Multi-criteria Ranking Research Articles

Comparison of multicriteria decision-making methods in portfolio formation

Investors apply various methods to select stocks and construct an investment portfolio. In the majority of methods, the principle of diversification is relevant. Also, sometimes investors‘ behaviour generate biases related to portfolio formation. Multicriteria decision-making methods can overcome such shortcomings of investors' decision-making; thus, they are widely used for portfolio selection. In the performed research, the portfolio is constructed from the stocks of the Spanish stock market. Stocks are selected based on financial indicators. SAW and TOPSIS multicriteria methods are used to range the suitable stocks. Portfolio weights are proportionate to the obtained multicriteria rank. Characteristics of the final selected stocks are presented graphically. Expected portfolio return and risk are also described when comparing two portfolios. The results of the research prove that multicriteria decision-making methods are suitable for portfolio formation. However, such portfolios should be kept for a long time in order to receive a return.

Prioritization of sub-watersheds of the Kanakapura Watershed in the Arkavathi River Basin, Karnataka, India- using Remote sensing and GIS

ABSTRACT Prioritization of sub-watersheds is a crucial aspect of watershed management as it may not be possible to execute development programs at a time for entire watersheds in a basin owing to the paucity of financial resources. The present study attempts to prioritize sub-watersheds in the Kanakapura watershed, in Ramanagara district, Karnataka, based on the multi-criteria ranking method. This process involves extraction and determination of various parameters such as rainfall, lithology, drainage density, slope, lineament, hydro geomorphology, land-use, and land-cover, using information from rain gauges, topographical sheets, and satellite imagery in Geographic Information System (GIS) platform. Kanakapura watershed is subdivided into nine sub-watersheds, namely, Bannimukudlu, Bennagodu, Doddaalahalli, Gadasahalli, Horalagallu, Kodihalli, Madarahalli, Maralebbekupe, and Mudagod. Priorities indexing of each of the parameters was done, based on their impact significance, and overlay analysis was performed to identify critical sub-watersheds. Some of the strategies proposed for effective conservation and management of water resources are the construction of a check dam, implementation of water harvesting methods, identification of artificial recharge sites. The results of the study show that priority needs to be given to the Bannimukudlu and Kodihalli sub-watersheds followed by remaining sub-watersheds to regulate surface water flow thereby improving water table level.

Multi-Criteria Ranking of Green Materials According to the Goals of Sustainable Development

Modern, well-educated and experienced policy-makers support and promote the use of environmentally friendly materials and resources. The use of green resources is an exceptional and inevitable strategy to meet the needs of a rapidly growing Earth population. The growing population raises the need for new housing construction and urban infrastructure development. Such substances in construction refer to green building materials (GBMs). The environmental impact is lower if GBMs replace non-GBMs. Here, ranking among GBMs can facilitate and support the selection process. This study aimed to contribute to the body of knowledge to introduce a method for identifying and prioritizing GBMs in the construction industry to use in green building. The required data were collected using existing literature, interviews and questionnaires. Relevant Sustainable Development Goals (SDGs) are the first criteria for assessing GBM selection criteria. Critical weighted GBM selection criteria are the second criteria for prioritizing GBMs. The results show that “Natural, Plentiful and Renewable”, “Affordability from cradle to gate” and “Affordability during operation” are the top three GBM selection criteria. The real case study helped select “Stramit Strawboard”, “Aluminium Composite Panels (ACPs)” and “Solar Roof Tiles” as the most suitable GBMs for use in the context of the study. The model and results presented in this study will help actors of the construction industry to select and use GBMs more quickly and thus achieve a better level of construction sustainability, as well as environmental friendliness, than before.

