Pairwise Comparison Process Research Articles

The next event will be held with more quality: Identifying and prioritizing barriers to knowledge management in sports events

AbstractEffective knowledge management can help in better performance of projects such as sports events, and it can prevent the reinvention of systems, ideas, and also the repetition of errors. However, considering their temporary nature, projects face serious barriers in effective knowledge management. Therefore, the purpose of this research was to identify and prioritize the barriers of knowledge management in sports events. The statistical population included sports managers and also professors who were knowledgeable about the research subject. Barriers to knowledge management in sports events were identified in two steps. In the first step, the literature was reviewed, and then an interview was conducted with the samples, and in this way, three indicators and 19 sub‐indices were identified. Next, a questionnaire was designed. After collecting the questionnaires, the barriers were ranked through the process of pairwise comparisons in an analytic hierarchy process. The results showed that the most important barriers to knowledge management in the sports event are managerial barriers, with a relative weight of 0.521; barriers related to human resources, with a relative weight of 0.356; and infrastructural and support barriers, with a relative weight of 0.123. The results showed the importance of managerial barriers in the knowledge management of sports events, which shows the necessity of informing and training managers in this area.

Augmented visualization by computer vision and chromatographic fingerprinting on comprehensive two-dimensional gas chromatographic patterns: Unraveling diagnostic signatures in food volatilome

Computer Vision is an approach of Artificial Intelligence (AI) that conceptually enables “computers and systems to derive useful information from digital images” giving access to higher-level information and “take actions or make recommendations based on that information”. Comprehensive two-dimensional chromatography gives access to highly detailed, accurate, yet unstructured information on the sample's chemical composition, and makes it possible to exploit the AI concepts at the data processing level (e.g., by Computer Vision) to rationalize raw data explorations. The goal is the understanding of the biological phenomena interrelated to a specific/diagnostic chemical signature. This study introduces a novel workflow for Computer Vision based on pattern recognition algorithms (i.e., combined untargeted and targeted UT fingerprinting) which includes the generation of composite Class Images for representative samples' classes, their effective re-alignment and registration against a comprehensive feature template followed by Augmented Visualization by comparative visual analysis. As an illustrative application, a sample set originated from a Research Project on artisanal butter (from raw sweet cream to ripened butter) is explored, capturing the evolution of volatile components along the production chain and the impact of different microbial cultures on the finished product volatilome. The workflow has significant advantages compared to the classical one-step pairwise comparison process given the ability to realign and pairwise compare both targeted and untargeted chromatographic features belonging to Class Images resembling chemical patterns from many different samples with intrinsic biological variability.

Evaluation Criteria for the Suitability of Apple Cultivation in Kashmir Valley, India

The study aims to provide viable criteria to assess land suitability for apple growing in Kashmir Valley, India. It used a Delphi survey and the analytical hierarchical process (AHP) approach to determine factors influencing apple production. The evaluation criteria were obtained through multiple rounds of the Delphi survey of stakeholders. The Delphi outcome was extended to enable prioritization of the criteria, using a pair-wise comparison process. Results show that experts value physical and economic considerations (accounting for 87% of the overall weight) more than social factors. Furthermore, location-specific factors (i.e., cost-benefit ratio and landholding size) have significant bearings on land suitability for apple in the study area. The Delphi-AHP method proved to be useful as it incorporated core issues of agricultural land use by creating a common framework for suitability evaluation and eliminated uncertainties in the decision-making process.

Cyclic sequential process of pairwise comparisons with application to multi-criteria decision making

Cyclic sequential process of pairwise comparisons with application to multi-criteria decision making

Climatic and Hydrological Factors Affecting the Assessment of Flood Hazards and Resilience Using Modified UNDRR Indicators: Ayutthaya, Thailand

This research aims to investigate the effect of climatic and hydrological factors on flood hazards and assess flood resilience in Ayutthaya, Thailand, using the 10 essentials for making cities resilient modified by the United Nations Office for Disaster Risk Reduction (UNDRR). Flood resilience assessment was performed based on a multi-criteria decision-making approach or the analytical hierarchy process (AHP) of pairwise comparison. The results indicate that runoff is considered the most influential factor in flood hazards, followed by land use, rainfall, and historical flood events, sequentially. Regarding the flood incident management concept, a questionnaire survey (n = 552) was conducted to understand the impacts of flood on local communities. The findings reveal that 50% of respondents had never received any flood information or participated in training sessions on flood preparedness. Most reported their concerns about the inadequate supply of drinking water during a flood. Spearman’s correlation coefficient shows positive correlations between flood disaster relief payments, preparedness training, access to flood hazard mapping, emergency health services, and their flood preparation actions. According to the modified UNDRR indicators, the top three highest AHP values in building community resilience to flood hazards in Ayutthaya are flood risk scenario identification, the effectiveness of emergency flood disaster response, integrated urban planning, and disaster risk reduction. The policy implications of this research include the need for national authorities to better understand the role cities can play a vital role in supporting both national and international climate resilience frameworks, especially Thailand’s National Disaster Management Plan, the Sendai Framework for Disaster Risk Reduction (SFDRR), and the global Sustainable Development Goals (SDGs).

