Practical Decision Problems Research Articles

Dominance-Based Rough Set Model in Hesitant Fuzzy Information Systems

Hesitant fuzzy information systems have been widely applied in decision-making due to their ability to handle uncertain information. In addition, dominance relationships are often taken into account in many practical decision-making problems. Therefore, it is of great significance to conduct research on hesitant fuzzy information systems involved with dominance relations. In this study, we introduce dominance relations in a hesitant fuzzy information system to make it a dominance-based hesitant fuzzy information system, which can provide a solid new idea for comparing hesitant fuzzy elements. Furthermore, a hesitant fuzzy dominance-based rough set model is constructed, and an attribute reduction method is designed to simplify the dominance-based hesitant fuzzy information system. Further, we propose lower and upper approximation discernibility matrices in the dominance-based hesitant fuzzy decision information system to extract decision rules. In addition, two numerical examples are given to demonstrate the effectiveness of the proposed attribute reduction methods.

A sequential multiple attribute three-way group decision-making method to heterogeneous MAGDM problems with unknown weight information

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the MAGDM problems with heterogeneous information. In this paper, based on 3WD theory and sequential strategy, we propose a sequential multiple attribute three-way group decision-making (SMA3WGD) method for the heterogeneous MAGDM problem with unknown weight information to achieve this goal. At first, we compute the weights of decision-makers by using the consensus degree between each of the decision-makers in a group. Then, we calculate the collective evaluation result of multiple decision-makers by utilizing the mathematical planning method with two objectives of minimum difference and high similarity between group evaluation values and individual evaluation values. Meanwhile, a nested sequence of the set of attributes is constructed according to the attribute values offered by decision-makers that have different degrees of consensus on different attributes. On this basis, a multilevel granular structure for the heterogeneous MAGDM problems is constructed, and then the compute of the cost functions and conditional probability is explored in a single level of granular structure, and the thresholds and three-way decision rules of alternatives are further determined depending on the principle of cost minimization. Furthermore, by using a sequential strategy, we obtain a sequential three-way decision result of the heterogeneous MAGDM problems under the multilevel granular structure. At last, the practicality and advantages of the suggested method are demonstrated via a blockchain service provider selection case study, a parameter sensitivity analysis along with a comparison analysis.

K-Cardinal specificity measures

The specificity measures can indicate the degree to which a fuzzy subset or possibility distribution points to one and only one element, it can also express the degree of anxiety when decision-makers select one alternative from a set of alternatives. However, in the practical decision-making problems, people usually do not make single choice, but rather make multiple choices. So in this paper, we introduce the concept of k-cardinal specificity measures which promote Yager's work about specificity measures, it can show the degree of anxiety when decision-makers want to select k alternatives from a set of n alternatives when 1≤k<n, some interesting properties about the k-cardinal specificity measures are also investigated.

T-Spherical fuzzy TOPSIS method based on distance measures and Hamacher Heronian mean averaging aggregation operators and its application to waste management

T-Spherical fuzzy sets provide a robust framework for handling uncertainty in real-world decision-making. This paper addresses the construction of T-Spherical fuzzy Heronian mean averaging aggregation operators using novel Hamacher operations. The study mathematically verifies the properties of these operators and develops new distance measures such as Hamming, Euclidean and Jaccard for quantifying dissimilarity between T-Spherical fuzzy numbers. Furthermore, the TOPSIS method is implemented using the proposed operators and distance measures to solve a practical decision-making problem in selecting the best transportation firm for medical waste disposal. A sensitivity analysis is made to assesses the efficacy of the proposed operators, while a comparison study with existing methods is exhibited to establish their efficiency.

