A Multi Criteria Decision Making to Support Major Selection of Senior High School

Senior high school students need to select a specialization, such as Mathematics and Natural Sciences, Social Sciences, or Language and Culture. This selection process can be improved by using Multi Criteria Decision Making (MCDM) methods. When MCDM methods are implemented, AHP method has accuracy of 61%, whereas AHP-Fuzzy TOPSIS 1 and AHP-Fuzzy TOPSIS 2 have accuracy of 75%. This research implements tests and analyzes new MCDM method, which is Hybrid MCDM Model, in helping aforementioned specialization selection process. There are four basic steps in Hybrid MCDM Model: performing experimental design to obtain attributes' weight and criteria, evaluating MCDM with the three existing methods, performing RSM regression to derive mathematical model, and decision making. This research introduces data normalization to the mathematical model which results in better implementation of Hybrid MCDM Model in the senior high school students' specialization selection process. Hybrid MCDM Model in the senior high school student specialization selection has accuracy of 86%, which includes 11% accuracy improvements compared to other applied MCDM methods

COMPARATIVE ANALYSIS OF SOME PROMINENT MCDM METHODS: A CASE OF RANKING SERBIAN BANKS

In the literature, many multiple criteria decision making methods have been proposed. There are also a number of papers, which are devoted to comparison of their characteristics and performances. However, a definitive answer to questions: which method is most suitable and which method is most effective is still actual. Therefore, in this paper, the use of some prominent multiple criteria decision making methods is considered on the example of ranking Serbian banks. The objective of this paper is not to determine which method is most appropriate for ranking banks. The objective of this paper is to emphasize that the use of various multiple criteria decision making methods sometimes can produce different ranking orders of alternatives, highlighted some reasons which lead to different results, and indicate that different results obtained by different MCDM methods are not just a random event, but rather reality

WEBIRA - comparative analysis of weight balancing method

The attributes weight establishing problem is one of the most important MCDM tasks. This study summarizes weight determining approach which is called WEBIRA (WEight Balancing Indicator Ranks Accordance). This method requires to solve complicated optimization problem and its application is possible by carrying out non trivial calculations. The efficiency of WEBIRA and other MCDM methods – SAW (Simple Additive Weighting) and EMDCW (Entropy Method for Determining the Criterion Weight) compared for 4 different data normalization methods. The re-sults of the study revealed that more sophisticated WEBIRA method is significantly efficient for all considered numbers of alternatives. Efficiency of all methods decreases with increasing number of alternatives, but WEBIRA is still applicable, while appli-cation of other methods is impossible as the number of alternatives is greater than 11. WEBIRA is the least affected by the data normalization, while EMDCW is the most affected method

Efficient radio resource management in next generation wireless networks

The current decade has witnessed a phenomenal growth in mobile wireless communication networks and subscribers. In 2015, mobile wireless devices and connections were reported to have grown to about 7.9 billion, exceeding human population. The explosive growth in mobile wireless communication network subscribers has created a huge demand for wireless network capacity, ubiquitous wireless network coverage, and enhanced Quality of Service (QoS). These demands have led to several challenging problems for wireless communication networks operators and designers. The Next Generation Wireless Networks (NGWNs) will support high mobility communications, such as communication in high-speed rails. Mobile users in such high mobility environment demand reliable QoS, however, such users are plagued with a poor signal-tonoise ratio, due to the high vehicular penetration loss, increased transmission outage and handover information overhead, leading to poor QoS provisioning for the networks' mobile users. Providing a reliable QoS for high mobility users remains one of the unique challenges for NGWNs. The increased wireless network capacity and coverage of NGWNs means that mobile communication users at the cell-edge should have enhanced network performance. However, due to path loss (path attenuation), interference, and radio background noise, mobile communication users at the cell-edge can experience relatively poor transmission channel qualities and subsequently forced to transmit at a low bit transmission rate, even when the wireless communication networks can support high bit transmission rate. Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed. The performance of proposed ATMA CAC scheme is investigated and compare it with the traditional CAC scheme. The ATMA scheme exploits the mobility events in the highspeed mobility communication environment and the calls (new and handoff calls) generation pattern to enhance the QoS (new call blocking and handoff call dropping probabilities) of the mobile users. The numbers of new and handoff calls in wireless communication networks are dynamic random processes that can be effectively modeled by the Continuous Furthermore, the NGWNs are envisioned to be Heterogeneous Wireless Networks (HWNs). The NGWNs are going to be the integration platform of diverse homogeneous wireless communication networks for a convergent wireless communication network. The HWNs support single and multiple calls (group calls), simultaneously. Decision making is an integral core of radio resource management. One crucial decision making in HWNs is network selection. Network selection addresses the problem of how to select the best available access network for a given network user connection. For the integrated platform of HWNs to be truly seamless and efficient, a robust and stable wireless access network selection algorithm is needed. To meet these challenges for the different mobile wireless communication network users, the NGWNs will have to provide a great leap in wireless network capacity, coverage, QoS, and radio resource utilization. Moving wireless communication networks (mobile hotspots) have been proposed as a solution to providing reliable QoS to high mobility users. In this thesis, an Adaptive Thinning Mobility Aware (ATMA) Call Admission Control (CAC) algorithm for improving the QoS and radio resource utilization of the mobile hotspot networks, which are of critical importance for communicating nodes in moving wireless networks is proposed