A TYPOLOGY FOR MCDM METHODS BASED ON THE RATIONALITY OF THEIR PAIRWISE COMPARISON PROCEDURES

MCDM methods have been proposed to select, rank, classify, or describe alternatives based on the process of pairwise comparison. While the typologies of MCDM methods present in the literature usually are focused on their amalgamation phase, this paper proposes a typology for classifying MCDM methods based on the rationality of their pairwise comparison procedures. Accordingly, four discriminants were used to provide support in choosing MCDM methods based on the typology proposed. The proposed typology allows the analyst to identify the type of rationality from MCDM methods that best fits the multicriteria problem.

Sustainable Quality of Life Criteria for Ledang National Park Community: Fuzzy Approach

Recently, there is a rising concern among researchers regarding the issue of sustainable criteria for quality of life (QoL). As it gains more interest, the issue becomes highly debated worldwide especially with regards to the national park community area. The aim of this study is to assess the identified sustainable criteria for QoL, particularly for the surrounding community of Ledang National Park (LNP) using the fuzzy Analytical Network Process (ANP). The fuzzy ANP was employed based on graded mean integration of representation and canonical representation of multiple operations to derive both local and global weights. To show the feasibility of the proposed method, three decision makers (DMs) were identified from the relevant agencies to assess three main criteria using linguistic evaluation via pairwise comparison process. In addition, nine sub-criteria were also investigated and analysed thoroughly using six steps of the fuzzy ANP towards achieving the sustainable criteria for QoL assessment. Based on the numerical analysis, it was found that the sub-criterion health(c32) has the highest global weight with a score of 0.184, which indicates that this sub-criterion is the biggest contributor to achieve the sustainability of QoL. The results also revealed the overall total score of 73.14%, thus placing LNP in the ‘moderately sustainable’ category. In the future, the entire investigated sub-criteria are suggested to be maintained and used to measure the sustainability of QoL. The findings from this study can be used to guide and assist the relevant authorities for future development planning in studied areas.

Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank

Objective – The disruption has been occurring in financial services. Thus, rethinking a new strategy for banking is needed to make a sustainable innovation in organizations. Studies mentioned that formulating strategy is a very costly, time-consuming, and comprehensive analysis. The purpose of this study is to present an integrated intelligence algorithm for estimating the bank’s strategy in Indonesia. Methodology – This study used the integration model between two modules. The algorithm has two basic modules, called Artificial Neural Network (ANN) and Analytical Hierarchy Process (AHP). AHP is capable of handling a multi-level decision-making structure with the use of five expert judgments in the pairwise comparison process. Meanwhile, ANN is utilized as an inductive algorithm in discovering the predictive strategy of the bank and used to explain the strategic factors which improved in forward. Findings and Novelty – The empirical results indicate that ANN and AHP integration was proved to predict the business strategy of the bank in five scenarios. Strategy 5 was the best choice for the bank and Innovate Like Fintechs (ILF) is the most factor consideration. The strategy choice was appropriate for the condition of the bank’s factors. This framework can be implemented to help bankers to decide on bank operations. Type of Paper: Empirical JEL Classification: M15, O32. Keywords: Bank’s strategy, ANN, AHP, BSC, Indonesia. Reference to this paper should be made as follows: Ristyawan, M.R. (2021). Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank, Journal of Finance and Banking Review, 5(4): 01 – 09. https://doi.org/10.35609/jfbr.2021.5.4(1)

Multicriteria Decision Analysis and Group Decision-Making to Select Stand-Level Forest Management Models and Support Landscape-Level Collaborative Planning

