Decision Problem Research Articles

Controlling mission hazards through integrated abort and spare support optimization

AbstractThis study explores the risk management challenges associated with safety‐critical systems required to execute specific missions. The working component experiences degradation governed by a continuous‐time discrete‐state Markov chain, whose failure leads to an immediate system breakdown and safety losses. To enhance system survivability, a limited number of identical spares are available for online replacement throughout the mission. At the same time, the mission abort action arises promptly upon encountering excessive safety hazards. To strike an optimal balance between mission completion and system survivability, we delve into the adaptive scheduling of component replacements and mission termination decisions. The joint decision problem of interest constitutes a finite‐time Markov decision process with resource limitation, under which we analyze a series of structural properties related to spare availability and component conditions. In particular, we establish structured control‐limit policies for both spare replacement and mission termination decisions. For comparison purposes, we evaluate the performance of various heuristic policies analytically. Numerical experiments conducted on the driver system of radar equipment validate the superior model performance in enhancing operational performance while simultaneously mitigating hazard risks.

Construction of New Similarity Measures for Complex Pythagorean Fuzzy Sets and Their Applications in Decision-Making Problems

Construction of New Similarity Measures for Complex Pythagorean Fuzzy Sets and Their Applications in Decision-Making Problems

Analysis of computer communication networks based on evaluation of domination and double domination for interval-valued T-spherical fuzzy graphs and their applications in decision-making problems

Analysis of computer communication networks based on evaluation of domination and double domination for interval-valued T-spherical fuzzy graphs and their applications in decision-making problems

Socioeconomic and Environmental Impact of the Implementation of Renewable Energy: An Analysis from the Neutrosophic PEST-SWOT

The Mantaro Valley in Peru is an inter-Andean River valley, through which the Mantaro River passes. Approximately a population of one million inhabitants live here. Currently, the damage caused by the use of non-renewable energy sources is very evident, both to the environment and to the local economy, which will become unsustainable in the future. That is why we want to critically study the implementation of renewable energy projects that support the generation of electricity and other types of energy in this area. However, this has some positive and negative elements. In this paper, we apply a PEST-SWOT analysis to evaluate the balance of each of these aspects. Furthermore, we use an evaluation in the form of single-valued neutrosophic numbers, which allow us to capture the uncertainty and indeterminacy in this decision-making problem.

An Integrated DEMATEL with Bipolar neutrsophic Dombi-based Heronian Mean Operator and Its Applications in Decision-making Problem

The Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is commonly used in examining and illustrating the relationship between different factors in a complex system. This paper proposes a novel approach that integrates the Bipolar neutrosophic Dombi-based IGWHM operator into the DEMATEL method, in which the criteria are analyzed by means of the cause-and-effect relationship diagram. The current studies on the classical DEMATEL approach have some limitations on the aggregation process, particularly in capturing the interrelationship of individual arguments by assessing their impact on each other within a complex system. To enhance the aggregation of complex information in the decision-making framework, the Bipolar neutrosophic Dombi-based Improved Generalized Weighted Heronian mean (IGWHM) operators are employed. The applicability and effectiveness of the proposed approach are demonstrated when solving a selection of transport service providers. The ability of the method to highlight the intricate interdependencies and ranking criteria based on their importance. The sensitivity of the developed approach is observed with variations in the involved parameter. Moreover, a comparative analysis is made with other methods to demonstrate its validity.

Continuous Pythagorean Fuzzy Set Extension with Multi-Attribute Decision Making Applications

One of the most recent innovations in the field of fuzzy sets has been continuous intuitionistic fuzzy sets (CINFUSs), where membership and non-membership degrees are defined by nonlinear functions, as a direct extension of intuitionistic fuzzy sets (IFSs). The membership and non-membership degrees of CINFUSs can account for uncertainty at every point since they are represented by continuous structures that change based on how the decision-maker responds to uncertainty. On the other hand, Pythagorean fuzzy sets (PFSs) allow for a more accurate representation of the data and a better way to handle uncertainty in decision issues by reflecting the hesitations of decision-makers over a larger range. Due to these superior advantages of CINFUSs and the fact that PFSs are more comprehensive than IFSs, in this study, continuous Pythagorean fuzzy sets (CPFUSs) have been aimed at introducing to define uncertainty more broadly and accurately by representing PFSs with a continuous structure as in IFSs. In this study, firstly, the basic principles and mathematical operators of CPFUSs have been developed and presented. Then, multi-attribute decision-making (MADM) models have been developed by considering different aggregation operators to indicate the feasibility and effectiveness of the continuous Pythagorean fuzzy (CPFU) extension. The developed CPFU-MADM models have been implemented to the solution of two different decision problems: green supplier selection and waste disposal site selection problems. In addition, sensitivity analyses have been conducted on criterion weights, expert weights and parameters in order to demonstrate the reliability and stability of the developed models. Furthermore, the validity and superiority of the developed models have been indicated by the comparative analysis conducted with IFSs and PFSs-based MADM models in the literature. MADM applications have shown that continuous Pythagorean fuzzy sets can successfully represent the expert decisions with different attitudes within the same model. It has been observed that the rankings of alternatives according to aggregation operators do not change even when there are differences in the score values of the alternatives.

