Prioritization Problem Research Articles

Q-Rung orthopair fuzzy 2-tuple linguistic WASPAS algorithm for patients’ prioritization based on prioritized Maclaurin symmetric mean aggregation operators

Due to the fuzziness of the medical field, q-rung orthopair fuzzy 2-tuple linguistic (q-RF2L) set is the privileged way to aid medical professionals in conveying their assessments in the patient prioritization problem. The theme of the present study is to put forward a novel approach centered around the merging of prioritized averaging (PA) and the Maclaurin symmetric mean (MSM) operator within q-RF2L context. According to the prioritization of the professionals and the correlation among the defined criteria, we apply both PA and MSM to assess priority degrees and relationships, respectively. Keeping the pluses of the PA and MSM operators in mind, we introduce two aggregation operators (AOs), namely q-RF2L prioritized Maclaurin symmetric mean and q-RF2L prioritized dual Maclaurin symmetric mean operators. Meanwhile, some essential features and remarks of the proposed AOs are discussed at length. Based on the formulated AOs, we extend the weighted aggregated sum product assessment methodology to cope with q-RF2L decision-making problems. Ultimately, to illustrate the practicality and effectiveness of the stated methodology, a real-world example of patients’ prioritization problem is addressed, and an in-depth analysis with prevailing methods is performed.

Kansei engineering for the intelligent connected vehicle functions: An online and offline data mining approach

The big data era enables automakers to mine users’ affective (Kansei) requirements for the car design. However, existing literature mostly applies text mining with users’ online comments, possibly leading to biased results since users without online comments were not considered. To fill in this gap, this paper proposes to jointly analyse users’ online commenting and offline usage big data, and develops a novel framework to efficiently fuse these two datasets for the Kansei engineering of the intelligent connected vehicle (ICV) functions. A behaviour-enhanced large language model is proposed to process users’ online comments; then, users’ Kansei requirements are further jointly analysed with their offline in-cabin behaviour data, by the proposed NLP-MDCEV (natural language process — multiple discrete-continuous extreme value) model, to understand user’s complex discrete and continuous choice decisions in the smart cockpit. In addition, the proposed framework aims to solve the problem of design tasks prioritization, where not all the Kansei requirements can be met if design resources are limited. The proposed framework is applied in the studied new energy vehicle company, with more than nine-months’ online comments and six-months’ offline usage data, where results suggest its merits of economic, efficient, and effective.

MLBRSA: Multi-Learning-Based Reptile Search Algorithm for Global Optimization and Software Requirement Prioritization Problems.

In the realm of computational problem-solving, the search for efficient algorithms tailored for real-world engineering challenges and software requirement prioritization is relentless. This paper introduces the Multi-Learning-Based Reptile Search Algorithm (MLBRSA), a novel approach that synergistically integrates Q-learning, competitive learning, and adaptive learning techniques. The essence of multi-learning lies in harnessing the strengths of these individual learning paradigms to foster a more robust and versatile search mechanism. Q-learning brings the advantage of reinforcement learning, enabling the algorithm to make informed decisions based on past experiences. On the other hand, competitive learning introduces an element of competition, ensuring that the best solutions are continually evolving and adapting. Lastly, adaptive learning ensures the algorithm remains flexible, adjusting the traditional Reptile Search Algorithm (RSA) parameters. The application of the MLBRSA to numerical benchmarks and a few real-world engineering problems demonstrates its ability to find optimal solutions in complex problem spaces. Furthermore, when applied to the complicated task of software requirement prioritization, MLBRSA showcases its capability to rank requirements effectively, ensuring that critical software functionalities are addressed promptly. Based on the results obtained, the MLBRSA stands as evidence of the potential of multi-learning, offering a promising solution to engineering and software-centric challenges. Its adaptability, competitiveness, and experience-driven approach make it a valuable tool for researchers and practitioners.

