Hierarchical Decision Model Research Articles

A Hierarchical Decision Model Proposal for Maintenance Strategy Selection in the Hydroelectric Power Plants

Günümüzde enerji talebi her geçen gün artmaktadır. Enerji arzını sağlayan tesisler artan bu talebe kesintisiz bir şekilde cevap verecek politikalar ve uygulamalar geliştirmektedirler. Enerjide sürekliliği sağlamakta etkili olan unsurlardan biri de enerjinin üretildiği santralların bakım stratejilerini arzı kesintiye uğratmayacak şekilde belirlemektir. Bu çalışmada, bir hidroelektrik santralda en kritik elektriksel ekipmanın bulunması ve bu ekipman için bakım stratejisinin seçimi amaçlanmıştır. Bu çalışma iki aşamadan oluşmaktadır. İlk olarak en kritik elektriksel ekipman belirlenmiştir. Ekipmanların sıralanmasında kullanılan kriterlerin ağırlıklarının belirlenmesinde Pisagor Bulanık Analitik Hiyerarşi Prosesi (PBAHP) kullanılmıştır. Alternatiflerin sıralanmasında ise, PBAHP yöntemi ile TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) yöntemi entegre edilmiştir. En kritik elektriksel ekipman olarak belirlenen generatör ekipmanı için en uygun bakım stratejisi revizyon bakım olarak belirlenmiştir.

An evaluation of the effectiveness of innovation ecosystems in facilitating the adoption of sustainable entrepreneurship

ABSTRACT As the digital revolution accelerates and expands in the business world, governments must transform the workforce experience to drive the economy and align it with the changes of the future. In this paper, an innovative and advanced methodological approach for assessing the composite relational dynamics supporting innovative and sustainable entrepreneurial ecosystems is investigated. This research uses the hierarchical decision model (HDM) to construct a generalized ecosystem assessment framework for entrepreneurship and innovation ecosystem strategies to increase the adoption of innovation in sustainable entrepreneurship. The research focuses on the policies and strategies generating all kinds of innovations and propose a model with a comprehensive set of measures to help policy and strategy development.

Monkey plays Pac-Man with compositional strategies and hierarchical decision-making.

Humans can often handle daunting tasks with ease by developing a set of strategies to reduce decision-making into simpler problems. The ability to use heuristic strategies demands an advanced level of intelligence and has not been demonstrated in animals. Here, we trained macaque monkeys to play the classic video game Pac-Man. The monkeys' decision-making may be described with a strategy-based hierarchical decision-making model with over 90% accuracy. The model reveals that the monkeys adopted the take-the-best heuristic by using one dominating strategy for their decision-making at a time and formed compound strategies by assembling the basis strategies to handle particular game situations. With the model, the computationally complex but fully quantifiable Pac-Man behavior paradigm provides a new approach to understanding animals' advanced cognition.

Multistrategy boosted multicolony whale virtual parallel optimization approaches

A multistrategy boosted multicolony whale optimization algorithm (MSMCWOA) is proposed. First, humpback whales are divided into different subcolonies based on various behavioral mechanisms of whale predation. Each subcolony evolves independently to ensure the diversity of the algorithm population. At the same time, local knowledge or experience generated from each subcolony is periodically provided to the global storage space. When the diversity of colonies decreases sharply, a hierarchical decision-making model based on information sharing is stimulated. Global storage space applies optimal information to guide subcolonies to jump out of potential local optimization. In addition, individuals in different subcolonies choose different strategies to communicate with each other with certain probability. The ability of knowledge acquisition and dissemination and the distance between individuals are considered to improve the effectiveness of information exchange and space search. Twenty-three unimodal and multimodal benchmark functions are tested and compared with state-of-the-art algorithms, and the experimental results show that the MSMCWOA has a better performance in terms of convergence rate and stability. The MSMCWOA is also successfully applied to the optimization design of the pressure vessel and tension/compression spring, and the result shows the superior performance of the MSMCWOA in solving project optimization problems.

