Energy efficient hierarchical resource management for mobile cloud computing

Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is an algorithm that can be used for alternative design in a decision support system (DSS). TOPSIS provides recommendation so that users can get information that support their decision, for example a tourist wants to visit a tourist destination in Malang, then TOPSIS provides recommendations of tourist destinations in the form of ranking recommendation, with the highest rank is the most recommended recommendation. TOPSIS-based Mobile Decision Support System (DSS) has relatively low algorithm complexity. However, there are some cases that require development from personal DSS to group DSS, for example tourists rarely come alone, in which case most of them invite friends or family. For users who are more than 1 person, the TOPSIS algorithm can be combined with the BORDA algorithm. This study explains about the implementation & testing of TOPSIS and TOPSIS-BORDA as algorithms for personal and group DSS in mobile-based tourism recommendation system in Malang. Correlation testing was conducted to test the effectiveness of TOPSIS in mobile-based recommendation system. In previous study, correlation testing for personal DSS showed that there was a relationship between the recommendation and user choice, with correlation value of 0.770769231. In this study, correlation testing for group DSS showed there is a positive correlation of 0.88 between the recommendations of the group produced by TOPSIS-BORDA and personal recommendations for each user produced by TOPSIS.

The fuzzy TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) is an attractive tool for measuring complex phenomena based on uncertain data. The original version of the method assumes that the object assessments in terms of the adopted criteria are expressed as triangular fuzzy numbers. One of the crucial stages of the fuzzy TOPSIS is selecting the fuzzy conversion scale, which is used to evaluate objects in terms of the adopted criteria. The choice of a fuzzy conversion scale may influence the results of the fuzzy TOPSIS. There is no uniform approach in constructing and selecting the fuzzy conversion scale for the fuzzy TOPSIS. The choice is subjective and made by researchers. Therefore, the aim of the article is to present a new, objective approach to the construction of fuzzy conversion scales based on Item Response Theory (IRT) models. The following models were used in the construction of fuzzy conversion scales: Polychoric Correlation Model (PM), Polytomous Rasch Model (PRM), Rating Scale Model (RSM), Partial Credit Model (PCM), Generalized Partial Credit Model (GPCM), Graded Response Model (GRM), Nominal Response Model (NRM). The usefulness of the proposed approach is presented on the example of the analysis of a survey’s results on measuring the quality of professional life of inhabitants of selected communes in Poland. The obtained results indicate that the choice of the fuzzy conversion scale has a large impact on the closeness coefficient values. A large difference was also observed in the spreads of triangular fuzzy numbers between scales based on IRT models and those used in the literature on the subject. The use of the fuzzy TOPSIS with fuzzy conversion scales built based on PRM, RSM, PCM, GPCM, and GRM models gives results with a greater range of variability than in the case of fuzzy conversion scales used in empirical research.

SummaryIn a vehicular ad‐hoc network (VANET), each vehicle is equipped with an on‐board unit to communicate vehicle to vehicle or vehicle to fixed infrastructure. VANET technology is offered to provide many facilities to passengers and drivers, including safety, entertainment, mobile commerce, driver assistance, and emergency alarms. VANET has unique features such as high‐speed node mobility and network topology dynamics. These special features cause many problems such as increased transmission delays and packet loss. On the other hand, providing a good routing plan for VANET is a critical issue. Therefore, this article proposes a cluster‐based routing using in‐vehicle meta‐heuristic algorithms (CRMHA‐VANET) which has two phases. In the first stage, the vehicles are clustered and the most suitable cluster head (CH) is selected using the gray wolf optimization algorithm (GWO). In the next step, the next suitable CH is selected for data transmission in direct paths using the technique for order of preference by similarity to ideal solution (TOPSIS). The performance of the proposed method is analyzed through several criteria such as package delivery rate, end‐to‐end delay and throughput. CRMHA‐VANET results in a 10% to 25% improvement over all performance metrics, that is, packet delivery rate, latency, and throughput, over CRBP (clustering routing based on PSO [particle swarm optimization]), WCV (weight based clustering for VANET), and AODV‐CD methods.