An Implementation of the Entropy Method for Determining Weighing Coefficients in a Multicriteria Optimization of Public Procurements

Determining the weights of criteria is a key problem in multicriteria analysis models. The problem of choosing an appropriate method of determining criteria weights in problems of multicriteria decision-making (MCDM) is a very important stage. Most authors suggest classifying the models of determining the weights of criteria into subjective and objective models. This paper presents the application of the Entropy method for determining criteria weights. The Entropy method is tested in the MCDM problem in public procurements. The results show that the Entropy method shouldn't be used as a correction method because in most cases, the decision maker Also shows that applying the Entropy method in MCDM problems for determining weighing coefficients can be counterproductive.

Pythagorean Cubic Normal Fuzzy Information Aggregation Operators and Their Application in Disability Evaluation

Normal fuzzy sets and Pythagorean cubic fuzzy sets are the best means to deal with fuzziness. Combining both of these structures in our current work, we establish the idea of Pythagorean cubic normal fuzzy set. We define some basic operational laws for Pythagorean cubic normal fuzzy set and introduce a number of aggregation operators, including Pythagorean cubic normal fuzzy weighted averaging operator, Pythagorean cubic normal fuzzy weighted geometric operator, Pythagorean cubic normal fuzzy order weighted averaging operator and Pythagorean cubic normal fuzzy order weighted geometric operator. We examine several favorable properties, including monotonicity, boundedness, and idempotency for the proposed operators. We develop an algorithm for the solution of multicriteria decision-making problems. Moreover, we propose an extended form of the TODIM (Portuguese acronym for Interactive Multi-Criteria Decision Making) method. We present a multicriteria decision-making example related to assessing the educational needs of students with disabilities. The techniques and operators defined in the current work provide greater generality and accuracy and give precise results. Ultimately, a detailed illustration is provided to show the closure process of these specified procedures and functions, demonstrating their credibility and efficacy.

Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems with Markov Risk Measures

Robustness to Modeling Errors in Risk-Sensitive Markov Decision Problems with Markov Risk Measures

The meta-aggregation approaches: Correctly choosing how to choose correctly in groups

Aggregating the choices regarding a given decision problem made by multiple individuals into a single solution is essential for exploiting the collective’s intelligence and effective crowdsourcing. There are various aggregation techniques, some of which come down to a simple aggregation rule, such as the majority rule. We propose two one-shot machine-learning-based aggregation approaches, which also exploit the existence of meta-cognitive features such as confidence. The first predicts which aggregation method will be best for a given collective decision-making case. The second directly predicts which decision is optimal given the selection made by each method. We offer a meta-cognitive feature-engineering approach for characterizing a collective decision-making case in a context-sensitive fashion. Experimental results show that using either of our proposed approaches increases the percentage of successfully aggregated cases, compared to the uniform application of the best rule-based aggregation method, by 5.8% and 6%. Furthermore, when an aggregation method is selected by our classifier, its effectiveness increases by an absolute percentage of 6.5% to 32.6%. We also offer a new aggregation method, the Devil’s-Advocate aggregator, to deal with cases in which standard aggregation methods fail. Ablation experiments demonstrate that removing the Devil’s Advocate aggregator decreases the success rate by 5.2%.

The Copeland ratio ranking method for abstract decision problems

The Copeland ratio ranking method for abstract decision problems

CVaR-Constrained Policy Optimization for Safe Reinforcement Learning.

Current constrained reinforcement learning (RL) methods guarantee constraint satisfaction only in expectation, which is inadequate for safety-critical decision problems. Since a constraint satisfied in expectation remains a high probability of exceeding the cost threshold, solving constrained RL problems with high probabilities of satisfaction is critical for RL safety. In this work, we consider the safety criterion as a constraint on the conditional value-at-risk (CVaR) of cumulative costs, and propose the CVaR-constrained policy optimization algorithm (CVaR-CPO) to maximize the expected return while ensuring agents pay attention to the upper tail of constraint costs. According to the bound on the CVaR-related performance between two policies, we first reformulate the CVaR-constrained problem in augmented state space using the state extension procedure and the trust-region method. CVaR-CPO then derives the optimal update policy by applying the Lagrangian method to the constrained optimization problem. In addition, CVaR-CPO utilizes the distribution of constraint costs to provide an efficient quantile-based estimation of the CVaR-related value function. We conduct experiments on constrained control tasks to show that the proposed method can produce behaviors that satisfy safety constraints, and achieve comparable performance to most safe RL (SRL) methods.