The COVID 19 Pandemic as a Moral Test for Society

Abstract The COVID-19 pandemic brings up unprecedented challenges. Healthcare practitioners find themselves in an extraordinary, wartime-like situation and are obliged to apply triage on a daily basis. In this context, routine procedures prove insufficient and the redefinition of ethical practice guidelines becomes a necessity – leading not only to a shift in procedures, but also reshaping the very value of human life. This, in turn, triggers an axiological crisis, which exacerbates the tension between paradigms of sanctity and quality of life and the conflicting principles of egalitarianism and utilitarianism. Triage should not be limited to healthcare practice only: the concept can be extended to a macro-level where policymakers decide whether to prioritize human lives over livelihoods and economic health, impacting healthcare system capacity. The complexity of the challenges arising from the pandemic uncovers the urgent need to seek solutions from an integrated approach. In this paper, we offer a transdisciplinary view: we first define technical aspects of triage from the perspective of a healthcare professional. In the second part, we further analyze the problem of medical prioritization in the light of doctrinal findings related to the value of human life, and we show how and to what extent this philosophical debate is reflected in the emerging ethical guidelines in France, Spain, Italy, Belgium, Germany and Austria. Finally, we broaden the concept of triage, adding the “macro-economic” ingredient in our analysis and we present the results for selected European countries with special emphasis on the case of Poland.

The impact of cognitive resource constraints on goal prioritization

Many decisions we face daily entail deliberation about how to coordinate resources shared between multiple, competing goals. When time permits, people appear to approach these goal prioritization problems by analytically considering all goal-relevant information to arrive at a prioritization decision. However, it is not yet clear if this normative strategy extends to situations characterized by resource constraints such as when deliberation time is scarce or cognitive load is high. We evaluated the questions of how limited deliberation time and cognitive load affect goal prioritization decisions across a series of experiments using a gamified experimental task, which required participants to make a series of interdependent goal prioritization decisions. We fit several candidate models to experimental data to identify decision strategy adaptations at the individual subject-level. Results indicated that participants tended to opt for a simple heuristic strategy when cognitive resources were constrained rather than making a general tradeoff between speed and accuracy (e.g., the type of tradeoff that would be predicted by evidence accumulation models). The most common heuristic strategy involved disproportionately weighing information about goal deadlines compared to other goal-relevant information such as the goal’s difficulty and the goal’s subjective value.

Increase the Accuracy of Detection of Pathogenic Genes of Breast Cancer using a Graph-Based Approach to the Gene Prioritization Problem

Cancer is one of the most common causes of mortality today. This disease's complications impose many costs on the human community's health, care, and well-being sectors. Solving complex biological problems requires advanced computational methods, and bioinformatics was created to solve such complex problems with the active interaction of several fields of science. Bioinformatics is an interdisciplinary science combining biological sciences, computers, mathematics, and statistics. The issue investigated in this research deals with one of the challenging issues in bioinformatics, namely candidate gene prioritization in breast cancer. Gene prioritization means sorting genes based on their relevance to a specific disease, such as breast cancer. Finally, the genes are checked according to their importance in performing costly experiments. The proposed approach in this research is to present a method based on graph mining for prioritizing genes. The study conducted with ENDEAOUR and DIR methods was compared and evaluated. The evaluation results show that the designed method is more efficient than other compared methods.

A bipolar fuzzy [formula omitted]-competition graph based ARAS technique for prioritizing COVID-19 vaccines

