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In this paper, we developed a dynamic Bayesian network (DBN) model to quantify uncertainties on battlefields. The model consists of the enemy's intention prediction model and the intelligence, surveillance, and reconnaissance (ISR) reliability model quantified by using the sensor detection probability. We calibrated and validated the DBN using a historical dataset based on actual provocations by comprehensively referencing open-source intelligence, including news articles from mass media and official announcements of the Ministry of National Defense. The calibrated model was then used to predict the enemy's intention in the near future, and the accuracy of the model was 84.6%. We suggested an appropriate course of action (COA) based on the enemy's intention to expedite decision-making. We further studied the interaction between the predicted enemy's intention and the selected COA. The main results of this paper are that various information regarding enemy actions, either equality or inequality, can be incorporated into the decision-making process. [ABSTRACT FROM AUTHOR]

Over the last twenty years, patient opinions of the received healthcare services have emerged as a crucial quality indicator and a valuable instrument for evaluating the service quality of health organizations. However, many surveyors still utilized uncertain information for conducting this evaluation process. Whereas, in the past few years, the issues of inconsistency, indeterminacy, and uncertainty in data have been growing and have become a matter of concern in dealing with decision-making issues. This paper proposes an improved RAFSI method based on a single-valued trapezoidal neutrosophic number (SVTraNN) as a multi-attribute framework for dealing with a healthcare service quality evaluation. The proposed framework consists of two features: a feature for determining numerical criteria weights involving the procedures of the Ordinal Priority Approach (OPA) and a feature for selecting the best polyclinic through contributing our new SVTraNN Harmonic and arithmetic mean to yield criterion functions in the RAFSI method. This paper then compares the results obtained by the proposed method with other SVTraNN decision-making methods. A comparative analysis and rank reversal testing of the improved method demonstrated that the multi-attribute framework proffers the option of capability and stability in overcoming inconsistency, indeterminacy, and uncertainty information efficiently during the healthcare service quality evaluation. [ABSTRACT FROM AUTHOR]

To evaluate some real-life multi-criteria decision-making (MCDM) problems, the decision-makers (DMs) need an interactive and flexible MCDM approach that works well for extended decision-making space and has the ability to apply any suitable operational laws to fuse the information under incomplete criteria weighting information. The objective of this study is to propose a multi-criteria group decision-making (MCGDM) approach to evaluate any real-life MCDM problem under incomplete criteria weighting information. To achieve the objective, the paper proposes an integrated MCGDM approach in which the q -rung orthopair fuzzy set (q-ROFS) is used to provide the extended decision-making space to the DMs, a transformative approach is applied to find the DMs' weights, the maximizing deviation method is implemented to find the criteria weights, Archimedean t -norm and t -conorm (ATU)-based operational laws are adopted to fuse the information, and the TOPSIS method is applied to rank the alternatives. The proposed integrated MCGDM approach has been applied to a real-life MCDM problem associated with the evaluation of different strategies implemented by governments to contain the COVID-19 pandemic. A sensitivity analysis is conducted to examine the impact of changes in the parameters involved in the proposed MCGDM approach on the results. A comparative analysis is also performed with some existing classical and aggregation operator-based MCDM methods to demonstrate the consistency of the proposed approach. Findings suggest that the proposed MCGDM approach is general and flexible enough to evaluate any real-life MCDM problem under unknown criteria weighting conditions. • The paper proposes ATU based qROF-TOPSIS method for unknown criteria weights. • The method applies q-ROFS to quantify uncertainty and ATU to fuse the information. • The maximizing deviation method is used to evaluate the criteria weights. • The TOPSIS method is applied to rank the alternatives. • The method is applied to evaluate government strategies against COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Consensus analysis is required to ensure the quality of decision results. It means that some DMs' opinions must be adjusted to reach a consensus. As a result, it harms DMs' benefits because their opinions are not fully considered. Additionally, the consensus-reaching needs cooperation between DMs. Therefore, the consensus improvement can be seen as a cooperative game among DMs. Considering the benefit conflicts of DMs, this paper studies the adjustments of DMs in view of the Nash-bargaining game, which can ensure the global Pareto optimality and maximum fairness of the consensus adjustment scheme. Considering the inconsistency between the Pareto and individual optimal solutions, we first study the Nash-bargaining consensus adjustment solutions between individual DMs. For the situation where DMs form coalitions to reduce their adjustments, two cases for the coalitional Nash-bargaining consensus adjustment solutions of DMs are further studied. Meanwhile, interactive algorithms for the formation of DM coalitions are provided. The main characteristics of the Nash-bargaining consensus adjustment solution are the consideration of personal interest, the rationality of DMs, and the fairness of the allocation scheme. Moreover, this paper takes the choice of the cooperative partner of elderly care institutions as an example to show the concrete application of built models. [ABSTRACT FROM AUTHOR]

Integrating smart industrial products (SIPs) into production processes introduces new dynamics of capabilities and challenges within organisations. However, a prevalent issue is the ad hoc adoption of SIPs without a cohesive strategy, resulting in isolated solutions that do not effectively enhance core organisational functions. Our research fills this critical gap by identifying and combining the capabilities and barriers posed by SIPs. Our findings allow us to propose a framework comprising the stages each main organisational function needs to develop to move from a traditional to a completely autonomous decision-making function. This evolutional path requires that each organisational function acquires selected capabilities and overcomes specific barriers. Our approach encompasses a multi-method methodology, commencing with a systematic literature review (SLR) encompassing 44 papers, leading to the identification of 20 capabilities and 23 barriers. Subsequently, through rigorous content analyses and expert interviews, we formulate the framework. This paper contributes to academia by providing a framework with an evolutional path that main organisational functions may follow according to the capability they acquire from the SIP and which barriers should be surpassed at each level. For managers in practice, the proposed framework enables a better understanding of the stage of their operations and which capabilities will take their main organisational function from traditional (level 0) to autonomous (level 3) stages. [ABSTRACT FROM AUTHOR]