Safety performance & evaluation framework in Indian construction industry

IntroductionThis paper proposes a framework to evaluate the safety performance of workers in Indian construction industry. The key indicators to measure the safety and health conditions are identified and prioritized on the basis of the importance in the establishment of safe and healthy work environment. Design/methodology/approachFrom the literature review and inputs from the construction managers, key safety indicators were identified. The prioritization was accomplished using multi-criteria ranking method (MCRM). Dominance-based Rough Set (DRSA) is used for the ranking of indicators. It is based on Rough set theory which draws motivation from the indiscernibility relation be- tween the objects. The importance of each indicator is assessed by MCRM method in the contribution towards establishing a safe workplace environ- ment from different perspectives in a construction industry that includes management from top, middle and low-level management. FindingsThe role and involvement of management in the operations and con- stant interaction with workers were found to have a significant impact on the safety performance of the firms. Imparting necessary safety training to the employees and promoting a friendly atmosphere at the workplace aids in building the confidence of the workers that subsequently make them safety compliant. Dedicated staff to ensure that the workers are following safety protocols enlisted in safety manuals acquires an important place in the measurement of the safety performance of the firms. Practical implicationsThe findings of the current study will help the senior management in the small scale Indian construction industry to gain a perspective to- wards the assessment of safety condition in the firms and establishing an efficient and easy-to-use safety assessment framework. It will also be useful for middle and low-level management, since they should read the results as a positive effect of their constant interaction with workers. Management is suggested to design a well-organized and detailed manual that docu- ments the safety-related information including the proper use of safety equipment and emergency procedures. Originality/valueThis paper proposes a safety framework for small-scale Indian construction industry for calibration and benchmarking safety performance of the employees. The mathematical foundation of the framework is the multi-criteria ranking based on Dominance based Rough set approach.

A Supplier Selection Model for a Wholesaler and Retailer Company Based on FITradeoff Multicriteria Method

The problem approached is regarding supplier selection in a wholesaler and retailer of the construction sector. The current approach used in the company analyzed for the supplier selection problem is unable to evaluate the many issues that the company considers must be addressed. Thus, the Flexible and Interactive Tradeoff (FITradeoff) ranking method was chosen to deal with this problem. It seeks to elicit criteria weights in a decision problem, using partial information about the decision maker's (DM’s) preferences in order to choose the most attractive alternative, based on the ranking of the suppliers provided. Based on the Hasse diagram developed when applying the method, the DM makes some observations regarding the suppliers' positions in the ranking. Bar graphics and radar graphics were also used and these enable the DM to evaluate each supplier better. It was perceived that the method is applicable and well accepted by the DM. The graphic part helps the DM in understanding the analysis of the problem, offering no barriers to proceed with the tradeoff. Thus, for this situation of selecting a supplier in a wholesaler and retailer of the construction sector, the DM felt comfortable with the proposed approach using the FITradeoff multicriteria ranking method and with the results presented, and the proposed approach is shown to be adequate.

Modified interval EDAS approach for the multi-criteria ranking problem in banking sector of Iran

In this study an important real-world multi-criteria problem in banking sector is focused. The aim of this problem is to rank some branches of a bank in Iran subject to some economic criteria under interval type uncertainty. The problem is complex because of its multi-criteria nature and also its interval type data. A new solution procedure is proposed for this problem. In the proposed procedure first the criteria are weighted as interval values according to an effective method which combines experts’ opinions and the Shannon’s entropy approach. Then, as a novelty, the classical EDAS multi-criteria solution approach is newly modified for interval type data in order to overcome the shortcomings of the interval EDAS approaches of the literature. Finally, using the data obtained from the case study, an extensive computational study is performed by the proposed solution procedure. The sensitivity of the proposed approach also is analyzed by changing its parameters and a comparative study was done by some other approaches.

PRIORITIZATION of ROAD TRANSPORTATION RISKS: AN APPLICATION in GİRESUN PROVINCE

The purpose of this study is to determine and rank the road transportation risk factors that are crucial for effective and economic supply chain management. Road transportation risk factors can be defined as equipment related risks, risk to be lost and disappearance, risks related to delivery and packaging, inadequacy of qualified personnel and technical equipment, risks caused from incompatibility to logistic information system/technology, security risk, compulsory reasons, risks originated from regulations and arrangements, risks related to waiting at customs gate and transport infrastructure based risks. Accordingly, fuzzy PIPRECIA as a multi-criteria ranking method was used to prioritize the risk factors. According to the results, while the transport infrastructure based risks criterion was found as the most important, the risk to be lost and disappearance factor was obtained as the least important one.