MultiFuzzTOPS: A Fuzzy Multi-Criteria Decision-Making Model Using Type-2 Soft Sets and TOPSIS

Effective and optimal decision-making can enhance system performance, potentially leading to a positive reputation and financial gains. Multi-criteria decision-making (MCDM) is an important research topic widely applied to practical decision-making problems. Using the basic idea of symmetry to balance the arrangement where elements or features have an equality or similarity in distribution, MCDM provides robust decisions in such multi-dimensional complex issues. This study proposes MultiFuzzTOPS, a decision-making model to deal with complexity of multi-criteria decision-making. The proposed MultiFuzzTOPS leverages the fuzzy logic and soft sets such as type-2 soft sets (T2SS) and technique for order preference by similarity to ideal solution (TOPSIS) for decision-making. We validate the proposed model by implementing it to solve the pesticide selection problem in food science by considering various criteria for the selection of pesticides. Our proposed MultiFuzzTOPS recommends the best pesticide compared with its counterparts because it covers the maximum information for the selection of the best alternative. Results are ranked on the basis of the Hamming distance and similarity coefficient. We also validate the effectiveness by performing the sensitivity analysis, and the validation shows the reliability and effectiveness of our proposed model.

Multiple landscape measure-based approach for dynamic optimization problems

Many practical decision-making problems involve dynamic scenarios, where the decision variables, conditions and/or parameters of their optimization models change over time. Such problems are known as dynamic optimization problems (DOPs). Although evolutionary algorithms (EAs) have been effective in solving static optimization problems, they face challenges in handling DOPs. To improve the performance of EAs in dealing with DOPs, this paper proposes a new evolutionary framework that uses different landscape measures to analyze problem landscapes and utilizes the information gained from this to improve the searching process. The proposed method is adopted in three EAs to deal with dynamic functions from IEEE-CEC2009 and two real-world problems. According to the experimental results, LIDOA with multi-measure methods improves the performance of GA, jDE and CMA-ES, on average by 6.71%, 3.03% and 7.78% on benchmark problems, respectively.

Multi-criteria constrained interval type-2 fuzzy decision-making: A space analysis perspective

Practical decision-making problems invariably involve inherent uncertainty in information, making it challenging to obtain precise evaluations. In this research, the utilization of constrained interval type-2 fuzzy sets (CIT2 FSs) enables the representation of complex uncertainty in decision information, and a space analysis approach, called CIT2 fuzzy multi-criteria acceptability analysis (CIT2FMAA), is proposed for conducting reliable decision-making. The CIT2FMAA introduces a novel paradigm for type-2 fuzzy decision-making, wherein CIT2 fuzzy evaluations are acknowledged as spaces of type-1 fuzzy evaluations and processed using type-1 fuzzy operations and methodologies. First, from a space analysis perspective, the type-2 fuzzy measures of logical rank expressions are formalized as CIT2 fuzzy rank acceptability indexes, and the CIT2 fuzzy rank acceptability analysis is developed to rank CIT2 fuzzy numbers. Second, the weighted average of the CIT2 fuzzy numbers is defined, and the CIT2FMAA is proposed to support uncertain multi-criteria decision-making. Subsequently, a sampling algorithm is designed for implementing CIT2 fuzzy rank acceptability analysis and CIT2FMAA. Finally, a linguistic disease diagnosis is performed to demonstrate the efficiency of CIT2FMAA. Compared to traditional type-2 fuzzy decision-making methods, CIT2FMAA is more efficient because it not only derives ranks of alternatives but also provides confidence degrees for those ranks.

An extended multi-expert concept lattice-based heterogeneous multi-attribute group decision-making approach

In practical multi-attribute group decision-making (MAGDM) problems, it is common to utilize heterogeneous representation forms to express distinct preference information for different experts, primarily due to their diverse backgrounds. To address the problem of reducing information loss from the aggregation of experts' opinions in heterogeneous MAGDM as well as improving the interpretability of the decision-making process, this paper introduces a concept lattice-based heterogeneous MAGDM approach. The heterogeneous multi-expert formal context is first proposed to capture the heterogeneous evaluation information of alternatives provided by different experts. Then, extended multi-expert concept lattices are constructed to aggregate evaluation information of alternatives by different experts. In this case, all concepts are considered to minimize information loss during the aggregation process. Second, to obtain more reasonable decision results, the distance between the concept intents and the heterogeneous positive and negative ideal solutions is considered, and the alternatives are ranked based on this measure. In addition, the MAGDM process for each alternative is visualized using the extended multi-expert concept lattices. This representation aids in identifying key concepts, their interdependencies, and the overall impact on the decision result. Finally, numerical examples and comparative analysis validate the validity and rationality of the proposed approach in heterogeneous MAGDM.