A bipolar fuzzy competition graph helps to examine the competition between two vertices when they have a common out-neighbourhood. With this idea, the bipolar fuzzy p-competition graph (BFpCG) can be defined when two vertices have p common out-neighbourhood. In this study, the above-said concepts are defined along with certain characteristics such as low, high, semi-perfect and perfect competitive competition graphs based on the number of components, and certain important results are derived. Moreover, the extensions of BFpCG are discussed by considering various situations and illustrated by a food web problem of 13 species. Besides, an application is taken to find the competition among the COVID-19 vaccines against five mutations: Alpha, Beta, Gamma, Delta and Omicron. Multi-criteria decision-making (MCDM) techniques utilize to access the prioritization problems. The additive ratio assessment (ARAS) is a robust MCDM technique and it simplifies complex decision-making problems through the utility degree. An efficient outcome can be attained by this technique when it works with positive and negative perceptions. Therefore, this study constructs a novel bipolar fuzzy ARAS system based on the BFpCG. To demonstrate the applicability of the proposed system, an application is taken in prioritizing COVID-19 vaccines, where Moderna and CoronaVac resulted in first and last rank, respectively. For this purpose, the efficacy rate of the vaccines against severe disease is considered. Finally, the outcomes are validated through sensitivity and comparative analyses. Then, the managerial implications, future works and limitations of the study are provided to explore the proposed research.

A developed gained and lost dominance score method for risk prioritization in FMEA with Fermatean fuzzy information

Failure mode and effect analysis (FMEA) is one of the most effective means for potential systematic risk assessment in a real work environment. Nevertheless, the traditional FMEA approach has been extensively criticized for many deficiencies in coping with risk evaluation and prioritization problems under inter-uncertain environments. To overcome the limitations, in this paper, a synthesized risk priority calculation framework is proposed for FMEA by combining the gained and lost dominance score (GLDS) method, the combination ordered weighted averaging (C-OWA) operator, and Fermatean fuzzy set (FFS). Firstly, we use FFS to express the experts’ uncertain risk evaluation information which can depict the fuzziness and ambiguity of the information. Secondly, the C-OWA operator combined with FFS is introduced to build the group risk matrix which can provide a more reasonable risk analysis result. Then, the developed GLDS method with FFS is presented to calculate the risk priority of each failure mode which takes both individual and group risk attitudes into consideration. Finally, a medical device risk analysis case is introduced to demonstrate the proposed FMEA framework. We also perform comparison analyses to confirm the effectiveness and rationality of the hybrid risk prioritization framework for FMEA under a complex and uncertain situation.

Prioritization and decision-making: A brief review of methods

Statistical and decision-making techniques for solving prioritization problems are described. These approaches include the analytic hierarchy process (AHP) of the multi-attribute decision-making and its extension to the statistical modeling and testing, scaling techniques of priority estimation, maximum difference models, identification of key drivers in regression, and other methods. The described techniques have been widely applied and proved to be helpful for identification and ordering the most important items in solving various marketing research and decision-making problems.

Detecting biased validation of predictive models in the positive-unlabeled setting: disease gene prioritization case study.

Positive-unlabeled data consists of points with either positive or unknown labels. It is widespread in medical, genetic, and biological settings, creating a high demand for predictive positive-unlabeled models. The performance of such models is usually estimated using validation sets, assumed to be selected completely at random (SCAR) from known positive examples. For certain metrics, this assumption enables unbiased performance estimation when treating positive-unlabeled data as positive/negative. However, the SCAR assumption is often adopted without proper justifications, simply for the sake of convenience. We provide an algorithm that under the weak assumptions of a lower bound on the number of positive examples can test for the violation of the SCAR assumption. Applying it to the problem of gene prioritization for complex genetic traits, we illustrate that the SCAR assumption is often violated there, causing the inflation of performance estimates, which we refer to as validation bias. We estimate the potential impact of validation bias on performance estimation. Our analysis reveals that validation bias is widespread in gene prioritization data and can significantly overestimate the performance of models. This finding elucidates the discrepancy between the reported good performance of models and their limited practical applications. Python code with examples of application of the validation bias detection algorithm is available at github.com/ArtomovLab/ValidationBias.