Application of Superiority and Inferiority Multi-criteria Ranking Method for Parametric Optimization of Laser Cutting Processes

Laser cutting of advanced engineering materials has now become quite attractive due to its various unique characteristics, like no tool wear, no built-up edge formation, no residual stress generation, no vibration etc. In order to enable this machining process to operate at its maximum competency, its’ different input parameters are often set at their optimal operating levels. This paper presents the applicability of superiority and inferiority multi-criteria ranking (SIR) method for determination of the best-suited parametric combinations for an Nd:YAG and a pulsed CO2 (carbon dioxide) laser cutting processes. The derived optimal parametric mixes obtained for the two considered laser cutting processes are validated based on the developed regression equations. In the Nd:YAG laser cutting process, an optimal parametric combination of gas pressure = 3 kg/cm2, pulse width = 0.6 ms, pulse frequency = 23 Hz, and cutting speed = 20 mm/min reduces the values of kerf width, kerf deviation, and kerf taper angle by 1.71, 2 and 25.88%, respectively. For the pulsed CO2 laser cutting process, the optimal mix determined as laser beam power = 3000 W, cutting velocity = 10 mm/s, gas pressure = 1.2 MPa and pulse frequency = 10 Hz causes reduction of 25.81, 6.65 and 2.84% in the values of surface roughness, kerf width and cut edge slope, respectively. Thus, it can be established that the response values estimated using the SIR method are better than those derived by the other optimisation techniques, while demonstrating its potentiality for parametric optimisation of laser cutting processes leading to enhanced performance.

Decision Support System for Determination of Development of Small and Medium Industries Using VIKOR

As a basis for regional economic growth, the existence of small and medium industries is very influential for people’s lives. But on the other hand the parties involved are very rarely optimize their potential, because of the many small and medium industries owned by the community, especially in Bangkalan Madura. One effort to overcome difficulties in optimizing the potential of small and medium industries is the application of determining the priority for the development of SMEs. This application uses the VIKOR method with the criteria of Investment Value, Production Capacity, Production Value, Value of Raw Materials and Labor. VIKOR is a ranking method using a multi-criteria ranking index based on a certain measure of proximity to the ideal solution. So this application will choose a small and medium industry that is suitable for assistance in its development. The test results show that from making this application using the Vikor method has an accuracy rate of 73%.

The application of PROMETHEE multi-criteria decision aid in financial decision making: Case of distress prediction models evaluation

Conflicting rankings corresponding to alternative performance criteria and measures are mostly reported in the mono-criterion evaluation of competing distress prediction models (DPMs). To overcome this issue, this study extends the application of the expert system to corporate credit risk and distress prediction through proposing a Multi-criteria Decision Aid (MCDA), namely PROMETHEE II, which provides a multi-criteria evaluation of competing DPMs. In addition, using data on Chinese firms listed on Shanghai and Shenzhen stock exchanges, we perform an exhaustive comparative analysis of the most popular DPMs; namely, statistical, artificial intelligence and machine learning models under both mono-criterion and multi-criteria frameworks. Further, we address two prevailing research questions; namely, "which DPM performs better in predicting distress?" and "will training models with corporate governance indicators (CGIs) enhance the performance of models?”; and discuss our findings. Our multi-criteria ranking suggests that non-parametric DPMs outperform parametric ones, where random forest and bagging CART are among the best machine learning DPMs. Further, models fed with CGIs as features outperform those fed without CGIs.

Uncertainty of the implementation time of geodynamic monitoring system in multi-criteria ranking of alternatives

<p>The paper deals with the problem of ranking alternatives to geodynamic monitoring systems in the case of uncertainty of their implementation time. The problem is characterized by the fact that the choice of alternatives and the effect of it depends on the quality properties of the applied organizational and technical solutions, taking into account the time of implementation. The ordering of alternatives is proposed taking into account the uncertainty of the implementation time factors. Ranking is realized by comparing the trees of functional characteristics of alternatives taking into account the compliance of their characteristics with time-varying requirements. The scope of the proposed method is the choice of configurations of geodynamic monitoring systems with significant differences in the implementation time of alternatives.</p>

Combination of HF set and MCDM for stable clustering in VANETs

Fast-moving nodes in vehicular ad hoc network (VANET) make network topology very dynamic, which deteriorates communication reliability and scalability. To overcome this problem, a hierarchical topology can be created using a clustering algorithm. This study presents a novel hesitant fuzzy (HF) multi-criteria ranking framework to deal with cluster-heads (CHs) election problem in VANET. An analogy is suggested between ‘ranking of HF elements’ and ‘CH selection in VANET’. Within the proposed framework, a multiple criteria decision-making (MCDM) method should be used to calculate vehicle eligibility to become CH. In this study, technique for order of preference by similarity to ideal solution (TOPSIS), Vlse Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) and EVAluation of MIXed data (EVAMIX) MCDM methods are applied. A simulation study, under a highway scenario, is conducted to investigate the performance of HF-TOPSIS, HF-VIKOR and HF-EVAMIX-based clustering algorithms. Obtained results show that CH election based on HF-EVAMIX leads to more stable clustering in comparison with HF-TOPSIS, HF-VIKOR and the conventional threshold-based method.