A multi-neighborhood tabu search for solving multi-budget maximum coverage problem

Many practical decision-making problems involve selecting k distinct subsets of objects from a set of candidate objects such that the chosen objects optimize a given goal while satisfying some constraints. The multi-budget maximum coverage problem (MMCP) is a general model that facilitates formulating such decision-making problems. Given a budget of k, a set of elements with profits, and a set of items with costs where each item is composed of a subset of the elements, the MMCP aims to select k disjoint subsets of items that maximize the total profits of the elements covered by these subsets, while their costs do not exceed their respective budgets. In this work, we present a multi-neighborhood tabu search (MTS) for this NP-hard problem. MTS comprises three essential components that employ four efficient neighborhoods to collaboratively perform the neighborhood exploration to obtain high-quality local optima, select neighborhood solutions with added perturbations, and generate offspring solutions based on population information exchange. To evaluate the effectiveness of MTS, we performed computations on 30 benchmark instances of the extended BMCP. We compared the results with the approximation algorithm, genetic algorithm, particle swarm optimization algorithm, and CPLEX solver. We also provide experiments to highlight the beneficial effect of the essential components in the MTS algorithm. The experimental results demonstrate that MTS not only performs efficiently but also yields solutions of superior quality.

Classification and regression in prescriptive analytics: Development of hybrid models and an example of ship inspection by port state control

In prescriptive analytics, unknown quantities are involved in practical decision-making problems, and these unknown quantities need to be predicted using auxiliary data. A classic approach is to develop machine learning (ML) models to generate point estimates, which are then input to the decision making problem in a deterministic manner to prescribe the optimal decision. However, the limited quantity and inevitable errors in the auxiliary data lead to inaccurate predictions and thus sub-optimal decisions. One viable approach to addressing the above issue is to consider the uncertainties in data by inputting the conditional distributions of the unknown quantities on the auxiliary data to the optimization problem on hand, and the distributions are predicted by regression ML models. Meanwhile, it is observed that the quantitative target in some problems are discrete, and these properties are analogous to categorical targets in classification problems. Considering the fact that describing and estimating the distribution of categorical variables are much easier than quantitative variables, this study innovatively develops random forest (RF) models with regression and classification features to generate the distribution of quantitative targets that are discrete. Especially, nodes splitting criteria in the RF models is in a regression manner, while the outputs of individual decision trees and the whole RF model is in a classification manner. Numerical experiments using real port state control (PSC) inspection records and settings at the Hong Kong port are conducted to validate and compare the above prescriptive analytics approaches. The superiority of applying the newly proposed RF model into the development of prescriptive analytics approaches is also demonstrated.

Novel transformation methods from semi-three-way decision spaces to three-way decision spaces and their applications