Application of AHP, TOPSIS, and TFNs to plant selection for phytoremediation of petroleum-contaminated soils in shale gas and oil fields

In software defect prediction systems, choosing the best data mining classification algorithm is essential for efficient decision-making. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method for ranking classification algorithms according to several criteria is assessed in this study. Classifiers were evaluated and ranked using the AR1 and JM1 datasets using a structured multi-criteria decision-making (MCDM) methodology. According to the results, Lazy-IBK continuously performs better than other classifiers, demonstrating how well TOPSIS finds the top-performing models. Furthermore, the comparative study of classification algorithms shows how reliable the approach is when taking into account variables like scalability, accuracy, and computational efficiency. According to the results, TOPSIS considerably enhances classifier selection when it comes to software defect prediction, resulting in data mining applications that make better-informed and impartial decisions. Key Words: MCDM, Software Defect Prediction, TOPSIS

A research on the cutting database system based on machining features and TOPSIS

<p>One common multi-criteria decision making (MCDM) technique is the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), which is frequently applied in several application fields. Finding an ideal and an anti-ideal solution, which are then utilized to determine the distances between the alternatives and the ideal solution, is the foundation of the TOPSIS approach. The method then ranks the alternatives according to their closeness to the ideal solution. TOPSIS is able to handle both quantitative and qualitative criteria, however, the method can be sensitive to the weight of the criteria, and the ranking results can be influenced by the choice of the reference alternatives. This paper provides an overview of the TOPSIS method, its applications, main characteristics and limitations. The paper also provides step-by-step instructions on how to apply the TOPSIS method, including the determination of the criteria weights, the construction of the decision matrix, and the calculation of the TOPSIS scores.</p>

The importance of disaster logistics and its share in the logistics sector are increasing significantly. Most disasters are difficult to predict; therefore, a set of measures seems to be necessary to reduce the risks. Thus, disaster logistics needs to be designed with the pre-disaster and post-disaster measures. These disasters are experienced intensely in Turkey and the importance of these measures becomes more evidential. Therefore, accurate models are required to develop an effective disaster preparedness system. One of the most important decisions to increase the preparedness is to locate the centres for handling material inventory. In this context, this paper analyses the response phase designing the disaster distribution centres in Turkey at the provincial level. AHP (Analytical Hierarchy Process) based TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and goal programming model integration is used to decide alternative locations of distribution centres. TOPSIS method is employed for ranking the locations, which is based on hazard scores, total area, population, and distance to centre. Two conflicting objectives are first proposed in the goal programming formulation, in which maximization of the TOPSIS scores and minimization of the number of distribution centres covering all demands named set covering model are included. Although Gecimli has the highest priority with 0.8 p score in the TOPSIS ranking, Altincevre (0.77) and Buzlupınar (0.75) ensure both the TOPSIS score and coverage of the demand nodes. The results from this paper confirm that the computational results ensure disaster prevention insights especially in regions with limited data.

The application of multiple criteria decision-making (MCDM) techniques in real-life problems has increased in recent years. The need to build advanced decision models with higher capabilities that can support decision-making in a broad spectrum of applications, promotes the integration of MCDM techniques with applicable systems, including artificial intelligence, and Geographic Information Systems (GIS). The Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are among the most widely adopted MCDM techniques capable of resolving water resources challenges. A critical problem associated with water resource management is dam site selection. This paper presents a comparative analysis of TOPSIS and AHP in the context of decision-making using GIS for dam site selection. The comparison was made based on geographic and water quality criteria. The geographical criteria are geology, land use, sediment, erosion, slope, groundwater, and discharge. The water quality criteria include Soluble Sodium Percentage, Total Dissolved Solid, Potential of Hydrogen, and Electrical Conductivity of water. A ratio estimation procedure was used to determine the weights of these criteria. Both methods were applied for selection of optimal sites for dams in the Sistan and Baluchestan province, Iran. The results show that the TOPSIS method is better suited to the problem of dam site selection for this study area. Actual locations of dams constructed in the area were used to verify the results of both methods.