Using Pythagorean Fuzzy Sets (PFS) in Multiple Criteria Group Decision Making (MCGDM) Methods for Engineering Materials Selection Applications

The process of materials’ selection is very critical during the initial stages of designing manufactured products. Inefficient decision-making outcomes in the material selection process could result in poor quality of products and unnecessary costs. In the last century, numerous materials have been developed for manufacturing mechanical components in different industries. Many of these new materials are similar in their properties and performances, thus creating great challenges for designers and engineers to make accurate selections. Our main objective in this work is to assist decision makers (DMs) within the manufacturing field to evaluate materials alternatives and to select the best alternative for specific manufacturing purposes. In this research, new hybrid fuzzy Multiple Criteria Group Decision Making (MCGDM) methods are proposed for the material selection problem. The proposed methods tackle some challenges that are associated with the material selection decision making process, such as aggregating decision makers’ (DMs) decisions appropriately and modeling uncertainty. In the proposed hybrid models, a novel aggregation approach is developed to convert DMs crisp decisions to Pythagorean fuzzy sets (PFS). This approach gives more flexibility to DMs to express their opinions than the traditional fuzzy and intuitionistic sets (IFS). Then, the proposed aggregation approach is integrated with a ranking method to solve the Pythagorean Fuzzy Multi Criteria Decision Making (PFMCGDM) problem and rank the material alternatives. The ranking methods used in the hybrid models are the Pythagorean Fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) and Pythagorean Fuzzy COPRAS (COmplex PRoportional Assessment). TOPSIS and COPRAS are selected based on their effectiveness and practicality in dealing with the nature of material selection problems. In the aggregation approach, the Sugeno Fuzzy measure and the Shapley value are used to fairly distribute the DMs weight in the Pythagorean Fuzzy numbers. Additionally, new functions to calculate uncertainty from DMs recommendations are developed using the Takagai-Sugeno approach. The literature reveals some work on these methods, but to our knowledge, there are no published works that integrate the proposed aggregation approach with the selected MCDM ranking methods under the Pythagorean Fuzzy environment for the use in materials selection problems. Furthermore, the proposed methods might be applied, due to its novelty, to any MCDM problem in other areas. A practical validation of the proposed hybrid PFMCGDM methods is investigated through conducting a case study of material selection for high pressure turbine blades in jet engines. The main objectives of the case study were: 1) to investigate the new developed aggregation approach in converting real DMs crisp decisions into Pythagorean fuzzy numbers; 2) to test the applicability of both the hybrid PFMCGDM TOPSIS and the hybrid PFMCGDM COPRAS methods in the field of material selection. In this case study, a group of five DMs, faculty members and graduate students, from the Materials Science and Engineering Department at the University of Wisconsin-Milwaukee, were selected to participate as DMs. Their evaluations fulfilled the first objective of the case study. A computer application for material selection was developed to assist designers and engineers in real life problems. A comparative analysis was performed to compare the results of both hybrid MCGDM methods. A sensitivity analysis was conducted to show the robustness and reliability of the outcomes obtained from both methods. It is concluded that using the proposed hybrid PFMCGDM TOPSIS method is more effective and practical in the material selection process than the proposed hybrid PFMCGDM COPRAS method. Additionally, recommendations for further research are suggested

Bibliometric analysis of scientific production on methods to aid decision making in the last 40 years