Forest management planning is a challenge due to the diverse criteria that need to be considered in the underlying decision-making process. This challenge becomes more complex in joint collaborative management areas (ZIF) because the decision now may involve numerous actors with diverse interests, preferences, and goals. In this research, we present an approach to identifying and quantifying the most relevant criteria that actors consider in a forest management planning process in a ZIF context, including quantifying the performance of seven alternative stand-level forest management models (FMM). Specifically, we developed a combined multicriteria decision analysis and group decision-making process by (a) building a cognitive map with the actors to identify the criteria and sub-criteria; (b) structuring the decision tree; (c) structuring a questionnaire to elicit the importance of criteria and sub-criteria in a pairwise comparison process, and to evaluate the FMM alternatives; and (d) applying a Delphi survey to gather actors’ preferences. We report results from an application to a case study area, ZIF of Vale do Sousa, in North-Western Portugal. Actors assigned the highest importance to the criteria income (56.8% of all actors) and risks (21.6% of all actors) and the lowest to cultural services (27.0% of all actors). Actors agreed on their preferences for the sub-criteria of income (diversification of income sources), risks (wildfires) and cultural services (leisure and recreation activities). However, there was a poor agreement among actors on the sub-criteria of the wood demand and biodiversity criteria. For 27.0% of all actors the FMM with the highest performance was the pedunculate oak and for 43.2% of all actors the eucalypt FMM was the least preferable alternative. The findings indicate that this approach can support ZIF managers in enhancing forest management planning by improving its utility for actors and facilitating its implementation.

Improvement of Analytic Hierarchy Process Based on Grey Correlation Model and Its Engineering Application

The complexity of the pairwise comparison process in the analytic hierarchy process (AHP) and the limitation of consistency are challenges that need to be addressed when there are many evaluation factors. An improved AHP is established by integrating the grey correlation model (GCM) and AHP, for which the tedious pairwise comparison process can be abandoned. The improved method integrates the advantages of grey system theory (GST) and AHP, overcomes the inherent limitations of AHP, and can be used to analyze many different types of problems that were previously analyzed by AHP or GST. In this paper, the improvement process is presented in detail, and the effectiveness of the improved AHP method is verified through several existing examples. This work uses the case of the Suning Square project in Langfang, Hebei Province, China, to apply the proposed improved AHP to construct a comprehensive evaluation system. The proposed method is used to evaluate the construction risk of foundation pit excavation, thereby providing technical guidance for efficient implementation of the project.

Artificial Neural Network and Analytical Hierarchy Process Integration: A Tool to Estimate Business Strategy of Bank

The disruption has been occurring in financial services. Rethinking new strategy of banking is needed to make a sustainable competitive advantage innovation in organizations. The four types of business strategy for banks are prospector, analyzer, defender, and reactor. Studies mentioned that formulating strategy is very costly, time consuming, and comprehensive analysis. The banks have to get rid of execution time inefficiency, lack of flexibility, and lack of ability to present several scenarios in the dynamic business environment. The purpose of this study is to present an integrated intelligence algorithm for estimating strategic resources of the bank strategy in Indonesia. The algorithm has two basic modules which are artificial neural network (ANN) and analytical hierarchy process (AHP). ANN is utilized as an inductive algorithm in discovering predictive strategy of the bank and used to explain the strategic resources which improved in forward. AHP has the capability to handle multi-level decision-making structure with use of expert judgments in pairwise comparison process. AHP is used to measure the weight of the resources and the score is used to determine the strategy. The empirical results indicate that ANN and AHP integration is proved to predict the business strategy of the bank. The strategy choice appropriate with the condition of bank's resources. This framework can be implemented to help banker for the decision making in bank operation. Keywords: business strategy, ANN, AHP, resources

Sustainability Assessment System for Highrise Buildings in Iraq

This study aims to preparation a standards code for sustainability requirements to contribute in a better understanding to the concept of sustainability assessment systems in the dimensions of Iraqi projects in general and in the high-rise building. Iraq is one of the developing countries that faced significant challenges in sustainability aspects environmental, economic and social, it became necessary to develop an effective sustainability building assessment system in respect of the local context in Iraq. This study presented a proposal for a system of assessing the sustainability requirements of Iraqi high rise buildings (ISHTAR), which has been developed through several integrated steps, begin with investigates the well-known sustainability systems, experts consensus based on the Delphi technique involving 32 experts consider as sustainability world leading and local highly-informed to identify applicable assessment categories and factors for the Iraqi context and its weighting system, then analyze the results using the pairwise comparison and Analytic Hierarchy Process (AHP) technique.