A novel approach for choosing dynamic community detection algorithms using PROMETHEE II

In this paper, a decision support tool is developed in order to assist senior managers to choose a dynamic community detection algorithm. The main objective behind this novel approach is to improve the search strategy of algorithms to decrease the search time of the best fitting one according to the study case. The suggested study ranks existing dynamic community detection algorithms in order to find the most appropriate one according to some preferences. For that purpose, we need a multi-criteria ranking method such as PROMETHEE II. This latter allows ranking alternatives from the best to the least according to some criteria extracted from the analysis of a provided questionnaire on dynamic community detection algorithms. The process of our approach is as follows: first, the questionnaire is provided to some experts in community detection domain. Second, answers are collected and analyzed in order to extract criteria. Third, a ranking of the algorithms with PROMETHEE II is operated according some extracted criteria and user preferences. Finally, the first ranked algorithm by PROMETHEE II is provided to the user in order to detect the communities.

Integrating multi-criteria analysis with PDCA cycle for sustainable energy planning in Africa: Application to hybrid mini-grid system in Cameroon

Rural communities in Africa are often poorly electrified and rely on polluting fuels for energy. Apart from the attendant health effects and greenhouse gas emissions from these energy sources, these systems are unreliable and more costly especially in long term use. Proposing a renewable energy installation plan for these communities often creates conflicting scenarios; requiring prioritization and opportunity cost. An improved MCDM method is an ideal approach that can solve this problem properly. This paper thus proposes a hybrid model that is based on integrating AHP-VIKOR with the PDCA cycle, for continuous improvement of the planning process. AHP and VIKOR were utilized for multi-criteria ranking to determine the optimal hybrid technology combination. The uniqueness of this model is the management support characteristic of the PDCA cycle that provides coherence between the planning process and the actual project, as well as criticisms and responses for the proposed system. The results show that the selected hybrid energy system does not only guarantee a reliable system configuration with unmet load of 2.45 kWh/year, it also has economic benefits over the least cost system and a standard diesel generator system respectively: income from power sales of 3,816.7$/year and 745.11$/year; and savings from carbon pricing of 68,810.17$/year and 8,323,774.7$/year.

Automation of multicriteria ranking of students using ePortfolio

Introduction. The paper considers improving the management quality of an educational organization through automating the personalized data collection, storage and ranking. The authors have developed a module of the university corporate system, an ePortfolio of students, which implements a multicriteria optimization method for calculating the students’ rating on the basis of the collected and stored data. The study object is multicriteria ranking methods. The subject of the study is the automated calculation of student’s rating within the electronic portfolio. The study objective is to develop tools for collecting, storing and processing data on individual achievements of students and the implementation of the mathematical method of multicriteria optimization for ranking students on the basis of the portfolio data. Practical implications include development of a tool for an effective management of the educational process.Materials and Methods. A prototype module of the university corporate system “ePortfolio” on the platform of 1C: Enterprise 8.3 is presented. To rank students, a special case of the alternative ranking method is implemented in the block of the data analysis module – pairwise comparison in order of their relative importance. At that, a unified scale of relations was used.Research Results. The authors described the scheme of the information system (IS) operation “ePortfolio”, presented a process interaction pattern for the portfolio formation, as well as a diagram of business processes under calculating an individual rating. A fragment of the sample is shown on which the performance of the multicriteria optimization block has been tested. The criteria of the calculations are described, as well as the rule of screening alternatives for solving for the optimum.Discussion and Conclusion. The paper presents the results of the IS block operation on ranking a list of students. The calculation results coincide with the practical data.

A new model for coreference resolution based on knowledge representation and multi-criteria ranking

Coreference resolution is critical for improving the performance of all text-based systems including information extraction, document summarization, machine translation, and question-answering. Most of coreference resolution solutions rely on using knowledge resources like lexical knowledge, syntactic knowledge, world knowledge and semantic knowledge. This paper presents a new knowledge-based coreference resolution model using neural network architecture. It uses XLNet embeddings as input and does not rely on any syntactic or dependency parsers. For more efficient span representation and mention detection, we used entity-level information. Mentions were extracted from the text with an unhand engineered mention detector, and the features were extracted from a deep neural network. We also propose a nonlinear multi-criteria ranking model to rank the candidate antecedents. This model simultaneously determines the total score of alternatives and the weight of the features in order to speed up the process of ranking alternatives. Compared to the state-of-the-art models, the simulation results showed significant improvements on the English CoNLL-2012 shared task (+6.4 F1). Moreover, we achieved 96.1% F1 score on the n2c2 medical dataset.