• Study analyzes 1374 papers on sensitivity analysis in Scopus. • RStudio, CiteSpace, and VOSviewer are used. • China has most articles published; India leads in international cooperation. • Top three cited journals: J. Clean. Prod., Expert Syst. Appl., and Comput. Ind. Eng. • Most cited authors are Pamučar D, Kahraman C, and Zavadskas EK. In the present era, the implementation of scientifically grounded Multi-Criteria Decision Making (MCDM) has emerged as a pivotal solution to diverse decision-making challenges across various domains. Although a substantial body of exploratory, conceptual, and experimental studies exists, only 9.457% studies have incorporated sensitivity analyses to assess the robustness of MCDM methods. An exhaustive scientific exploration of sensitivity analysis within the scope of MCDM is thus lacking. This research aims to address this gap through Bibliometric Analysis while examining 1374 articles published between January 2000 and March 2023 from the Scopus database. Using RStudio (Biblioshiny), CiteSpace, and VOSviewer software, the study constructs a visual representation of the most prolific countries, institutions, and authors. Impressively, China takes the lead in article publications, while India excels in international collaboration. "An extended TODIM multi-criteria group decision-making method for green supplier selection in an interval type-2 fuzzy environment," featured in the Journal of Environmental Management, emerges as the most cited paper with a total citation of 455. The study also identifies the top three most cited journals, namely "Journal of Cleaner Production," "Expert Systems With Applications," and "Computers and Industrial Engineering." "North China Electric Power University" is the leading institute with the highest research outputs. "Pamučar D" is the most cited author, with 2594 citations and 39 articles, followed by "Kahraman C" and "Zavadskas EK. This study sheds light on trends in scientific developments and collaborations, providing a model for the application of sensitivity analysis in MCDM research and highlighting global trends. An understanding of the current state of sensitivity analysis research can assist researchers working in the entire domain of MCDM. Additionally, the visualization provides prescriptive data for future work and applications related to sensitivity analysis. [ABSTRACT FROM AUTHOR]

• The centroid distance of PFNs is proposed and its properties are discussed. • Propose a score function based on the centroid distance and discuss its properties. • Applications of Pythagorean fuzzy clustering, ranking and MCDM are developed. • Some advantages of the proposed methods are showed through comparative analysis. By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages. [ABSTRACT FROM AUTHOR]

There are more complex heterogeneous preference expressions and methods (HPEMs) in heterogeneous multi-attribute group decision making (MAGDM) with several attribute sets, which makes the effective fusion of HPEMs become an important challenge in practical applications. The paper aims to propose a stochastic-integration-based decision support framework for the unified fusion of various HPEMs and provide some feasible solution methods. The construction process of decision support framework is embodied in recombining and connecting the HPEMs into "information flows" according to the traditional multi-attribute decision making procedures. For the information flows, three types of solution methods, such as the global solution method, the simplified solution method, and the consistency-measure-based solution method, are proposed to transform them into a final superiority-probability-based pairwise comparison matrix (SPM). Based on the final SPM, a ranking method is developed that uses probabilities to present the superiority relationship between two alternatives. In addition, a comparison discussion is given to highlight the advantages and disadvantages of the research. And two application examples are used to illustrate the validity of the proposed framework and solution methods. The decision support framework proposed in this paper is more open and inclusive, providing a feasible way for the unified fusion of various HPEMs. The proposed solution methods not only realize the sufficient solution, but also extend the decision results to probabilistic form, making the decision results more consistent with heterogeneous environment. The research in the paper can further enrich the methodology of heterogeneous MAGDM system and provide methods and technic support for complex group decision-making problems. [ABSTRACT FROM AUTHOR]

• Investigate a novel TrFBWM to yield the TrF criteria weights. • Develop the TrFZSG to rank the alternatives. • Propose a TrF-BWM-ZSG by fusinging the BWM and ZSG. This paper investigates a hybrid emergency decision-making (EDM) method called TrF-BWM-ZSG by combining the BWM (best-worst method) and ZSG (zero-sum game) in trapezoidal fuzzy (TrF) environment. Firstly, this paper defines the entropy and similarity for trapezoidal fuzzy numbers (TrFNs). Based on the entropy and similarity measures, a bi-objective programming model is constructed to distribute the weights for experts. Then, based on the multiplicative consistent trapezoidal fuzzy reference comparisons (TrFRCs), a novel TrFBWM is investigated to yield the TrF criteria weights. This novel TrFBWM highlights two innovations: (i) A crisp goal programming weight-determining model is constructed by using the multiplicative consistent TrFRCs; (ii) A reasonable consistency ratio is defined, in which the consistency index is determined according to the consistent transitivity of TrFRCs. Afterwards, the TrFZSG is developed to achieve the alternatives' ranking order by extending the classical ZSG into TrF environment. Lastly, the availability of the TrF-BWM-ZSG is corroborated through a real EDM case of Henan rainstorm disaster. The advantages of the TrF-BWM-ZSG are manifested by several valuable comparisons. Some desirable results derived by the TrF-BWM-ZSG are received: (i) The optimal alternative selected by the proposed TrF-BWM-ZSG suits the actual rescue situation; (ii) The proposed TrF-BWM-ZSG owns high generality since TrFN can reduce to crisp number, interval and triangular fuzzy number; (iii) The developed novel TrFBWM is helpful to preserve more inherent information of TrFNs. [ABSTRACT FROM AUTHOR]