A decision making model based on the leading principal submatrices of a reciprocal preference relation

A completed comparison matrix is the basic tool in the analytic hierarchy process (AHP). In practice, the process of forming a comparison matrix is complex and it is worth being investigated carefully. In this study, by decomposing pairwise comparison process of alternatives, the leading principal submatrices (LPSMs) of a completed comparison matrix are used as the basis for decision analysis. Based on the particle swarm optimization (PSO), a novel method for improving consistency of inconsistent comparison matrices is proposed. The fitness function is constructed by considering the acceptable consistency of pairwise comparison matrices and the similarity degree between the initial and the adjusted decision-making information. A new algorithm is elaborated on for solving a decision making problem with multiplicative reciprocal matrices. Numerical results are reported to show the advantages of the proposed model by comparing with the other methods. The observations reveal that the proposed method and algorithm are effective when dealing with inconsistent comparison matrices and the corresponding decision making problems.

Decision making with a sequential modeling of pairwise comparison process

The Analytic Hierarchy Process (AHP) is considered to be the natural way to realize decision making with pairwise comparisons of alternatives. Within the framework of the AHP model, the full process of forming comparison matrices is considered as an indivisible whole; and the irrational and uncertain behavior of decision makers has been identified and highlighted. In practice, the process of comparing alternatives is complex, especially when the number of alternatives is large. In this study, we propose a sequential modeling of wise comparison process for alternatives, which subsumes the typical AHP model as a special case. A new mathematical framework based on the leading principal submatrices is formed. The process of quantifying priorities of alternatives and the inconsistency-degree process of judgements are proposed to capture the irrationality and uncertainty experienced by decision makers in pairwise comparisons. It is found that the rank preservation is natural according to the comparison process. The acceptable consistency of multiplicative reciprocal matrices is redefined under the principle of weak rationality. A feedback mechanism is proposed to help decision makers avoid irrational behavior and to produce a better outcome of the decision procedure. Some comparisons with the AHP model demonstrate that the proposed model offers a clear way to capture the irrational and uncertain behavior of decision makers when evaluating the importance of alternatives.

From NIMBY to BIMBY: An evaluation of aesthetic appearance and social sustainability of MSW incineration plants in China

The Not-In-My-BackYard (NIMBY) claims caused by the rapid expansion of waste incineration in China make stakeholders pay more attention to the environmental and social impact. Nowadays, the environmental impact is under control of the whole society while the social sustainability is often neglected. As the social sustainability is vital to the public acceptance and conducive to convert NIMBY claims to Beauty-In-My-BackYard (BIMBY) synergy, all stakeholders desire to know the gap but lack evaluation method. This paper proposes a quantitative evaluation system for the aesthetic appearance of MSW incineration plants for the first time. It uses aesthetic appearance to measure the social sustainability of MSW incineration plants. Real-world photos of 177 incineration plants in China were collected to support the evaluation using weighted sum method. The weights of factors contributing to the aesthetic appearance were obtained by AHP. The process of pairwise comparison in AHP was conducted by an online questionnaire survey involving more than 100 interviewees in various professions. It is evaluated that the aesthetic appearance is not correlated with the pollution control ability. The spatial distribution of different levels of aesthetic appearance shows that eastern provinces should pay more attention to the improvement of old incineration plants. As case studies, the designs of four typical plants are analyzed. The analysis results reveal the gap between NIMBY plants and BIBMY plants. More importantly, they can contribute to push forward the improvements of aesthetic appearance, social sustainability, and public acceptance for MSW incineration plants in China.

FastJT: An R package for robust and efficient feature selection for machine learning and genome-wide association studies

BackgroundParametric feature selection methods for machine learning and association studies based on genetic data are not robust with respect to outliers or influential observations. While rank-based, distribution-free statistics offer a robust alternative to parametric methods, their practical utility can be limited, as they demand significant computational resources when analyzing high-dimensional data. For genetic studies that seek to identify variants, the hypothesis is constrained, since it is typically assumed that the effect of the genotype on the phenotype is monotone (e.g., an additive genetic effect). Similarly, predictors for machine learning applications may have natural ordering constraints. Cross-validation for feature selection in these high-dimensional contexts necessitates highly efficient computational algorithms for the robust evaluation of many features.ResultsWe have developed an R extension package, fastJT, for conducting genome-wide association studies and feature selection for machine learning using the Jonckheere-Terpstra statistic for constrained hypotheses. The kernel of the package features an efficient algorithm for calculating the statistics, replacing the pairwise comparison and counting processes with a data sorting and searching procedure, reducing computational complexity from O(n2) to O(n log(n)). The computational efficiency is demonstrated through extensive benchmarking, and example applications to real data are presented.ConclusionsfastJT is an open-source R extension package, applying the Jonckheere-Terpstra statistic for robust feature selection for machine learning and association studies. The package implements an efficient algorithm which leverages internal information among the samples to avoid unnecessary computations, and incorporates shared-memory parallel programming to further boost performance on multi-core machines.