On the decision problem for quantified probability logics

Let $\mathsf{QPL}^{\mathrm{e}}$ expand the quantifier-free ‘polynomial’ probability logic of [4] by adding quantifiers over arbitrary events; it can be viewed as a one-sorted elementary language for reasoning about probability spaces. We prove that the $\Sigma_2$-fragment of the $\mathsf{QPL}^{\mathrm{e}}$-theory of finite spaces is hereditarily undecidable. By earlier observations, this implies that $\Pi_2$ is the maximal decidable prefix fragment of $\mathsf{QPL}^{\mathrm{e}}$. Moreover, we obtain similar results for two natural one-sorted logics of probability that emerge from [1].

Efficient digital quadratic unconstrained binary optimization solvers for SAT problems

Abstract Boolean satisfiability (SAT) is a propositional logic problem of determining whether an assignment of variables satisfies a Boolean formula. Many combinatorial optimization problems can be formulated in Boolean SAT logic -- either as k-SAT decision problems or Max k-SAT optimization problems, with conflict-driven (CDCL) solvers being the most prominent. Despite their ability to handle large instances, CDCL-based solvers have fundamental scalability limitations. In light of this, we propose recently-developed quadratic unconstrained binary optimization (QUBO) solvers as an alternative platform for 3-SAT problems. To utilize them, we implement a 2-step [3-SAT]-[Max 2-SAT]-[QUBO] conversion procedure and present a rigorous proof to explicitly calculate the number of both satisfied and violated clauses of the original 3-SAT instance from the transformed Max 2-SAT formulation. We then demonstrate, through numerical simulations on several benchmark instances, that digital QUBO solvers can achieve state-of-the-art accuracy on 78-variable 3-SAT benchmark problems. Our work facilitates the broader use of quantum annealers on noisy intermediate-scale quantum (NISQ) devices, as well as their quantum-inspired digital counterparts, for solving 3-SAT problems.

Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems

Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems

Classification, Applications and Future Directions of Bio-Inspired Algorithms From Swarm Intelligence to Quantum Computing Integration Using PROMETHEE Methodology

Biologically inspired algorithms (BIAs) are computational methods that model natural processes and systems. Although various types of BIAs have been developed over time, their classification has not been extensively studied. The most widely recognized types include evolution-based algorithms that draw inspiration from natural evolution and swarm-based algorithms that mimic the collective behaviors of animal groups. In addition, ecosystem-based algorithms provide another classification perspective. Many modern BIAs go beyond this well-established framework. These algorithms have shown themselves to be highly adaptable, finding application in a variety of domains to solve challenging optimization and decision-making problems. Examples include solving problems such as network routing, resource planning, graph coloring, and the traveling salesman problem. In artificial intelligence, BIAs have improved neural networks by improving clustering, classification, and prediction accuracy. In the medical field, they have made significant contributions to advances in diagnosis and treatment planning. As BIAs continue to evolve, their integration with advanced technologies such as quantum computing paves the way to overcome challenges such as slow integration and computational inefficiency. Reflecting the adaptability and resilience of natural systems, BIAs are increasingly being used in emerging areas such as cloud computing and wireless sensor networks, highlighting their growing relevance for scalable and reliable problem solving. Research significance: The focus of this research is on investigating biologically inspired algorithms (BIAs), which are computational techniques that model natural processes. These algorithms are crucial for solving complex optimization and decision-making problems in diverse domains such as resource planning, networking, artificial intelligence, and medicine. Despite their widespread applications, the classification of BIAs has received little attention. This research highlights the importance of understanding their classification and categorization, which can lead to more effective algorithm selection and optimization. By reviewing and refining the evolutionary-based, swarm-based, and ecosystem-based algorithm categories, this work helps to improve the theoretical framework of BIAs. In addition, the research explores the potential for improving BIA convergence rates by integrating emerging technologies such as quantum computing, which addresses key challenges in real-world applications. In the end, this research could promote the application of bio-inspired algorithms across a range of domains, enhancing computer efficiency, scalability, and flexibility. Methology: Alternatives: Genetic Algorithm (Evolutionary based), Particle Swarm Optimization (Swarm-based), Ant Colony Optimization (Swarm-based), Artificial Bee Colony Algorithm (Swarm-based). Evaluation Parameters: Convergence Speed, Accuracy, Scalability, Resource Efficiency, Computational Complexity, Energy Consumption, Noise Sensitivity. Result: The results show that Genetic Algorithm (Evolutionary based) received the highest ranking, whereas Artificial Bee Colony Algorithm (Swarm-based) received the lowest ranking. Conclusion: According to the PROMETHEE method, Genetic Algorithm (Evolutionary based) ranks highest in terms of its value for Bio-Inspired Algorithms.