Edge Computing Intelligence Using Robust Feature Selection for Network Traffic Classification in Internet-of-Things

Internet-of-Things (IoT) devices are massively interconnected, which generates a massive amount of network traffic. The concept of edge computing brings a new paradigm to monitor and manage network traffic at the network’s edge. Network traffic classification is a critical task to monitor and identify Internet traffic. Recent traffic classification works suggested using statistical flow features to classify network traffic accurately using machine learning techniques. The selected classification features must be stable and can work across different spatial and temporal heterogeneity. This paper proposes a feature selection mechanism called Ensemble Weight Approach (EWA) for selecting significant features for Internet traffic classification based on multi-criterion ranking and selection mechanisms. Extensive simulations have been conducted using publicly-available traces from the University of Cambridge. The simulation results demonstrate that EWA is capable of identifying stable features subset for Internet traffic identification. EWA-selected features improve the mean accuracy up to 1.3% and reduce RMSE using fewer features than other feature selection methods. The smaller number of features directly contributes to shorter classification time. Furthermore, the selected features can train stable traffic classification generative models irrespective of the dataset’s spatial and temporal differences, with consistent accuracy up to 97%. The overall performance indicates that EWA-selected statistical flow features can improve the overall traffic classification.

MODEL OF A FUZZY DYNAMIC DECISION SUPPORT SYSTEM

Within the framework of this article, the analysis of the ways of building an automated decision support system for business and technological processes based on the thematic apparatus of the theory of fuzzy functions is carried out. The purpose of decision support systems is to help the decision maker (business owners, top managers of companies, heads of state organizations and law enforcement agencies) in choosing the most optimal solution based on the information at his disposal. It is proposed to use mathematical methods based onfuzzy multicriteria ranking as methods for making a decision. The paper proposes a model of an automated decision support system for business and technological processes of organizations based onthe mathematical apparatus of the theory of fuzzy functions, which allows automating the process of choosing a solution and eliminating the influence of subjective factors when assessing possible decisions.

Comparative study of Topk based on Fagins algorithm using correlation metrics in cloud computing QoS

With the exponential growth of cloud computing services recently, several internet technologies began to require the processing of multi-criteria ranking. The collaborative filtering methods and Topk selection computations have been proven to be more effective in information retrieval. In addition, they are widely used to evaluate the QoS for cloud services recommendation. However, the biggest challenge is not only to reduce the size of skyline results, but also to have a good response quality that reflects the user requirement. To deal with these problems, we propose in this paper an approach based on Topk algorithm combined with the weighted sum method. This approach is introduced for refining the skyline result using the Topk query advantages. Then in order to evaluate the performance of our approach, we compared the proposed algorithm with Fagin's one. The experimental results show the efficiency of our algorithm particularly in comparing the runtime results and using specific metrics of correlation.

COMPLEMENTARITY OF THE GRAPHICAL ANALYSIS FOR INTERACTIVE AID AND DOMINANCE-BASED ROUGH SET APPROACH APPLIED TO THE CLASSIFICATION OF NON-URBAN MUNICIPALITIES

Graphical analysis for interactive aid (GAIA) and the dominance-based rough set approach (DRSA) are compared as methods of explaining the solution to a multi-criteria ranking problem obtained using the preference ranking organization method for the enrichment of evaluations (PROMETHEE). The classification of 52 municipalities in Northern Quebec in terms of the socioeconomic situation is based on three attributes: home conditions, employment and demographic potential. The classification provided to the decision maker is aggregated information. To facilitate decision-making, the problem is first considered as a sorting task, in which municipalities are distributed into three categories: best (B), worst (W) or intermediate (I), based on the PROMETHEE ranking. In order to improve the position of a municipality thus categorized, the decision maker needs information that will answer the questions: What criteria are relevant to the municipality? What criteria are in conflict? What are the critical values of the criteria? We show that GAIA and DRSA provide convergent and complementary information that allow enrichment of the answers to these questions. Keywords: management decision support, multi-criteria analysis, GAIA, dominance-based rough set approach.