VDFEFuse: A novel fusion approach to infrared and visible images

To address the problems of energy loss and edge blur when fusing infrared and visible images with traditional methods, a novel infrared and visible image fusion approach based on the visual differentiation feature extraction is proposed, termed as VDFEFuse. Owing to the unique thermal radiation imaging characteristic of the infrared sensor, more intensity information is preserved in the infrared images. In this approach, we construct a visual differentiation feature extraction operator to compensate for the loss of such significant features in the fusion process to the greatest extent. Then, through the LatLRR-based multilevel decomposition and the specific fusion strategies, a set of fused sub-images is generated. To select the best fusion result from fused sub-images, a novel hierarchical structure decision model is formulated in which the qualitative and quantitative evaluations of all fused sub-images can be taken into account simultaneously. Compared with nine image fusion methods, experiments showed that not only does the proposed algorithm have better visual effects from subjective judgment and objective evaluation, but it also can better retain the rich brightness information in the source image.

Proposed Framework for the Assessment of Business Intelligence Platforms for Medium-to-Large Hospitals Using Hierarchical Decision Modeling and Expert Judgments

This research focuses on the assessment of business intelligence platforms for medium-to-large hospitals using hierarchical decision modeling methodology (HDM) and expert judgment. The healthcare data come from different source systems, like EMRs or HR software, to different departments, like radiology or pharmacy. The data comes from all over the organization. Healthcare data is complex and complicated, i.e., the data is structured and unstructured and has various formats like text, numeric, images, multimedia, paper, etc. Even before the pandemic, the healthcare data was growing. However, healthcare data has grown exponentially during the pandemic, resulting in a growing need for storage, retrieval, and analysis challenges using the traditional approaches. In addition, there is a growing demand for clinical and administrative information from hospital executives and services providers. The use of business intelligence (BI) is seen as a possible solution to this challenge. BI helps direct, high-level decision-making by evaluating practice performance and predicting patient outcome trends. BI application/use has the potential to reduce costs and build healthier revenue streams, and at the same time, deliver the best patient care. BI can assist decision-makers from clinic to billing, HR, and everything in between. The objective of the current research paper is to help healthcare executives in the assessment of BI alternatives by considering multiple criteria and multiple stakeholders. The research applied a multi-criteria approach, considered People, Business Processes, and Technology perspectives, and used Hierarchical Decision Modeling (HDM) methodology to evaluate solution alternatives. HDM methodology requires the expert judgment of experts to provide the relative ranking of the perspectives and criteria. There are four alternative solutions suggested. Three solutions are cloud-based, and one is legacy, aka traditional in-house solution. Cloud and legacy systems will co-exist for the long run.

Technology management maturity assessment model in healthcare research centers

In the context of continuous learning in healthcare organizations, a mature system can be defined as a system that generates timely actions to the information that it derives from internal and external data to create meaningful measurement regarding system learning and increased efficacy and effectiveness in health outcomes. However, there is a lack of a model that provides managers and decision-makers with a systematic, multi-criteria, validated, quantifiable, and repeatable maturity model to assess and enhance health organizations' performance in continuous learning and technology management. This research proposes a multi-criteria model to assess technology management maturity and continuous learning in research centers within university hospitals by using Hierarchical Decision Model (HDM), validated and quantified by panels of healthcare subject matter experts. The model can help research centers with pinpointing their strengths and opportunities in terms of continuous learning from the data they have access to while giving them organizational self-awareness and guide them in setting their strategies and resource allocation. The model will serve as a much-needed technology management tool for healthcare organizations to assess their technology management maturity and continuous learning efforts and assist them in creating more effective roadmaps.

Risk investigation in circular economy: a hierarchical decision model approach

ABSTRACT Integrating circular economy practices into the conventional supply chain is a challenging task due to the sheer number of risks and uncertainties involved. These risks can hamper the functioning of recovery processes and act as impediments to the adoption of circular systems. Furthermore, there is a scarcity of studies focusing on the risk aspects in the context of the circular economy. This study attempts to address this gap in the literature by identifying the risks associated with circular economy operations and proposing a taxonomy for the same. Furthermore, a risk hierarchical decision model is developed to determine the relationships among the risks and differentiate between driving and dependent risks. A two-phase approach, kappa statistical analysis and interpretive structural modelling, is used to validate the proposed taxonomy and understand the risk interactions. The study statistically categorises the circular economy risks into different categories and scientifically evaluates the interdependencies among the risks. Unlike conventional perception, majority of the high-priority risks are organisational rather than financial in nature.