Selection of third-party logistics provider based on extended multimoora technique under double hierarchy linguistic single-valued neutrosophic set

The demand for third-party logistics (3PL) providers becomes an increasingly important issue for corporations seeking improved customer service and cost reduction. Currently, there is no way to select the appropriate method for selecting 3PL. Therefore, this paper develops a new extended multi-objective optimization ratio analysis plus full multiplicative form (MULTIMOORA) method under double hierarchy linguistic single-valued neutrosophic sets (DHLSVNSs). For this, we first develop a new mathematical tool, i.e., DHLSVNSs, by studying single-valued neutrosophic set (SVN) and double hierarchy linguistic term set (DHLTSs), which is very effective for solving uncertainty in decision-making problems. A list of Einstein aggregation operators and their fundamental aspects for DHLSVNSs are presented based on Einstein's norms, as aggregation operators play an essential role in decision-making. A step-by-step algorithm of the Extended DHLSVN-MULTIMOORA approach is designed to tackle ambiguous and uncertain data during decision-making problems. The algorithm developed for the suggested technique is illustrated with a numerical example relevant to 3PL. A comparison of the proposed methods with various existing methodologies is carried out to demonstrate the superiority of the suggested algorithms.

An Assessment of Factors Influencing Airline Disruption Management Decisions Through R-SWARA Method

Objective: This study aims to identify the key criteria determining operational decisions to disruptions in airline operations and analyze the importance weights of these criteria.. Method: In this context, a literature review was conducted to identify potential criteria influencing disruption management decisions. Subsequently, semi-structured interviews were held with experts working in airline operations control centers to finalize the criteria. Additionally, the relative importance weights of these factors were determined using the expert-focused R-SWARA (Rough Step-wise Weight Assessment Ratio Analysis) method. Results: The findings reveal that flight safety is the most significant criterion in the decision-making process of airlines' disruption management. Security ranked as the second most important criterion in the current study. The rankings of other criteria also provide valuable scientific insights for further examination. Originality: Research on airline disruption management primarily focuses on optimizing responses to operational disruptions and developing new solution models. These studies have predominantly employed mathematical optimization models to minimize the adverse effects of disruptions. However, no research has been found that utilizes decision-making methods to prioritize the criteria influencing responses to such disruptions. Addressing this topic as a decision-making problem in this study provides a macro perspective on the perspectives influencing the resolution of disruption management challenges.

Sustainable Supplier Selection with Adaptive Network- Based Fuzzy Inference System (ANFİS)

Choosing the right sustainable supplier is a critical decision problem for businesses. Due to the increase in the number of competitors, the increase in evaluation criteria and the difficulty in defining evaluation criteria, the need for reliable methodologies that work with high accuracy increases as the complexity of the selection problem increases. This article discusses a new approach to the problem of sustainable supplier selection based on the Adaptive Network-Based Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN) methods. In the study, firstly the sustainable supplier selection criteria have been reduced to four criteria by the ANFIS method. For sustainable supplier performance evaluation, ANFIS model and ANN model were developed. Multiple regression analysis was performed to compare the performance success of both models. The model with the highest success rate was determined as ANFIS model. At the end of the study, the most suitable sustainable supplier selection was made with ANFIS model.

Intuitionistic Hesitant Fuzzy Rough Aggregation Operator-Based EDAS Method and Its Application to Multi-Criteria Decision-Making Problems

The fundamental notions of the intuitionistic hesitant fuzzy set (IHFS) and rough set (RS) are general mathematical tools that may easily manage imprecise and uncertain information. The EDAS (Evaluation based on Distance from Average Solution) approach has an important role in decision-making (DM) problems, particularly in multi-attribute group decision-making (MAGDM) scenarios, where there are many conflicting criteria. This paper aims to introduce the IHFR-EDAS approach, which utilizes the IHF rough averaging aggregation operator. The aggregation operator is crucial for aggregating intuitionistic hesitant fuzzy numbers into a cohesive component. Additionally, we introduce the concepts of the IHF rough weighted averaging (IHFRWA) operator. For the proposed operator, a new accuracy function (AF) and score function (SF) are established. Subsequently, the suggested approach is used to show the IHFR-EDAS model for MAGDM and its stepwise procedure. In conclusion, a numerical example of the constructed model is demonstrated, and a general comparison between the investigated models and the current methods demonstrates that the investigated models are more feasible and efficient than the present methods.