As a vital theoretical research of three-way decision (3WD), three-way decision space (3WDS) further enriches the theory on 3WD and enhances its application. In 3WDS, decision evaluation function (DEF) is an indispensable component and plays a crucial role in practical decision problems. But, a large number of valuable functions are the semi-decision evaluation functions (S-DEFs). For this problem, Hu gave many S-DEFs and presented a transformation method from S-DEFs to DEFs. Since then, it has led to a research hotspot that is the introduction of the usual aggregation functions (AFs) into the transformation method of Hu's form. This paper focuses on the new form of transformation methods from semi-three-way decision spaces (S-3WDSs) to 3WDSs and their applications. Firstly, we present a new form of transformation method with parameter σ from S-DEFs to DEFs. And, we verify both theoretically and experimentally that this introduced parameter σ provides a valid window to dynamically adjust 3WDSs by changing the parameter σ, respectively. Notably, this new form of transformation method covers all existing 3WDSs transformation methods except the representable uninorms-based methods. Secondly, we give a more general function named with F-function, which can be introduced to these construction methods in the same way as usual AFs. This enables decision-makers to select more suitable functions for solving practical problems. Thirdly, based on the new form, usual AFs and F-functions, we present some new construction methods of S-DEFs (resp. DEFs) related to fuzzy sets, interval-valued fuzzy sets and random sets. Finally, to make up for the application of the theory of 3WDSs in complex datasets, we select 7 complex datasets for experimenting and conduct comparative and sensitivity analysis to test the viability and efficacy of our methods. Notably, we also give an algorithm to calculate the thresholds α and β, which can adjust the optimal thresholds α and β according to different 3WDSs.

A fair consensus adjustment mechanism for large-scale group decision making in term of Gini coefficient

With social media and e-democracy development, decision-making environments and problems have become increasingly complex. Traditional group decision-making, including a small number of decision makers, cannot deal with many practical decision-making problems. Large-scale group decision-making has received extensive attention as an efficient measure to address this issue. This paper proposes a two-stage optimization consensus mechanism for large-scale group decision-making by the Gini coefficient, which considers both the consensus among decision makers’ opinions and the fairness of consensus adjustment. First, we introduce an improved density peak clustering method to cluster decision makers into subgroups that can avoid different clustering centers being too close. Then, we define the subgroups' weights by the silhouette coefficient, the subgroup judgment distance, and their sizes. Respecting the importance of minority opinions, we design a new method to identify and adjust the weights of the minority opinions, which considers more aspects than previous ones. Further, we use the Gini coefficient to measure the equity of consensus adjustment of the group and subgroup. Moreover, we present a two-stage Gini coefficient-based consensus mechanism to obtain the adjusted consensus individual decision matrices, which can ensure the minimization and fairness of consensus adjustment and allocation under the set constraints. Based on these results, we give a new large-scale group decision-making method. Notably, it is the first method that fully considers the opinions of minor subgroups and the fairness of allocation results. These characteristics are essential in decision-making problems such as resource allocation and urban public construction. Finally, we show the utility and validity of the new method through a case study and make the comparative analysis from numerical and principle aspects.

Optimization consensus analysis for group decision making in view of non-transferable and transferable allocation schemes

Most practical decision-making problems commonly contain several alternatives evaluated by more than one decision maker (DM) according to the built criteria system. It is known as multi-criteria group decision making problems. The decision-making results should be based on highly similar individual opinions to derive the most suitable choice. Nevertheless, the divergence of individual judgments is usually significant due to various subjective and objective reasons. In this case, some DMs need to modify their judgments to derive final results, the consensus adjustment in group decision making. Like supply chain management, the centralized consensus process can minimize individual adjustments under the set consensus condition. Based on the hypothesis of rational humans that individuals always pursue maximum benefits, this paper proposes an iterative consensus process in a centralized environment. According to non-consensus adjustments, the iterative procedure contains non-transferable and transferable two parts. When the non-transferable consensus adjustment procedure is completed, the transferable consensus adjustment procedure is activated using an asymmetrical Nash bargaining game allocation mechanism. Additionally, a case study is provided to demonstrate the efficaciousness and practicality of the new method. Meanwhile, sensitivity and comparison analyses are done.

Integrated multi-criteria group decision-making methods based on q-rung picture fuzzy sets for the identification of occupational hazards.