The notion of linguistic interval-valued intuitionistic fuzzy set (LIVIFS) is one of the best tools in order to deal with the qualitative decision making problems. Therefore, in this paper a linguistic interval-valued intuitionistic fuzzy (LIVIF) QUALIFLEX method with a likelihood-based comparison approach is proposed. First, the notion of likelihood of fuzzy preference relation (FPRs) to compare the linguistic interval valued intuitionistic fuzzy numbers (LIVIFNs). By employing a criterion-wise preference assessment of alternatives through the comparison of likelihoods, we introduce a novel QUALIFLEX-based model. This model aims to quantify the degree of concordance in the complete preference order for effective management of decisions involving multiple criteria. We demonstrate the practicality and applicability of the proposed methods through an illustrative example, specifically focusing on the context of Supplier Selection Problems. To validate the efficacy of the proposed methodology, a comparative analysis is performed against other existing methods. [ABSTRACT FROM AUTHOR]

Three-way incomplete multi-attribute decision-making model (TWIMADM) for multiple decision makers is an important topic in data science, which is an effective tool for decision making. However, calculation results of neighborhood blocks are still affected by neighborhood radius. In addition, most of the existing models without considering the information from the evaluations of multiple decision makers separately is difficult to obtain a comprehensive assessment. To address these problems, this paper builds a novel multi-source TWD model based on multi-granularity ball. First, the well-established granular ball is extended to multi-granularity, by which a weighted conditional probability is generated to divide the concerned region into three parts: positive, negative and boundary region. The calculation of the multi-granularity ball is independence of the neighborhood radius parameter. Subsequently, the joint entropy is developed by using the defined multi-granularity to obtain the optimal decision. Furthermore, the relative utility function is utilized to capture the preferences of decision makers, the classification and ranking of each object are determined by calculating the threshold value and expected utility value. Evaluation information from all decision makers are combined and fused. Finally, a novel TWD model is developed to address the challenges of incomplete multi-attribute decision-making problems involving multiple decision makers. The comparative case analyses and experimental evaluations have demonstrated the stability, validity, and feasibility of our model. • We first extend the well-established granular ball to multi-granularity. • We utilize multi-granularity to compute neighborhood blocks. • We design the corresponding algorithm for multi-granularity ball generation. • Consider the multiple decision makers scenario. • We establish joint entropy to fully consider the information of each decision maker. [ABSTRACT FROM AUTHOR]

A missing data problem is inevitable when collecting time series datasets from marine sensors. Due to this, sensor data is not reliable enough to assist decision-making. To impute missing values, a number of methods are available. Choosing the best imputation method, however, is not a trivial task, as it usually involves domain expertise and trial-and-error iterations. Additionally, if imputations are done carelessly, they produce a high error, resulting in incorrect assumptions by stakeholders. In this paper, a meta-learning approach is presented that can be used to extract characteristics of the underlying data, and based on that, a less error-prone imputation method is recommended. Ten commercial ocean-going vessel datasets are used to evaluate our proposed method. A total of 29,527 data samples were generated, comprising 22 inputs and 1 target. The proposed method achieves a weighted F1-Score of 87.5% when utilizing stratified 10-fold cross-validation. Our approach can improve the average imputation score up to 86%, with the worst-case improvement being 5%. This demonstrates that our proposed approach is efficient and effective in recommending the best imputation methods. • The characteristics of the dataset influence the recommendation of imputation methods. • Meta-learning can model the relationship between dataset characteristics and the performance of imputation methods. • Interpolation and MissForest are the most effective imputation techniques. [ABSTRACT FROM AUTHOR]

Feature selection facilitates uncertainty disposal and information mining, and it has received widespread research interests. Divergence-based fuzzy rough sets (Div-FRSs), a new kind of fuzzy rough sets, have been applied to feature selection and induced two efficient algorithms, FS-AFS and FS-RFS. Nevertheless, FS-AFS and FS-RFS still have advancement space, because the dependency functions only focus on lower approximation and ignore the uncertainty in upper approximation, which will certainly undermine the algorithmic evaluation effects. To this end, this paper introduces the upper approximation and fuses it with lower approximation via class-specific pattern and Spearman coefficient to construct a new information measurement for metric perfection, called Spearman-based self-information. Relying on this new measurement, a novel feature selection algorithm SPESI is established to improve FS-AFS and FS-RFS. At first, Spearman coefficient is introduced and upper approximation is defined. Second, class-driven precision and roughness are built by incorporating Spearman coefficient-based class-specific weight vectors with class-driven lower and upper approximations. Meanwhile, the granulation monotonicity of newly-defined measurements is also explored. Then, the core measurement Spearman-based self-information is firstly given and its feature significance motivates a feature selection algorithm SPESI with heuristic search. Finally, data experiments are implemented to validate the effectiveness of SPESI, and a conclusion can be drawn that SPESI outperforms FS-AFS and FS-RFS to acquire better classification performances with fewer feature numbers and less running time. • Upper approximation is first defined in Div-FRSs to induce measurement. • Class-specific information of D s enriches decision analysis. • Spearman correlation coefficient reveals the relevance between classes. • Algorithm SPESI outperforms FS-AFS and FS-RFS to offer better classification performances. [ABSTRACT FROM AUTHOR]

Using multi-criteria decision making (MCDM) technique to rank alternatives is a well-known area of study in which aggregation operators, such as ordered weighted averaging (OWA) and induced OWA (IOWA) operators, play an important role in merging information and producing an overall ranking. The distance measures from ideal argument values in aggregation operators have gained attention in recent literature. Distance measures are traditionally used as argument variables, which leads to the depiction that the attribute cannot be aggregated directly. In this paper, a generalized form of distance-induced OWA (DIOWA) operators is proposed with distance measures used as order-inducing variables. A distinctive benefit of DIOWA operators is that they permit us to consider ideal argument values while simultaneously also taking the attribute values as argument variables. Three variants of DIOWA operators are proposed and investigated, namely a) the Hamming distance-induced OWA (HDIOWA) operator, b) the normalized Hamming distance-induced OWA (NHDIOWA) operator, and c) the weighted Hamming distance-induced OWA (WHDIOWA) operator. We highlight their important properties and provide proofs to necessary theorems, and also suggest the determination methods for calculating their associated weights. We discuss further extensions of the proposed DIOWA operators with the help of generalized and quasi-arithmetic means. We discuss the use of our proposed family of operators for two different decision making situations, and demonstrate their validity by an illustrative numerical example. Finally, we apply the proposed operators to a real-life problem of ranking Chinese provinces for their science and technology (S&T) development levels. The proposed operators are shown to be a useful addition to the aggregation toolbox for decision analysts. [ABSTRACT FROM AUTHOR]