Smart Technology Prioritization for Sustainable Manufacturing in Emergency Situation by Integrated Spherical Fuzzy Bounded Rationality Decision-Making Approach

The delays and disruptions during the pandemic have awakened interest in the sustainability and resilience of production systems to emergencies. In that context, the deployment of smart technologies has emerged as an almost mandatory development orientation to ensure the stability of manufacturing. The core value of smart technologies is to reduce the dependence on human labor in production systems. Thereby, the negative impacts caused by emergency situations are mitigated. However, the implementation of smart technologies in a specific production system that already exists requires a high degree of suitability. Motivated by this fact, this study proposes an integrated spherical fuzzy bounded rationality decision-making approach, which is composite of the spherical fuzzy decision-making trial and evaluation laboratory (SF DEMATEL) and the spherical fuzzy regret theory-based combined compromise solution (R-SF CoCoSo) method. The proposed approach reflects both the ambiguities and psychological behaviors of decision-makers in prioritization problems. It was applied to prioritize seven smart technologies for manufacturing in Vietnam. The results show that reliability, costs, and maturity are the most important criteria for choosing smart technology which is suitable for an existing production system in Vietnam. Our findings seem to suggest that the automatic inspection, remote machine operation, and robots are the most suitable smart technologies to stabilize and sustain production in Vietnam for emergency situations.

A dynamic failure mode and effects analysis for train systems failures risk assessment using FCM and prospect theory

Improving the reliability of railway train systems and preventing potential failures in the train operation process is one of the most significant tasks. The failure mode and effects analysis (FMEA) is the most effective and widely applied technique for identification, evaluation, and prevention risk of potential failures in diverse fields. Nevertheless, current risk prioritization approaches for FMEA overlook the transfer of decision makers’ risk preferences under different risk states of potential failures. In addition, little attrition has been paid to addressing the risk prioritization problems in FMEA under a dynamic environment. In order to bridge these research gaps, this paper proposes a dynamic prioritization approach for FMEA by integrating the Fuzzy Cognitive Map (FCM) and the prospect theory. First, improved weighted arithmetic averaging (WAA) operator based on the similarity measure is constructed to aggregate each decision maker’s evaluation information. Then, the FCM is applied to obtain the risk matrix and interaction relationships among failures under different risk states. Next, the dynamic prospect theory is built to determine the risk priority of each failure by considering the risk preference of decision makers, in which the dynamic weight functions are derived based on the risk matrix under different risk states. Finally, the proposed dynamic risk prioritization approach for FMEA is tested by the failures risk analysis of the railway train bogie system in the railway train systems. The comparison study is conducted to demonstrate the reliability and rationality of the proposed risk prioritization approach.

Research on optimization strategy of urban medium voltage direct current distribution network construction considering application scenarios

In the distribution network planning work, taking into account the constraints of funds and construction annual plan, there is always the prioritization problem of multiple MVDC distribution networks. Aiming at the problem, this paper proposes a method for the optimization analysis of MVDC distribution network planning objects. Firstly, a comprehensive evaluation index system of urban MVDC distribution network planning and optimization is established from three aspects of improving power supply reliability and the quality of voltage, MVDC distribution network scenario types and distribution network social benefits. Then, the qualitative indexes are quantified based on the cloud model, and the comprehensive evaluation values of each distribution network are calculated and sorted by the hierarchical additive weighting of Delphi entropy weight method. Taking the maximum comprehensive evaluation value of the scheme as objective function and the annual total investment as constraint, the construction priority of multiple MVDC distribution networks is determined. Finally, taking several distribution networks in Guangzhou, Guangdong Province, China as examples, the simulation results show that the optimal objects of distribution network based on the urban scenario demands are X4, X8 and X1, with the lowest total investment cost of 19,180 yuan, thus verifying the effectiveness of the proposed method.