Practical group decision-making (DM) problems frequently involve challenging circumstances when attempting to assign appropriate values to the data because of the haziness and uncertainty of the surrounding circumstances. In order to address the ambiguity and imprecision that arise in DM issues, q-rung picture fuzzy sets (q-RPFSs) have a more broader structure. In this research, the criteria importance through intercriteria correlation (CRITIC) and the decision-making trial and evaluation laboratory (DEMATEL) techniques are separately integrated with the multi-attributive border approximation area comparison (MABAC) method. The MABAC method, which measures how far each alternative is from the border approximation area, is very stable and useful for resolving real-world problems. The CRITIC technique calculates the criteria weights by taking into account the relationships between attributes, and the DEMATEL methodology is recognized as the best method for determining how several criteria or factors interact with one another. As a result of these justifications, we made the decision to create the CRITIC-MABAC and DEMATEL-MABAC procedures for q-RPFSs. By using the suggested strategies, the primary goal of this article is to determine the occupational risk that has the greatest impact on the health of a hospital's medical staff. We begin by employing the CRITIC technique to determine the criteria weights. In addition, we calculate the weights of the criteria using the DEMATEL approach. The offered methodologies are investigated for their applicability to determine the most serious occupational hazard for hospital employees. We conducted a comparison with three earlier studies to verify the accuracy of the tactics that are offered.

Distributed dynamic online learning with differential privacy via path-length measurement

Dynamic online learning has been given great concerns as real-time and non-stationary systems develop and it can be used for solving many practical sequential decision problems like dynamic recommendation systems for online advertisement. Due to the large volume of big data in online learning, dynamic online learning is required to be deployed under the distributed framework where the communication among agents is time-varying and real-time. Meanwhile, since the information sharing among agents is vulnerable to interception by the adversary, the privacy protection problem is significant, but to the best of our knowledge, this work is the first to consider privacy-preserving distributed online learning under dynamic circumstances. Specifically, based on a differentially private distributed online gradient descent algorithm, we minimize the regret of the network. Moreover, via the rigorous mathematical analysis, we achieve the sublinear regret bound under ϵ-differential privacy, that is, we respectively obtain O(T+VT) and O(log⁡T+VT) for convex and strongly convex function, where T is the time horizon and the path-length VT reflects the variation of the minimizer sequence. The experiments performed on the real datasets validate that the presented algorithm achieves acceptable optimization performance even when privacy protection is relatively strict.

Double Sparse Deep Reinforcement Learning via Multilayer Sparse Coding and Nonconvex Regularized Pruning.

Deep reinforcement learning (DRL), which highly depends on the data representation, has shown its potential in many practical decision-making problems. However, the process of acquiring representations in DRL is easily affected by interference from models, and moreover leaves unnecessary parameters, leading to control performance reduction. In this article, we propose a double sparse DRL via multilayer sparse coding and nonconvex regularized pruning. To alleviate interference in DRL, we propose a multilayer sparse-coding-structural network to obtain deep sparse representation for control in reinforcement learning. Furthermore, we employ a nonconvex log regularizer to promote strong sparsity, efficiently removing the unnecessary weights with a regularizer-based pruning scheme. Hence, a double sparse DRL algorithm is developed, which can not only learn deep sparse representation to reduce the interference but also remove redundant weights while keeping the robust performance. The experimental results in five benchmark environments of the deep q network (DQN) architecture demonstrate that the proposed method with deep sparse representations from the multilayer sparse-coding structure can outperform existing sparse-coding-based DRL in control, for example, completing Mountain Car with 140.81 steps, achieving near 10% reward increase from the single-layer sparse-coding DRL algorithm, and obtaining 286.08 scores in Catcher, which are over two times the rewards of the other algorithms. Moreover, the proposed algorithm can reduce over 80% parameters while keeping performance improvements from deep sparse representations.