Highlights • A baseline value based method is introduced into multiple criteria problems, where redundant solutions always exist in non-dominated solutions. • Different selection strategies are analyzed on single and multiple objectives problems under baseline value. • The VIKOR method is extended to E-VIKOR, which is able to deal with evaluation problems with incomplete information, denoted by an interval mode in the paper. Abstract For multi-objective problems, most corresponding methods will terminate when the non-dominated set (Pareto set) is acquired, while some redundant systems may exist in it. The same situation may appear in the weapon system portfolio selection as it always tends to be a multi-objective problem. The paper employs a baseline-based method to decide whether to select a redundant system. First, the problem is analyzed in depth to demonstrate the connotation of baseline and related concepts are defined. Then, based on the definition of the redundancy system, baseline system, and baseline value, the weapon system portfolio selection approach with regard to a baseline value is proposed. Starting from the two core parts of the approach, selection strategy analysis, and weapon systems ranking, the strategy analysis of weapon system selection under a single objective and multiple objectives were performed. Subsequently, the interval number theory was employed to extend the VIKOR method to the E-VIKOR method, with a linear programming model as the weighting method under uncertainties to rank the candidate weapon systems. Finally, a case with three different ranking results of candidate weapon systems are studied under different weighting schemes. Based on the ranking results, baseline value, and selection strategy, the weapon system portfolio refining was developed. [ABSTRACT FROM AUTHOR]

As an important tool for information fusion, evidence theory has been widely applied in many areas. Unlike spatial evidence combination, temporal evidence combination is sequential and dynamic, which calls for new temporal evidence combination rule. In this paper, temporal evidence is combined based on relative reliability evaluation and evidence discounting. We first recall the method of evidence reliability evaluation based on intuitionistic fuzzy multiple criteria decision making (ERE-IFMCDM). Based on ERE-IFMCDM method, relative reliability factors of evidence sources in neighboring time nodes can be obtained. Then, according to evidence discounting and Dempster's combination rule, a method for temporal evidence combination based on the relative reliability factor is developed. Numerical examples and simulation demonstrate that the proposed method is time sensitive, which can reflect the dynamic feature of temporal information fusion. Moreover, the proposed method can deal with conflict in temporal fusion much better, which can enhance the anti-interference performance of the fusion system. The fusion framework proposed in this paper provides a dynamic perspective of the fusion of temporal uncertain information. It is helpful for the structural design of intelligent fusion system based on multiple temporal-spatial information. [ABSTRACT FROM AUTHOR]

Due to more and more awareness of environmental conservancy and sustainable development, automated container terminals (ACTs) have gained increasing attention and become popular worldwide. However, there is a crucial challenge in implementing ACTs, i.e., how to develop terminal layout planning (including terminal layout and facility selection) to realize performance goals. Focusing on this challenge, the aim of this paper is to resolve how to appropriately select terminal layout and facilities in ACTs with respect to various performance criteria. Several research limitations can be gathered from the existing literature: (i) Most of the existing works mainly develop optimization technologies to tackle layout optimization and facility comparison issues within a few types of terminal layout and facilities, yet few researchers have proposed evaluation methods for making selection decisions on terminal planning. (ii) Only a few types of criteria are considered, and requirements and preferences of different participants are ignored, not to say realizing an optimal compromise by employing comprehensive criteria weights in evaluation processes. (iii) New technological improvement in ACTs has not received much attention, and there still lacks a method to precisely quantify the concerned criteria. Accordingly, this paper introduces a criteria system comprising economic, environment, social, resource and performance groups and develops an agent-based simulation method considering different terminal layout and facilities to determine criteria values. A multi-criteria decision making (MCDM) framework is proposed for making the most reasonable and appropriate selection decisions, which employs a variation coefficient weighted method to calculate comprehensive criteria weights, which integrate subjective and objective weights obtained from AHP (analytic hierarchy process) method and entropy weight method. To overcome rank reversal problems in the TOPSIS (technique for order preference by similarity to an ideal solution) method, a new improved rank reversal robust TOPSIS method, called NR-TOPSIS, is utilized to rank and select alternatives, which is ranking stable in the experiments performed. An illustrative application on a container terminal in China demonstrates the effectiveness of the proposed MCDM method and sensitivity and comparative analysis verifies the robustness and stability of the method. [ABSTRACT FROM AUTHOR]

In many real-world decision-making tasks, multi-agent need to learn collaboration in a high-dimensional complex action space, rather than just a single discrete action space. Recently, value decomposition learning methods such as QMIX have emerged as a promising approaches for collaborative multi-agent tasks. However, most of the value decomposition algorithms can only be used in discrete action space, which would limit their practicability. To address the limitation, we propose a novel algorithm called Multi-Agent Sequential Q-Networks (MASQN), which can be applied to the multi-agent domains with continuous, multidiscrete or hybrid action spaces. The proposed algorithm is based on the structure of centralized training with decentralized execution (CTDE). The decentralized actors ensure adaptability to different action spaces by utilizing action space discretization and sequential models, and the centralized critic utilizes the value decomposition architecture to guide effective updates of the policy parameters for each agent. We also give the convergence of joint policy from the perspective of policy iteration, by combining it with the CTDE structure and the constraint of the Individual Global Max (IGM) condition. Finally, we evaluate the MASQN algorithm on two benchmark environments: MAMuJoCo and Hybrid Predator–Prey. The empirical results show that MASQN out performs the state-of-the-art performance on three different action spaces. • This MARL algorithm can be directly used in continuous action spaces. • This MARL algorithm can be directly used in multi-discrete action spaces. • This MARL algorithm can be directly used in hybrid action spaces. • We analyzed the convergence of this algorithm in our paper. [ABSTRACT FROM AUTHOR]