Colosseum: Regression Test Prioritization by Delta Displacement in Test Coverage

The problem of test-case prioritization has been pursued for over three decades now and continues to be one of the active topics in software testing research. In this paper, we focus on a code-coverage based regression test-prioritization solution ( <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math></inline-formula> ) that takes into account the position of changed (delta) code elements (basic-blocks) along the loop-free straight-line execution path of the regression test-cases. We propose a heuristic that logically associates each of these paths with three parameters: (i) the offset (displacement <i>a</i> ) of the first delta from the starting basic-block, (ii) the offset (displacement <i>c</i> ) of the last delta from the terminating basic block, and (iii) the average scattering (displacement <i>b</i> ) within all the intermediate basic-blocks. We hypothesize that a regression test-case path with a shorter overall displacement has a good chance of propagating the affects of the code-changes to the observable outputs in the program. <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math></inline-formula> prioritizes test-cases with smaller overall displacements and executes them early in the regression test-execution cycle. The underlying intuition is that the probability of a test-case revealing a regression fault depends on the probability of the corresponding change propagation. The change in this context can potentially lead to an error. Extending this logic, delta displacement provides an approximation to failed error propagation. Evaluation on 20 open-source C projects from the Software-artifact Infrastructure Repository and GitHub (totaling: 694,512 SLOC, 280 versions, and 69,305 test-cases) against four state-of-the-art prioritizations reveals that: <inline-formula><tex-math notation="LaTeX">${{\sf Colosseum}}$</tex-math></inline-formula> outperforms the competitors with an overall 84.61% success in terms of 13 prioritization effectiveness metrics, majority of which prefer to execute top- <inline-formula><tex-math notation="LaTeX">$k\%$</tex-math></inline-formula> prioritized test-cases.

Digital transformation project selection using Interval Valued Type-2 Intuitionistic Fuzzy TOPSIS

Digital transformation necessitates fundamental changes in business processes, business models, and even cultures of the companies. DT projects provide a substantial value to the business competition and effect their market shares. In order to reach these results, digital transformation projects should be carefully analyzed and evaluated. In this paper, we focus on digital transformation project prioritization problem under multiple criteria. Just like many other technology related decisions, digital transformation problems are inherently uncertain, which leads researchers to employ the fuzzy set theory. Interval Valued Type-2 Intuitionistic Fuzzy Sets (IVT2IFS) are a relatively new extension of fuzzy sets which take into account membership and non-membership values as an interval. In this paper, we utilize Interval Valued Type-2 Intuitionistic Fuzzy TOPSIS method for a digital transformation project prioritization problem and apply the model to a real-world example.

Prioritization of model smell refactoring using a covariance matrix-based adaptive evolution algorithm

The refactoring process enhances the software design by modifying the structure of design parts impaired with model smells without altering the overall software behavior. However, handling these smells without proper prioritization will not produce the anticipated effects. In this paper, we solve the prioritization of the model smell refactoring using a multi-objective optimization (MOO) algorithm called the multi-objective (MO) covariance matrix adaptation evolution strategy (MO CMA-ES). Our formulation relies on the refactoring of unified modeling language (UML) class diagrams to mitigate the negative effect of design smells. We treat the prioritization problem as a real-valued MOO where we propose novel data encoding procedures. We use two conflicting objectives, quality, and maintainability , to balance the refactoring. We first build a new solution representation that guarantees smell fixing and eliminates the rejection limitation. Furthermore, we suggest a custom mapping scheme to properly encode real-valued quantities using unique representations. For performance evaluation purposes, we developed a large custom dataset with more than 30,000 class records, using seven popular open-source software projects. A novel relative coverage metric is proposed to mitigate the limitations associated with the standard coverage. For benchmarking purposes, we also consider an improved version of the nondominated sorting genetic algorithm (NSGA-II(. The reported performance scores confirm the superiority of the MO CMA-ES algorithm over NSGA-II. The former successfully identified the refactoring sequences that lead to the best improvements in software quality and maintainability while it is able to fix all identified design smells. These improvements are quantified in terms of hypervolume, coverage, spacing metrics, and execution time. The MO CMA-ES attained the highest average maximum quality score of 1149 while keeping the average maintainability at the lowest score of 13.8. In all experiment settings, the proposed solution leads to longer refactoring sequences at no additional computational cost.