Hierarchical sequential three-way decision model

Knowledge acquisition is one of the important issues in granular computing. In recent years, scholars have paid much attention to this problem and proposed the rule-based acquisition algorithms. However, a large number of the decision rules mined by the existing algorithms are not comprehensible. At the same time, the long detailed rules are easy to lead to over-fitting. In order to generate simpler and easier-to-comprehensible rules and improve human decision-making, a hierarchical sequential three-way decision model is proposed by combining sequential three-way decisions with hierarchical rough set model. Specifically, we generalize the concepts of the conditional attributes through the concept hierarchy tree, design the multi-hierarchical decision table with multiple levels of granularity, and illustrate the corresponding algorithm to acquire the generalized rules step by step. The experimental results demonstrate that the proposed model can mine hierarchical sequential three-way decision rules under different levels of granularity.

A Scoring Model to Evaluate Offshore Oil Projects

Offshore oil projects are defined as mega projects that are associated with larger project size, higher complexities with inherent risks, and higher chances of failure in the investment and decision-making processes. These characteristics require significant assessment and acquisition methods in order to reduce failure. Financial analysis is the most widely used method to evaluate offshore oil projects; however, there are several other internal and external factors that affect the overall success of offshore oil projects. A comprehensive approach and advanced tools are required to evaluate factors that can have a significant impact before any investment decisions are made. This paper evaluates the important factors that influence offshore oil project investment decisions as well as score their weight of impact using a multi criteria decision model. A comprehensive literature review was conducted to recognize the complexities of these factors. Four perspectives were identified in order to assess the attributes that are necessary for successful offshore projects. These perspectives are economical, technical, political and environmental and safety. A hierarchical decision model (HDM), along with desirability curves, was implemented to evaluate offshore oil projects in order to improve investment decisions and reduce failure.

Health technology diffusion: Case of remote patient monitoring (RPM) for the care of senior population

Increase in population is causing further increase in senior population which is the major cost factor in health care. Health information technology has been cited as one of the tools to help reduce this cost for this group of patients. Therefore, our study explores strategies and policies helping reduce the barriers for the adoption of a health information technology – teleconsultation – for senior population. The objective of this research is to develop a model to evaluate alternative solutions to increase the adoption of Remote Patient Monitoring (RPM) for the senior population. The research leverages hierarchical decision modeling (HDM) to build a hierarchical multi criteria assessment model. The model elements are derived through literature and validated by an expert panel.

Факторы оценки альтернатив при проектировании и обосновании целесообразности строительства гидротехнических сооружений

Introduction. Reservoirs are important elements of the water resources regulation system; they make a substantial contribution to regional economies. A multi-criterial approach to selection of the best locations for dams is proposed in the absence of exhaustive information on the quantitative assessment of individual criteria. Morphometric characteristics of retaining reservoirs are among the most important tools for analyzing water resources; they represent a quantitative study designated for the mathematical simulation of the most important characteristics of a reservoir and their linking to the indices and parameters that allow to find out the hydrological conditions. The purpose of the study is to select and rank the factors needed to determine the priority locations for the construction of hydraulic structures. Materials and methods. A critical analysis of the research works is carried out; the hydrological, geological, topographical aspects and land use factors are reasonably selected, which allow to propose a hierarchical decision-making model for prioritizing areas where hydraulic structures can be designed. Results. AHP-TOPSIS, a hybrid method of the multicriterial analysis, was used. A reasonable choice of parameters and other factors is necessary to make decisions needed to determine the best area that will accommodate construction works, since it will reduce the amount of pre-project work in a particular region. The reservoir, whose parameters and coefficients are studied, will be thoroughly explored with a focus on its hydrology, topography, geology, and terrain use. The municipality of Manicaragua, belonging to the province of Villa Clara, Cuba, is considered as a case of the practical application of this approach. Based on the information provided by the geographic information systems and the hybrid method of the multi-criterial analysis, a pairwise comparison of alternatives for each of the established parameters was performed, the weights of alternatives were obtained, and the order of prioritization of the areas that can accommodate hydraulic structures was established. Conclusions. The analysis of the priority areas, made on the basis of the selected parameters used as the criteria and alternatives, allowed us (1) to analyze 12,591 districts of the municipality of Manicaragua as the potential locations of hydraulic structures, and (2) to reduce their number to 23 %.