The development of data science and sensing technologies provides opportunities to mitigate uncertainty in hybrid manufacturing–remanufacturing systems, and drives manufacturers to invest in intelligent manufacturing (IM). This paper considers the adoption of IM to mitigate classification errors in the end-of-use (EoU) products' acquisition while improving the operational efficiency of manufacturing–remanufacturing systems. By constructing game models, we jointly investigate the investment, pricing, production and order quantities, and collection decisions in a closed-loop supply chain. Interestingly, we find that IM does not always increase manufacturers' enthusiasm to engage in remanufacturing, firms' profits or consumer surplus. It does not always reduce environmental impact either. Analytical results show that IM reduces manufacturers' enthusiasm to engage in remanufacturing if the initial core misclassification rate is low. Furthermore, we identify key factors that impact the firms' profits, consumer surplus, and carbon emissions. For a manufacturer producing high-cost new products, investing in IM hurts its profits when the remanufacturing cost savings due to intelligent manufacturing is small and the manufacturing benefit due to IM is not significant. Moreover, investing in IM reduces consumer surplus if the cost of new products is high, and the IM does not lead to significant remanufacturing cost saving and manufacturing benefit increase. The results reveal that investing in IM is preferred from the environmental point of view if the IM-induced manufacturing cost saving is small. Our research provides management insights into the intelligent transformation of manufacturers with hybrid manufacturing–remanufacturing systems, as well as policymakers concerned with value creation for customers and environmental impacts. • We incorporate discrete probability distributions of misclassification into models. • Assume investing in IM mitigates misclassification while reducing operational costs. • We investigate optimal strategies in both scenarios respectively with and without IM. • We study the impacts of IM on firms profits, consumer surplus and environmental. • Lower misclassification rates reduce the willingness to engage in remanufacturing. [ABSTRACT FROM AUTHOR]

Sequential decision making is an important part of robotic problems that is receiving unprecedented attention from both academia and industry. Recently, Deep Reinforcement Learning (DRL) has shown its promising capabilities in decision making problems. However, traditional DRL algorithms directly operate in the space of low-level actions, when it is applied in the domain of robotics, it can easily result in an exponential growth of computational complexity and suffer from the "curse of dimensionality", becoming less efficient as the dimensionality of the environment increases. To address this issue, a novel DRL hyper-heuristic approach is proposed in this paper. The proposed approach is tailored to align with a problem taken from a real-world competition by taking advantage of well-developed low-level heuristic actions in order to narrow the search space and speed up the convergence. This fundamental contribution is a significant step forward from earlier approaches that directly exploit the entire low-level action domain. A state augmentation scheme and a novel reward design are utilized to further improve the performance of the proposed method. Moreover, a Real-to-Sim based training framework is developed to reduce the cost of acquiring real-time data and improve the robustness of agent's decision-making model. Numerous experimental results demonstrate our proposed method can achieve notable performance gains compared to both competitive DRL baselines and heuristic approaches of the same problem in both known environment and previously unseen scenarios. • We propose a novel DRL hyper-heuristic approach for robot decision-making problems. • Our method can narrow the search space and accelerate convergence compared with SOTA. • A Real-to-Sim training framework is developed to improve the robustness of model. [ABSTRACT FROM AUTHOR]

This paper introduces a novel Integrated Interval Rough Pivot Pairwise Relative Criteria Importance Assessment (IRN PIPRECIA) model combined with Interval Rough Combined Compromise Solution (IRN CoCoSo), marking a significant advancement in sustainable traffic flow management for commercial vehicles. This innovative merger is a first in literature, methodologically enhancing the evaluation of road sections based on critical parameters including passenger car equivalent (PCE) 85%, AADT, road conditions, and accident data. Our model systematically fills the research gap in holistic traffic performance analysis, providing a unique tool for prioritizing road safety and efficiency. The key scientific contribution is the model's ability to integrate causal and consequential traffic factors into a single framework, offering a novel multi-criteria decision-making (MCDM) approach. Results, validated through various verification models, show the integrated model's effectiveness in real-world scenarios, confirming its robustness and stability. With strong engineering application potential, our work supports urban planners and traffic managers in making informed, sustainable decisions. The model extends beyond traditional traffic analysis, promising a shift towards more adaptive, data-driven infrastructure management. Future research will aim to refine the criteria basis and explore real-time decision-making through advanced MCDM applications. [ABSTRACT FROM AUTHOR]

Multi-step ahead carbon price forecasting is crucial for making informed decisions in production, operations, and investments. However, many forecasting methods fail to incorporate intermediate time series data due to its unavailability during testing. This study leverages such data within the Learning using Privileged Information (LUPI) framework. Under the guidance of two prevalent mechanisms in LUPI, we develop two forecasting models—a privileged information-enhanced Least Squares Support Vector Machine (LSSVM+) and a Gated Recurrent Unit (GRU) network equipped with generalized distillation. Both methods are rigorously assessed using European Union Allowance (EUA) carbon future prices in both univariate and multivariate time series modeling scenarios. The outcomes emphasize the superiority of LUPI-based methods in boosting multi-step carbon price forecasting, leading to more strategic decisions in carbon markets. • The paper embeds learning using privileged information into carbon price forecasting. • LSSVM+ is developed for accurate carbon price movement prediction. • GRU network with generalized distillation is used to forecast carbon price movement. • Empirical results verify model superiority in univariate and multivariate scenarios. [ABSTRACT FROM AUTHOR]