A New Interactive Decision Approach with Probabilistic Linguistic Data: An Application in the Academic Sector

As an innovative generalization to a linguistic term set, the probabilistic variant is gaining abundant attraction in the decision process. However, earlier studies with this variant for decision-making have not adequately explored hesitation in data articulation and interactive ranking. Driven by the claim, in this paper, a new integrated approach is put forward under the probabilistic linguistic context, which attempts to address the claims by presenting a regret/rejoice technique and an interactive WASPAS algorithm for determining the significance of factors and personalized ranking of alternatives. To test the usefulness of the approach, the online course prioritization problem based on empirical data is exemplified, and a comparison demonstrates the benefits and limitations of the proposed work.

Offshore wind power station (OWPS) site selection using a two-stage MCDM-based spherical fuzzy set approach

In response to challenges from the COVID-19 pandemic and climate change to achieve the goal of ensuring sustainable economic growth, offshore wind power development not only provides a clean and sustainable source of energy but also provides opportunities for economic growth and job creation. Offshore wind energy projects have been promptly suggested in Vietnam due to policy advancement, with the country's excellent wind resources. The success of an offshore wind energy project is decided mainly by choosing the best location for offshore wind power station (OWPS) construction, which is a complex multicriteria decision-making (MCDM) problem with the coexistence of conflicting factors. There is a problem with incomplete decision information use and information loss during the decision-making process, and it is easy to overlook the interaction difficulty in a fuzzy environment. To address the complex nature of the prioritization problem posed, this study proposes a hybrid MCDM framework combining the spherical fuzzy analytical hierarchy process (SF-AHP) and weighted aggregated sum product assessment (WASPAS). SF-AHP is used in the first stage to determine the significance levels of OWPS evaluation criteria. WASPAS is then utilized to rank locations of OWPS. A comprehensive set of evaluation criteria developed based on the concept of sustainable development has been recognized by reviewing the literature review and interviewing experts to practice the two-stage MCDM model. A real case study for Vietnam is conducted to test the effectiveness of the proposed method. The best location schemes have been determined by using the decision framework. The results of the sensitivity analysis and a comparison analysis demonstrate that the decision framework is practical and robust. The proposed methodology can be used to attain a decision-making process at the regional level for offshore wind farm planning and coastal development, and the study results encourage the establishment of renewable energy development policies.

Bayes optimal informer sets for early-stage drug discovery.

An important experimental design problem in early-stage drug discovery is how to prioritize available compounds for testing when very little is known about the target protein. Informer-based ranking (IBR) methods address the prioritization problem when the compounds have provided bioactivity data on other potentially relevant targets. An IBR method selects an informer set of compounds, and then prioritizes the remaining compounds on the basis of new bioactivity experiments performed with the informer set on the target. We formalize the problem as a two-stage decision problem and introduce the Bayes Optimal Informer SEt (BOISE) method for its solution. BOISE leverages a flexible model of the initial bioactivity data, a relevant loss function, and effective computational schemes to resolve the two-step design problem. We evaluate BOISE and compare it to other IBR strategies in two retrospective studies, one on protein-kinase inhibition and the other on anticancer drug sensitivity. In both empirical settings BOISE exhibits better predictive performance than available methods. It also behaves well with missing data, where methods that use matrix completion show worse predictive performance.

Online Maintenance Prioritization Via Monte Carlo Tree Search and Case-Based Reasoning

AbstractWhen maintenance resources in a manufacturing system are limited, a challenge arises in determining how to allocate these resources among multiple competing maintenance jobs. This work formulates an online prioritization problem to tackle this challenge using a Markov decision process (MDP) to model the system behavior and Monte Carlo tree search (MCTS) to seek optimal maintenance actions in various states of the system. Further, case-based reasoning (CBR) is adopted to retain and reuse search experience gathered from MCTS to reduce the computational effort needed over time and to improve decision-making efficiency. The proposed method results in increased system throughput when compared to existing methods of maintenance prioritization while also reducing the computation time needed to identify optimal maintenance actions as more information is gathered. This is especially beneficial in manufacturing settings where maintenance decisions must be made quickly to minimize the negative performance impact of machine downtime.