Evaluation of education scenarios for acquiring digital competences of secondary school students in Slovenia

Digital competences are among the most important competences in modern society, with the education system playing a key role in the acquisition of digital competences by youths. As a rule, digital competences of students in general secondary schools in the EU are not optimal and vary considerably. As EU states are autonomous regarding secondary schools, their education organisation varies. Therefore, attempts are being made to implement certain standards of learning outcomes to be attained by secondary school students. Such standards are being prepared, and systematic implementation of the acquisition of digital competences in schools will begin as soon as there are enough trained teachers, for who in 2017 in the framework of DigCompEdu, a European framework of digital competences of educators was developed, with concrete advice for their work in the education process. We have prepared several education scenarios that can be used to enhance digital competences of students in general secondary schools where there are no professional subjects in which the students could acquire digital competences. Since the education scenarios are described by a large number of parameters, we prepared a hierarchical multi-attribute decision model for their evaluation and grading. Experts from the ministry, from professional services and secondary schools, cooperated in the preparation and validation of the model. The model is an aid to decision-makers who are looking for best scenarios, and also allows schools to evaluate their status and recognise what has to be done to attain a certain level of digital competences of students.

Technology Transfer Evaluation: Driving Organizational Changes Through a Hierarchical Scoring Model

Innovation is seen today as an indispensable practice if an organization wishes to achieve competitive advantage and sustainable success in the marketplace. The development of new products and services and new technologies that enable those products and services seem to be the crown jewel of innovation. The management of these technologies, more specifically the process through which technologies are transferred, has been the focus of scholarly debates and studies for several decades. Currently, there are no quantitative, comprehensive, and easily applicable models in the literature that researchers and practitioners can use to assess organizations’ technology transfer capabilities. This article has sought to delineate important factors and perspectives applicable to technology transfer, and to use those in order to assess an organization's capabilities, through the application of a hierarchical decision modeling model to create a technology transfer score. The research model is applied to a utility company in the Pacific Northwest of the United States, and the results provide insights on how to measure and interpret technology transfer capabilities as well as a starting point for technology managers to improve those capabilities.

Ranking the Key Areas for Autonomous Proving Ground Development Using Pareto Analytic Hierarchy Process

Autonomous or highly automated road vehicles and all related technologies are under intensive research and development. Moreover, internationally a massive investment increase can be observed in the automotive industry. According to this megatrend, new automotive test tracks appear or older ones transform to be capable of testing and proving for autonomous vehicles. Therefore, the question emerges: what are the key areas for automated drive development, which must be financed in case of autonomous proving ground design? It is a real challenge to be able to make the right decisions due to a lack of numerous experiences in this field. In this research, experts of automated driving technology have been surveyed and their opinion and knowledge have been synthesized. As a strong purpose of gaining robust results, the conventional AHP has been amended by the Pareto approach to ensure that the derived weights correspond to the expert scoring intention so perfectly that it cannot be more improved. Since the non-Pareto optimal weight results might cause rank reversal in the final prioritization, the applied Pareto test guarantees that the final outcome reflects the expert evaluators’ incentive. The conducted analysis has indicated that the obtained results are robust not only from the sensitivity point of view but also from the Pareto optimality approach. The proposed hierarchical decision model is therefore applicable to assist decision making for autonomous proving ground developments. The main contribution of the article, however, is to present the first reliable prioritization of the autonomous proving ground elements to extend the body of professional knowledge.

Project Assessment Tools Evaluation and Selection Using the Hierarchical Decision Modeling: Case of State Departments of Transportation in the United States

AbstractA considerable amount of research has been conducted on the performance of project delivery methods and applications in project development within the transportation industry. However, deci...