As Artificial Intelligence (AI) systems become increasingly complex, ensuring their decisions are transparent and understandable to users has become paramount. This paper explores the integration of Case-Based Reasoning (CBR) with Explainable Artificial Intelligence (XAI) through a real-world example, which presents an innovative CBR-driven XAI platform. This study investigates how CBR, a method that solves new problems based on the solutions of similar past problems, can be harnessed to enhance the explainability of AI systems. Though the literature has few works on the synergy between CBR and XAI, exploring the principles for developing a CBR-driven XAI platform is necessary. This exploration outlines the key features and functionalities, examines the alignment of CBR principles with XAI goals to make AI reasoning more transparent to users, and discusses methodological strategies for integrating CBR into XAI frameworks. Through a case study of our CBR-driven XAI platform, iSee: Intelligent Sharing of Explanation Experience, we demonstrate the practical application of these principles, highlighting the enhancement of system transparency and user trust. The platform elucidates the decision-making processes of AI models and adapts to provide explanations tailored to diverse user needs. Our findings emphasize the importance of interdisciplinary approaches in AI research and the significant role CBR can play in advancing the goals of XAI. • Investigate CBR's role in enhancing AI explainability. • Principles for developing CBR-XAI platforms are highlighted. • Novel integration of CBR with XAI is showcased through a CBR-driven XAI platform. [ABSTRACT FROM AUTHOR]

With the advances in social media and e-democracy technologies, large-scale decision making (LSDM) problems with trust relationships demands effective consensus-reaching processes. Existing literature has identified a need for improvement in the updating method of trust relationships and the recommendation-feedback mechanism, while the overall effect of trust relationships on consensus reaching remains unexplored and not quantitatively examined. This study proposes a novel interactive dynamic trust-lead consensus reaching process (IDT-CRP) based model to address these challenges. The model integrates a dynamic Trust network updating (DTNU) process and a trust-lead and efficiency-driven recommendation-feedback (TERF) process. In the DTNU process, secondary trust is defined to represent the trust derived from decision makers' modification behaviors in each iteration. In the TERF process, a novel trust-lead and efficiency-driven recommendation-feedback mechanism is proposed, in which two different decision scenarios are provided. Furthermore, the role of trust relationships in enhancing group consensus level is analyzed, revealing its significant impact while identifying an upper limit. Novelties of this paper include 1) the improved trust updating algorithm which derived secondary trust from modification behaviors instead of opinion similarities; 2) the improved recommendation-feedback mechanism with different trust utilization scenarios while retaining minority opinions; 3) the novel IDT-CRP model based on the interactive mechanism between trust relationship and opinion evolution. A series of experiments are implemented, validating that the proposed model is of good feasibility, effectiveness and robustness, and can be applied in future LSDM research. [ABSTRACT FROM AUTHOR]

With the advancement of prediction methods in the field of artificial intelligence, accurate price predictions can effectively support financial portfolio selection. This paper proposes an intelligent stock portfolio selection method based on a prediction neural network, incorporating signal processing and hyperparameter optimization techniques. The method is divided into two key stages: stock price prediction and portfolio selection. In the first stage, we apply Savitzky–Golay filtering to denoise price data and reveal its patterns, and optimize the hyperparameters of the long short-term memory network using the sparrow search algorithm to achieve high-precision stock price predictions. In the second stage, we use the mean-Conditional Value-at-Risk (mean-CVaR) model to select the optimal stock allocation, considering factors such as potential returns, prediction accuracy, and growth rate. Numerical comparisons based on multiple public financial datasets demonstrate that the proposed two-stage method significantly outperforms seven benchmark methods. Specifically, on the Shanghai and Shenzhen 300 (CSI 300) Index dataset, the proposed method achieves a determination coefficient of 0.9980 and an accuracy rate of 97.05%. Additionally, its cumulative returns reach 9.38%, 8.63%, and 7.54% at different confidence levels. [Display omitted] • An AI-powered technique for stock price prediction via filtering and optimization. • Two-stage portfolio decision process with the mean-conditional value-at-risk model. • Achieved high predictive ability: R-squared of 0.9980 and Accuracy Rate of 97.05%. • Outperforms traditional investment strategies: a maximal cumulative return of 9.3%. • Performance tops, compared with 7 benchmark strategies on open financial datasets. [ABSTRACT FROM AUTHOR]

Interval additive preference relation (IAPR) is an efficient tool for describing the uncertain pairwise comparisons between alternatives in group decision-making (GDM). Existing GDM methods with IAPRs view the preference value as a whole and establish concrete conflict/consensus mechanisms for fusing individual opinions while neglecting the stochastic preference and aggregation modes to meet the different demands of decision. Thus, to reflect the stochastic characteristic in both preference representation and group preference aggregation, this paper presents a novel two-stage stochastic preference analysis on GDM with IAPRs. In first stage, the method involves extracting stochastic additive preference relations (SAPRs) from the IAPRs. A preference relations matrix is then established using a logarithmic least squares model applied to the SAPRs. In second stage, the weighted preference matrix is synthesized by combining the preference relations matrix with the decision makers' weight space. This leads to the computation of positive and negative ideal preference solutions, as well as positive and negative preference distances. Subsequently, the relative preference closeness is calculated based on these distances. Relative ranking results are determined by an algorithm evaluating pairwise comparisons among alternatives, resulting in transitional preference rankings. The second stage involve deriving ranking acceptance indices and ranking confidence degrees through a continuous extraction process, facilitating the establishment of the ultimate best preference rankings. The proposed method's effectiveness and validity are demonstrated by numerical examples and Monte Carlo simulations. • Introduce a two-stage stochastic preference analysis for GDM with IAPRs. • Apply the logarithmic least squares method to the stochastic APRs. • Obtain the ranking acceptability indices and preference confidence degrees with a continuous extraction process. • Demonstrate the effectiveness and validity of the proposed method using Monte Carlo simulations. [ABSTRACT FROM AUTHOR]

