering logistics (Liang et al ., 2021). Cloud service selection has also been envisaged as a multi-attribute decision-making (MADM) problem due to the intrinsic relationship among the multiple QoS parameters (Gireesha et al. , 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

Wang

Hu²,

Wang³

2023

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PurposeBy introducing the mass customization service mode into the cloud logistics environment, this paper studies the joint optimization of service provider selection and customer order decoupling point (CODP) positioning based on the mass customization service mode to provide customers with more diversified and personalized service content with lower total logistics service cost.Design/methodology/approachThis paper addresses the general process of service composition optimization based on the mass customization mode in a cloud logistics service environment and constructs a joint decision model for service provider selection and CODP positioning. In the model, the two objective functions of minimum service cost and most satisfactory delivery time are considered, and the Pareto optimal solution of the model is obtained via the NSGA-II algorithm. Then, a numerical case is used to verify the superiority of the service composition scheme based on the mass customization mode over the general scheme and to verify the significant impact of the scale effect coefficient on the optimal CODP location.Findings(1) Under the cloud logistics mode, the implementation of the logistics service mode based on mass customization can not only reduce the total cost of logistics services by means of the scale effect of massive orders on the cloud platform but also make more efficient use of a large number of logistics service providers gathered on the cloud platform to provide customers with more customized and diversified service content. (2) The scale effect coefficient directly affects the total cost of logistics services and significantly affects the location of the CODP. Therefore, before implementing the mass customization logistics service mode, the most reasonable clustering of orders on the cloud logistics platform is very important for the follow-up service combination.Originality/valueThe originality of this paper includes two aspects. One is to introduce the mass customization mode in the cloud logistics service environment for the first time and summarize the operation process of implementing the mass customization mode in the cloud logistics environment. Second, in order to solve the joint decision optimization model of provider selection and CODP positioning, this paper designs a method for solving a mixed-integer nonlinear programming model using a multi-layer coding genetic algorithm.

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A Fuzzy-Multi Attribute Decision Making approach for efficient service selection in cloud environments

Cited by 22 publications

References 76 publications

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework

Uncertain Big QoS Data-Driven Efficient SaaS Decision-Making Method

Joint optimization decision of service provider selection and CODP positioning based on mass customization in a cloud logistics environment

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