Purpose: Multicriteria methods have gained traction in both academia and industry practices for effective decision-making over the years. This bibliometric study aims to explore and provide an overview of research carried out on multicriteria methods, in its various aspects, over the past forty-four years. Design/Methodology/Approach: The Web of Science (WoS) and Scopus databases were searched for publications from January 1945 to April 29, 2021, on multicriteria methods in titles, abstracts, and keywords. The bibliographic data were analyzed using the R bibliometrix package. Findings: This bibliometric study asserts that 29,050 authors have produced 20,861 documents on the theme of multicriteria methods in 131 countries in the last forty-four years. Scientific production in this area grows at a rate of 13.88 per year. China is the leading country in publications with 14.14%; India with 10.76%; and Iran with 8.09%. Islamic Azad University leads others with 504 publications, followed by the Vilnius Gediminas Technical University with 456 and the National Institute of Technology with 336. As for journals, Expert Systems With Applications; Sustainability; and Journal of Cleaner Production are the leading journals, which account for more than 4.67% of all indexed literature. Furthermore, Zavadskas E. and Wang J have the highest publications in the multicriteria methods domain regarding the authors. Regarding the most commonly used multicriteria decision-making methods, AHP is the most favored approach among the ten countries with the most publications in this research area, followed by TOPSIS, VIKOR, PROMETHEE, and ANP. Practical implications: The bibliometric literature review method allows the researchers to explore the multicriteria research area more extensively than the traditional literature review method. It enables a large dataset of bibliographic records to be systematically analyzed through statistical measures, yielding informative insights. Originality/value: The usefulness of this bibliometric study is summed in presenting an overview of the topic of the multicriteria methods during the previous forty-four years, allowing other academics to use this research as a starting point for their research

E-commerce development risk evaluation using MCDM techniques

© Springer International Publishing Switzerland 2016. Electronic commerce (EC) development takes place in a complex and dynamic environment that includes high levels of risk and uncertainty. This paper proposes a new method for assessing the risks associated with EC development using multi-criteria decision-making techniques A model based on the analytic hierarchy process (AHP) and the technique for order of preference by similarity to ideal solution (TOPSIS) is proposed to assist EC project managers and decision makers in formalizing the types of thinking that are required in assessing the current risk environment of their EC development in a more systematic manner than previously. The solution includes the use of AHP for analyzing the problem structure and determining the weights of risk factors. The TOPSIS technique helps to obtain a final ranking among projects, and the results of an evaluation show the usefulness performance of the method

A hybrid multicriteria approach to GPR image mining applied to water supply system maintenance

[EN] Data processing techniques for Ground Penetrating Radar (GPR) image mining provide essential information to optimize maintenance management of Water Supply Systems (WSSs). These techniques aim to elaborate on radargrams in order to produce meaningful graphical representations of critical buried components of WSSs. These representations are helpful non-destructive evaluation tools to prevent possible failures in WSSs by keeping them adequately monitored. This paper proposes an integrated multi-criteria decision making (MCDM) approach to prioritize various data processing techniques by means of ranking their outputs, namely their resulting GPR image representations. The Fuzzy Analytic Hierarchy Process (FAHP) is applied to weight various evaluation criteria, with the purpose of managing vagueness and uncertainty characterizing experts' judgments. Then, the Elimination Et Choix Traduisant la REalite III (ELECTRE III) method is used to obtain the final ranking of alternatives. A real case study, focusing on a set of four GPR images as outputs of different data processing techniques, is presented to prove the usefulness of the proposed hybrid approach. In particular, the GPR images are ranked according the evaluation of five criteria namely visualization, interpretation, identification of features, extraction of information and affordability. The findings offer a structured support in selecting the most suitable data processing technique(s) to explore WSS underground. In the presented case, two options, namely the variance filter and the subtraction methods, offer the best results. (C) 2018 Elsevier B.V. All rights reserved.Part of this work has been developed under the support of the Universitat Politecnica de Valencia, Valencia (Spain), grant: UPV mobility program for PhD students, awarded to the first author, and of Fundacion Carolina PhD, within its short stage scholarship program awarded to the second author.Carpitella, S.; Ocaña-Levario, SJ.; Benítez López, J.; Certa, A.; Izquierdo Sebastián, J. (2018). A hybrid multicriteria approach to GPR image mining applied to water supply system maintenance. Journal of Applied Geophysics. 159:754-764. https://doi.org/10.1016/j.jappgeo.2018.10.021S75476415

Multi-criteria decision making in civil engineering. Part II – applications

The first part of the paper shortly presented developments of multi-criteria decision making (MCDM) methods and general data about their use in civil engineering, i.e. distribution by years, countries, authors and journals (Zavadskas et al. 2015). The current part of the paper focuses on MCDM application areas and domains. Web of Science Category “Engineering Civil” in Thomson Reuters Web of Science Core Collection academic data base is searched for a topic of MCDM. Only articles and review document types are selected for a detailed survey. They are grouped by Research Areas as presented in Web of Science data base. The most numerous research areas as Construction Building Technology, Transportation, Water Resources and Engineering (other topics) are analysed in detail. Research domains and solved problems are described as well as applied MCDM methods are highlighted. A total of 114 articles are reviewed, showing a wide possibilities of applying MCDM methods for civil engineering problems

Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS

© 2017 IEEE. Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business