This study presents an integrated framework for evaluating the structural condition of existing reinforced concrete buildings using a combination of rebound hammer testing, the Hybrid Risk Assessment Method (RAM), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Four buildings of varying ages and usage types were selected to capture diverse structural conditions and exposure environments. Rebound hammer tests, conducted in accordance with IS 13311 (Part 2): 1992, provided in-situ compressive strength estimates, while RAM quantified the severity, probability, and detectability of potential failures to derive a Risk Priority Number (RPN) for each structure. These risk scores, along with building age and rebound values, were incorporated into the TOPSIS multi-criteria decision-making framework to rank the buildings by their proximity to an ideal condition. Results indicated that Building 3 exhibited the highest relative closeness to the ideal solution (RCi = 1.0), followed by Building 2, Building 1, and Building 4, the latter having the highest risk and lowest performance. The integrated RAM–TOPSIS approach demonstrated its effectiveness in combining qualitative expert judgment with quantitative field data, enabling a robust, data-driven prioritization of maintenance interventions. This methodology enhances the reliability of non-destructive testing outcomes and supports cost-effective, safety-focused asset management for existing structures. Keywords- Building condition assessment, Non-destructive testing, Rebound hammer test, Risk Assessment Method (RAM), TOPSIS

This study focuses on analyzing the feasibility of pension loan applicants for state civil servants at PT. BPR DP TASPEN, with a focus on the use of the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) as an alternative analysis method. Previously, PT. BPR DP TASPEN uses a subjective evaluation method to assess the feasibility of loan applicants, which leads to potential risks and errors in the analysis. In this study, the authors recommend the use of TOPSIS as a support decision-making system to standardize the analytical process. TOPSIS was chosen for its efficiency in reducing the calculation time and ease of implementation. This method uses criteria and alternatives to calculate, produces the best alternative based on a weighted bobot, and provides the final decision. The results of the study indicate that TOPSIS provides a good solution for determining the feasibility of pension loan applicants. By considering criteria such as age, pension, debt, bank loan, health history, and income, only seven out of 40 loan applicants met the best criteria, which were close to the ideal positive solution. The selected applicants were recommended for approval as pension loans by the company, reducing the potential risks of credit and improving the accuracy of the selection of loan criteria.

PurposeThe purpose of this study is to provide a decision support tool to deal with the problem of seting priorites among patients competing for limited health care resources. Limited resources and unlimited demands prevent health-care services to be provided to all those in need. This became publicity evident with the current Covid-19 pandemic. Although controversial, health care rationing has always existed and is now inevitable. Setting priorities becomes then inevitable. How to define those priorities is a complex and yet irresolvable issue mainly because it involves several and conflicting criteria, translated into efficiency and equity considerations. This is why multi-criteria decision analysis (MCDA) was introduced to health care as an appropriate decision-support framework for solving complex problems.Design/methodology/approachThis paper proposes the application of two combined approaches – analytic hierarchy process (AHP)-Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and AHP-VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR), as decision support tools to rank patients with competing needs in a more effective and equitable way. A rationing scenario involving four patients, differentiated by personal characteristics and health conditions, is used to illustrate, test and compare the applicability of both approaches. After extraction of the relative weights of the prioritization criteria involved in the hypothetical scenario from paired wise comparison methods, TOPSIS and VIKOR priority setting methods were designed.FindingsResults suggest that patients ranking from both combination approaches are similar and in accordance with the order made directly by health-care professionals. Therefore, the relative weights computed by AHP in combination with TOPSIS and/or VIKOR methods could be used with suitable applicability by health-care decision-makers.Originality/valueThis study is the first attempt to apply a combination of MCDA methods to patients’ prioritization context and the first to cross previous studies to deepen and consolidate the research.