Impact of TV dramas on consumers' travel, shopping and purchase intentions

AbstractCountries are increasingly competing with each other to attract tourists. However, little is known about how consumers' tourism‐related behaviors respond to country image endeavors, such as TV dramas. We propose that as an important image source TV dramas from a country contribute to crafting the country's brand image and thereby influence viewers' tourism related intentions. Considering the case of Turkish TV dramas and drawing on the Hierarchical Decision Model (HDM), a survey of 400 Arab viewers revealed that exposure to Turkish TV dramas results in increased intentions to visit and shop in Turkey as well as to purchase products made in Turkey. Furthermore, while increase in purchase intentions results primarily from enhanced experiential associations related to the country, increase in visit or shopping intentions results from improved status associations related to the country.

Water-energy nexus under uncertainty: Development of a hierarchical decision-making model

In this study, an interval stochastic bi-level programming (ISBP) method is developed for planning the water-energy nexus (WEN) system. In ISBP, interval-parameter programming (IPP) and two-stage stochastic programming (TSP) are introduced into a bi-level programming (BP) framework. ISBP can tackle dual uncertainties expressed as probability distributions and interval values as well as balance the tradeoff between two-level decision makers. An ISBP-WEN model is then formulated for planning the WEN system of Jinan (the capital of Shandong province, China), in which the upper-level model aims to maximize system benefit and the lower-level model aims to minimize the electricity consumed for water allocation. Major findings are: (i) dual uncertainties can affect the city’s water-allocation pattern and lead to different amounts of water shortage and electricity consumption; (ii) [25.5, 31.3]% of water-allocation target cannot be satisfied under the low water-flow level, implying that the city would face serious water shortage; (iii) compared with surface water and reclaimed water, groundwater is the scarcest source for the city and the proportion of groundwater deficit would be [41.2, 49.9]% under the low water-flow level; (iv) compared with the traditional single-level model, electricity consumption from ISBP-WEN would be reduced by [3.9, 5.1]%. These findings can help managers to identify the desired strategy between different stakeholders and provide numerical information for planning the WEN system under dual uncertainties.

Non-cooperative game-based multilateral contract transactions in power-heating integrated systems

The distributed trading mechanism has gained substantial popularity in protecting the privacy of the agents and reducing the computation burden. The existing studies focus on the distributed transaction mechanism applied in the power market, but the distributed multilateral transaction in the integrated energy system has not been explored. This paper proposes a novel power-heating multilateral contract transaction mechanism considering spot market prices. A multi-leader multi-follower (MLMF) Stackelberg game model is formulated to describe the multilateral contract transactions between the integrated energy service providers (IESPs) and the load aggregators (LAs). In this model, the IESPs play the role of leaders to select discriminate prices for different LAs while the LAs play the role of followers to determine the energy purchases. The competition among IESPs is formulated as a non-cooperative Nash game, where each stakeholder seeks to maximize its own profit. Then, the existence of the Nash equilibrium is proved. Besides, a distributed algorithm is developed to derive the equilibrium of the proposed hierarchical decision model, through which the optimal strategies for each player can be obtained. Two case studies with different scales demonstrate the effectiveness of the proposed contract transaction mechanism and the distributed solution algorithm.

The use of readiness assessment for big data projects

Big data projects, including smart-city-related big data projects, are facing an alarmingly high percentage of failure. The reasons behind this phenomenon and the lessons learned from it are well researched. However, big data projects are still failing, as there is a lack of effective models to leverage the lessons learned from previous projects to evaluate an organization’s readiness for a big data project.In this paper, the authors introduce a novel readiness assessment model. This model leverages the lessons learned from previous projects and the experience of experts to be better prepared for an upcoming smart-city-related big data project. Cities can use the model to evaluate their readiness for this type of project in a structured and comprehensive way that will allow for higher chances of conducting a successful big data project.To develop the model, hierarchical decision modeling (HDM) and expert judgment quantification were used to provide the categorization and relative ranking of factors that influence smart-city-related big data projects. HDM is an effective way to understand the relationship between multiple factors and allows for expert panels to prioritize those factors. Moreover, desirability functions were used to extend the understanding of the factors’ dynamics and what needs to be done to better prepare for the challenges associated with each factor. Finally, the model was tested by applying it to several smart-city-related big data projects to show its value.This research highlights the importance of readiness assessment for conducting big data projects and provides a readiness assessment model that cities can use to prepare for an upcoming big data project.