Evidence-Based Integrated Analysis of Environmental Hazards in Southern Bolivia.

The “Valles Cruceños” rural region plays a fundamental role for securing food and other resources for the neighboring, and fast sprawling, city of Santa Cruz de la Sierra (Bolivia). Due to the increasing pressure on its natural resources, the region is affected by progressive and severe environmental degradation, as many other rural regions in South and Central America. In this situation, sound policies and governance for sustainable land management are weak and not supported by data and scientific research outputs. With the present study, we aim at developing a novel and practical integrated hazard analysis methodology, supporting the evidence-based understanding of hazard patterns and informing risk assessment processes in the urban-rural continuum. Firstly, the main environmental hazards affecting the area were identified via questionnaire campaigns, held by the staff of local municipalities. Focusing on the hazards mostly perceived by the inhabitants of the region, including deforestation, water pollution and precipitation changes, hazard maps were created by using multiple environmental hazards indicators. An integrated hazard map was then built in a GIS environment, after a pair-wise comparison process. The maps represent a first baseline for the analysis of the present status of natural resources in “Valles Cruceños” area, and the proposed approach can be scaled up for integrated environmental hazards analysis in similar areas of Latin America.

Role of the effective payoff function in evolutionary game dynamics

In most studies regarding evolutionary game dynamics, the effective payoff, a quantity that translates the payoff derived from game interactions into reproductive success, is usually assumed to be a specific function of the payoff. Meanwhile, the effect of different function forms of effective payoff on evolutionary dynamics is always left in the basket. By introducing a generalized mapping that the effective payoff of individuals is a non-negative function of two variables on selection intensity and payoff, we study how different effective payoff functions affect evolutionary dynamics in a symmetrical mutation-selection process. For standard two-strategy two-player games, we find that under weak selection the condition for one strategy to dominate the other depends not only on the classical σ-rule, but also on an extra constant that is determined by the form of the effective payoff function. By changing the sign of the constant, we can alter the direction of strategy selection. Taking the Moran process and pairwise comparison process as specific models in well-mixed populations, we find that different fitness or imitation mappings are equivalent under weak selection. Moreover, the sign of the extra constant determines the direction of one-third law and risk-dominance for sufficiently large populations. This work thus helps to elucidate how the effective payoff function as another fundamental ingredient of evolution affects evolutionary dynamics.

Measuring inconsistency and deriving priorities from fuzzy pairwise comparison matrices using the knowledge-based consistency index

The fuzzy analytic hierarchy process (AHP) is a widely applied multiple-criteria decision-making (MCDM) technique, making it possible to tackle vagueness and uncertainty arising from decision makers, especially in a pairwise comparison process. Indeed, as the human brain reasons with uncertain rather than precise information, pairwise comparisons may involve some degree of inconsistency, which must be correctly managed to guarantee a coherent result/ranking. Several consistency indexes for fuzzy pairwise comparison matrices (FPCMs) have been proposed in the literature. However, some scholars argue that most of these fail to be axiomatically grounded, which may lead to misleading results. To overcome this lack of an axiomatically grounded index, a new index is proposed in this paper, referred to as the knowledge-based consistency index (KCI). A comparative study of the proposed index with an existing one is carried out, and the results show that KCI contributes to substantially reducing the computation time. In addition, the different fuzzy weights derived from the initial FPCM (for KCI computation purposes) can also be employed to find a crisp set of weights that corresponds to an optimal solution to the MCDM problem according to the decision maker’s viewpoint and expertise.

Simulated Hesitant Fuzzy Linguistic Term Sets-Based Approach for Modeling Uncertainty in AHP Method

One of the important points in decision-making is representing the uncertainty. There have been developed approaches which are based on either fuzzy sets theory or probability theory to model the inherent uncertainty and imprecision in the pairwise comparison process of analytic hierarchy process (AHP). This study proposes a hybrid approach that integrates fuzzy sets theory and simulation technique into AHP method to better represent the uncertainty in decision-making process. The aim of the proposed approach is to benefit the advantages of both probability theory and fuzzy sets theory which may provide a new paradigm for modeling uncertainty in multi-criteria decision-making (MCDM). An illustrative example is also presented to show the applicability of the proposed approach.