Data envelopment analysis (DEA) is a frontier analysis procedure for evaluating the relative performance of decision making units (DMUs) with multiple inputs and multiple outputs. To improve its discrimination power, an important extension is proposed as cross-efficiency, which uses peer DMUs’ optimal relative weights to evaluate the relative performance. However, the existing cross-efficiency methods show an inconsistent and unbalanced evaluation standard, since each DMU might determine a different total (or mean) efficiency value across all DMUs. The different values imply that the DMUs that have assigned larger cross-efficiency scores will have a larger effect in aggregating the ultimate cross-efficiency scores and different DMUs’ effects are unbalanced in cross-efficiency methods. In this paper, we will deal with this unbalanced cross-efficiency evaluation problem. To this end, we first suggest a practical adjustment measure to rectify the traditional cross-efficiency, which will provide a common evaluation standard for all DMUs and make each DMU dispatch an identical total efficiency score across all DMUs. Further, we propose a game-like iterative procedure to obtain the optimal balanced cross-efficiency. Finally, we present both a numerical example and an empirical study derived from the literature and a real-world problem to demonstrate the usefulness and efficacy of the new balanced cross-efficiency evaluation approach. The work presented in this paper can extend the traditional cross-efficiency approaches to situations involving unbalanced evaluation standards, and make the evaluation results more practical significance. [ABSTRACT FROM AUTHOR]

Performance measurement is one of the most essential real life decision making problems faces several criteria and different types of uncertainty and vagueness. Human expertise is mandatory for such situation although it is not enough to overcome such complicated cases. Expert systems which can sense, analyze and incorporate all details of real life decision making problems are required. In this paper an expert system is proposed in form of an uncertain Data Envelopment Analysis (DEA) approach. More formally a mixed fuzzy-robust uncertainty is addressed in the proposed DEA. Two scenario-based robust DEA models under Constant Return to Scale (CRS) and Variable Return to Scale (VRS) conditions have been proposed. Since the observed values for the inputs and outputs of Decision Making Units (DMUs) in each scenario may be ambiguous or vague, fuzzy DEA models corresponding to each of the robust scenarios are developed. So, this paper proposes four fuzzy-robust DEA models, which simultaneously maintain the advantage of each of the fuzzy and robust approaches, and at same time calculate the upper and lower bounds of the efficiency scores of DMUs under CRS and VRS conditions. Finally, to evaluate the validity and applicability of the proposed models, two numerical examples and a real case study in Small and Medium-sized Enterprises (SMEs) are presented and discussed. [ABSTRACT FROM AUTHOR]

This paper presents a complete review of computer vision algorithms and vision-based intelligent applications, that are developed in the field of the Unmanned Aerial Vehicles (UAVs) in the latest decade. During this time, the evolution of relevant technologies for UAVs; such as component miniaturization, the increase of computational capabilities, and the evolution of computer vision techniques have allowed an important advance in the development of UAVs technologies and applications. Particularly, computer vision technologies integrated in UAVs allow to develop cutting-edge technologies to cope with aerial perception difficulties; such as visual navigation algorithms, obstacle detection and avoidance and aerial decision-making. All these expert technologies have developed a wide spectrum of application for UAVs, beyond the classic military and defense purposes. Unmanned Aerial Vehicles and Computer Vision are common topics in expert systems, so thanks to the recent advances in perception technologies, modern intelligent applications are developed to enhance autonomous UAV positioning, or automatic algorithms to avoid aerial collisions, among others. Then, the presented survey is based on artificial perception applications that represent important advances in the latest years in the expert system field related to the Unmanned Aerial Vehicles. In this paper, the most significant advances in this field are presented, able to solve fundamental technical limitations; such as visual odometry, obstacle detection, mapping and localization, et cetera. Besides, they have been analyzed based on their capabilities and potential utility. Moreover, the applications and UAVs are divided and categorized according to different criteria. [ABSTRACT FROM AUTHOR]

In literature, many studies try to analyze healthcare usage and generated decision support systems. In this paper, the aim is to generate a decision support system for insurance companies in the United States according to the federal dataset. The data set contains variables from the United States Healthcare Administration formal data group and they are; group of age, group of healthcare insurance, group of nationality, Corporate Social Responsibility (CSR) usage, and Advance Premium Tax Credit (APTC) usage. The best model for each state in the United States is determined with the dataset from 2014 November to 2015 February. In this study, various statistical models are attempted to generate independent and optimal models for each state. In the model selection process, three different metrics are used. Accuracy rate, f1score and precision score are used in the model selection process and models are compared to each other according to these three metrics for each state uniquely. Consequently, the proposed model is used with a decision support scheme to support customer service workers in healthcare insurance companies. The decision support system under consideration can facilitate a substantial decrease in the duration of interactions between customer service representatives and clients. Additionally, this system can generate more logically sound and efficient customer offers. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to propose a novel hybrid framework for evaluating and benchmarking trustworthy artificial intelligence (AI) applications in healthcare by using multi-criteria decision-making (MCDM) techniques under a new fuzzy environment. To develop such a framework, a new decision matrix has been built, and then integrated with q-ROF2TL-FWZIC (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Fuzzy-Weighted Zero-Inconsistency) and q-ROF2TL-CODAS (q‐Rung Orthopair Fuzzy 2‐Tuple Linguistic Combinative Distance-Based Assessment). In this integration, q-ROF2TL-FWZIC is utilized for assigning the weights of evaluation attributes of trustworthy AI, while q-ROF2TL-CODAS is employed for benchmarking trustworthy AI applications. Findings show that the q-ROF2TL-FWZIC method effectively weights the evaluation attributes. The transparency attribute receives the highest importance weight (0.173566825), whereas the human agency and oversight criterion has the lowest weight (0.105741901). The remaining attributes are distributed in between. Moreover, alternative_4 receives the highest rank order (score of 7.370410417), while alternative_13 receives the lowest rank order (score of −4.759794397). To evaluate the validity of the proposed framework, systematic ranking and sensitivity analysis assessments were employed. [ABSTRACT FROM AUTHOR]