Multigranulation-based double-quantitative rough sets for multi-source event-based decision systems

For the multi-source event-based decision system (MSEDS), each source exhibits its own one-sidedness and limitations. How to fuse the information contained in multiple sources to make a comprehensive decision has become an important research field. In this paper, the related concepts of support characteristic levels in one source are introduced from the viewpoint of single quantification and double quantification. The multigranulation-based single-quantitative rough set models, the related properties, and decision rules for single source based on the presented single-quantitative support characteristic levels are discussed. How to construct multigranulation-based single-quantitative and double-quantitative rough approximations in MSEDS and to analyze their corresponding properties and decision-making rules is addressed. To interpret the proposed multigranulation-based single-quantitative rough sets and double-quantitative rough sets in MSEDS, an illustrative example about an MSEDS formed by medical expert diagnosis is investigated, which is helpful to apply these developments to practical event-based decision-making problems.

Linguistic Single-Valued Neutrosophic Multi-Criteria Group Decision Making Based on Personalized Individual Semantics and Consensus

In practical linguistic multi-criteria group decision-making (MCGDM) problems, words may indicate different meanings for various decision makers (DMs), and a high level of group consensus indicates that most of the group members are satisfied with the final solution. This study aims at developing a novel framework that considers the personalized individual semantics (PISs) and group consensus of DMs to tackle linguistic single-valued neutrosophic MCGDM problems. First, a novel discrimination measure for linguistic single-valued neutrosophic numbers (LSVNNs) is proposed, based on which a discrimination-based optimization model is built to assign personalized numerical scales (PNSs). Second, an extended consensus-based optimization model is constructed to identify the weights of DMs considering the group consensus. Then, the overall evaluations of all the alternatives are obtained based on the LSVNN aggregation operator to identify the ranking of alternatives. Finally, the results of the illustrative example, sensitivity and comparative analysis are presented to verify the feasibility and effectiveness of the proposed method.

A combined intuitionistic fuzzy closeness coefficient and a double normalization-based WISP method to solve the gerontechnology selection problem for aging persons and people with disability

&lt;abstract&gt; &lt;p&gt;This study aims to introduce a decision-making framework for prioritizing gerontechnologies (GTs) for aging persons and people with disability under an intuitionistic fuzzy set (IFS) context. First, the intuitionistic fuzzy (IF)-divergence measure and its properties are developed to obtain the criteria weight. Second, a new exponential function-based score function and its properties for the IFS are introduced to order the different IFSs. Third, an IF-relative closeness coefficient (RCC)-based method is proposed to determine the criteria weights. Fourth, the double normalization (DN) procedure-based weighted integrated sum product (WISP) approach is introduced under the IFSs. To demonstrate the applicability and usefulness of the proposed IF-RCC-DN-WISP model, a case study that involves ranking the different GTs for aging persons and people with disability is conducted from an IF perspective. The results of the developed model show that mobility is the most appropriate gerontechnology for aging persons and people with disability. A comparison with different models is also performed to prove the superiority of the obtained results. The comparative study shows how the developed model outperforms the other extant models, as it can offer more sensible outcomes. Therefore, it is more suitable and efficient for expressing uncertain information when treating practical decision-making problems.&lt;/p&gt; &lt;/abstract&gt;

Development of the PRISM Risk Assessment Method Based on a Multiple AHP-TOPSIS Approach

The PRISM method is a risk assessment approach that focuses on hidden-risk identification and ranking. The combined AHP-PRISM method was created for strategic assessments based on pairwise comparisons. The PRISM and AHP-PRISM methods have remarkable visual decision support and control functions that make them useful in practical problem solving. However, the methods can be successfully applied with the same factor weights. To eliminate this significant disadvantage and enable an in-depth analysis of the alternatives based on the ideal best and ideal worst solutions, AHP-PRISM was integrated with TOPSIS in this study. As a result, the novel AHP-TOPSIS-based PRISM method can be configured more extensively for practical decision-making problems than the previous PRISM approaches. In addition, the novel method supports the ideal best and worst analysis of the alternatives without losing its ability to focus on identifying hidden risk. The method was tested on data related to strategic incident groups of incoming logistics business processes at a nuclear power plant.