Penelitian ini bertujuan untuk merancang dan mengimplementasikan Executive Support System (ESS) yang efektif menggunakan metode Simple Additive Weighting (SAW), Analytic Hierarchy Process (AHP), dan Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) dalam konteks perencanaan strategis. Kemampuan ESS dalam mendukung pengambilan keputusan eksekutif dihadapkan pada kompleksitas faktor dan kriteria yang melibatkan banyak aspek bisnis. Metode SAW digunakan untuk memberikan nilai relatif pada setiap kriteria, memberikan pendekatan yang sederhana dan mudah dimengerti. AHP digunakan untuk menangani penilaian kriteria secara hierarkis, memungkinkan penetapan bobot yang lebih akurat berdasarkan tingkat hierarki kriteria. TOPSIS digunakan untuk memilih alternatif terbaik dengan mempertimbangkan kedekatan dengan solusi ideal. Penelitian ini adalah penelitian kuantitatif dengan menggunakan metode kepustaakaan. Hasil penelitian menunjukkan bahwa integrasi metode SAW, AHP, dan TOPSIS dalam ESS memberikan kontribusi positif dalam meningkatkan kemampuan sistem dalam memberikan dukungan yang lebih optimal bagi pengambilan keputusan eksekutif. Kesederhanaan SAW, penilaian hierarkis AHP, dan ketepatan pemilihan TOPSIS bekerja bersama untuk memberikan solusi yang akurat dan efisien.

Circular Economy and Industry 4.0 Technologies Introduction: Markets that promote product reuse rather than product elimination and subsequent extraction of new resources are examples of a circular economy. All waste categories, including clothing, scrap metal, and outdated technology, are recycled or put to better use in such economies. This approach can offer a solution not only to safeguard the environment but also to use natural resources more wisely and produce new industries, jobs, and skill sets. A paradigm of production and consumption known as the "circular economy" incorporates sharing, leasing, reuse, repair, refurbishment, and recycling to lengthen the package's life cycle. The primary goal of a circular economic model is to design out waste. This model is predicated on the idea that there is no such thing as waste. To accomplish this, last-generation products are made with high-quality materials and are amenable to the extraction and reuse cycle, making it easier for users to handle, modify, or update them. The circular economic model is ultimately distinguished by these short product cycles, with the exception of disposal and recycling, which waste a significant amount of embodied energy and labor. The ultimate objective is to manage finite stocks and balance renewable resource flows to protect and enhance natural capital. Research significance: The circular economic model distinguishes between the cycles of technology and biology. Consumption only occurs during biological cycles when biologically-based goods are intended to replenish the system through procedures like food, compost, or anaerobic digestion of cork, linen, or other materials. These cycles recreate environments that give the economy renewable resources, such as soil or the ocean. Technical cycles, on the other hand, restore and reuse products through tactics like recycling, reusing, repairing, or producing something new. Ultimately, one of the goals of the circular economy is to increase resource yields by focusing on the components, goods, and services that are circulated and used most frequently across the technological and biological cycles. The concept of the circular economy gained popularity in China in the 1990s in response to economic expansion and the depletion of natural resources. The central idea of the circular economy concept is to strike a balance between resource and environmental use, capitalizing on material flow and recycling, and economic growth Methodology: ideal solution (TOPSIS) is prioritized through unity is a technique that provides, this is a multicriteria decision analytical method. TOPSIS stands for (PIS). Short geometric distance alternative to select is the positive ideal solution, basically distance to have ideal solution of thought (nis) negative too long from is geometry. Of TOPSIS the assumption is even greater is, is coming or going the criteria are the same are increasing. Many parameters in scaling problems or criteria often improper dimensions normalizations due to having are generally required. Alternative: Reliability, Responsiveness, Agility, Costs, Active management efficiency. Evaluation preference: Regenerate, Share, Optimise, Loop, Virtualise, Exchange. Results: From the result it is seen that Share is got the first rank where as is the Regenerate is having the lowest rank.

Watershed prioritization using morphometric analysis by MCDM approaches