The paper proposes a new information set of multiple interacting fuzzy linguistic set(MIFLS) to describe the interaction of uncertain linguistic information. The property of the MIFLS and its characteristic of processing consensus information are also discussed, the introduction of MIFLS provides a new perspective to improve the consistency of decision makers' evaluation information, and the application of MIFLS can improve the accuracy of decision making. Furthermore, we propose an optimization consistency information model based on the MIFLS and TOPSIS method, an example of emergency decision plan is used to illustrate the applicability and efficiency of the proposed model. The optimal consistency adjustment is not only the highly consistent evaluation information, but also the minimal difference between the original information and the adjusted information. The proposed MIFLS is fit for dealing with the interaction of decision information, which means MIFLS is one of the effective tools to processing the interaction of uncertain fuzzy set and dealing with the emergency decision making problem. More important, the MIFLS not only provides a way to quantify the correlation of multi-attribute information, but also provides a method how to improve the consistency of decision-maker information. [ABSTRACT FROM AUTHOR]

Aiming at three issues in large-scale group decision-making (LSGDM): risk attitudes, dynamically changing roles, and dynamically adjust evaluation information of decision makers (DMs), this paper constructs an LSGDM model by considering risk attitudes and dynamically changing roles. Firstly, we introduce the concept of risk attitudes and the method of identification and creatively combine it with Decision Making Trial and Evaluation Laboratory to divide DMs into four categories including core DMs, driven DMs, independent DMs, and followers. Considering the dynamically changing roles of DMs, an adjustment method for evaluation information is proposed. Then a double recognition mechanism is developed to determine whether the consensus has been reached. Moreover, we construct a method to determine the weights of DMs. Based on the above research, an LSGDM model based on probabilistic double hierarchy linguistic term set is constructed and applied to a practical problem of the selection of emergency medical distribution schemes. Finally, a sensitivity analysis is performed to examine the impact of different adjustment coefficients on the results, and the validity of our method is further illustrated by comparative analysis. [ABSTRACT FROM AUTHOR]

In multiple attribute decision making (MADM) problems, when belief distributions (BDs) are used to express the preference information of decision-maker, obtaining the attribute weights using the difference among the assessments is a complex but interesting challenge. This paper introduces the weighted similarity measure of belief distribution to distinguish the alternatives to the greatest extent. After proposing the concepts of similarity measure and weighted similarity measure for two belief distribution vectors, an optimization model is developed to generate the attribute weights. After that, the comparison rules and the ideal belief distributions are defined, then the regret theory is extended to rank alternatives with the consideration of the psychological behavior of the decision-maker. The main characteristic of the proposed method involves: (1) An optimization model based on weighted similarity measure is proposed to derive attribute weights in BD environment; (2) The psychological behavior is taken into account to assist the decision-maker in avoiding regret; Finally, the applicability and validity of the proposed method are demonstrated by a medical example in which doctors could make the diagnosis with minimal regret. [ABSTRACT FROM AUTHOR]

In the Big Data era, context-aware mobile recommender systems are crucial in assisting citizens and tourists in making informed decisions, providing a suitable way for users to find the relevant data. These systems should be proactive, able to detect the ideal time and location to provide recommendations for a specific item or activity. To accomplish this, push-based recommender systems can be employed, utilizing context rules to determine when a recommendation should be initiated. However, there is very limited reported experience in defining and implementing such systems and a complete generic solution that adapts flexibly to the preferences of users and protects their privacy is still missing. In this paper, we present a novel approach where appropriate types of recommendations are provided automatically, without the need for user input. Our proposal allows users to easily activate, deactivate, customize, and create rules for improved personalization. Additionally, the module that, based on the context, decides the types of recommendations required is executed on the user's mobile device, reducing wireless communication and safeguarding the user's privacy, as context data are evaluated locally. To illustrate the approach, we have developed R-Rules, a prototype for Android devices focused on the triggering of recommendation rules, which provides a friendly user interface that facilitates user personalization. We have evaluated various technological options and demonstrated the feasibility, performance, and scalability of the proposal, as well as its suitability to users' needs. • Generic architecture for mobile push-based recommendations. • Decision module executing on the user device: privacy and wireless savings. • Flexible user-defined rules to decide when to fire a specific type of recommendation. • Android prototype and extensive experimental evaluation. [ABSTRACT FROM AUTHOR]

Intelligent computing is an independent algorithm system that imitates the laws and principles of nature's biological chain to design and solve practical problems in modern society, it can help people better handle some big data problems through artificial intelligence technology and different cognitive characteristics. Especially, when dealing with group decision problems, it is often considered as human behavior based on cognitive computing. The q-rung orthopair fuzzy set (q-ROFS) is not only a generalization of Pythagorean fuzzy set (PFS), but also can deal with the decision problem of multi-source information in a wider area. In this paper, we first give the detailed explanations of the relationship between the q-ROFS and q-rung orthopair fuzzy number (q-ROFN)by an example, and itis pointed out that there are some defects in the existing two ranking methods for the Pythagorean fuzzy numbers (PFNs) and intuitionistic fuzzy numbers (IFNs) through counterexamples, and then all q-ROFNs are mapped into the unit triangle of the first quadrant through q-compression mapping, and the concept of distance factor for q-ROFNs is proposed by Euclidean distance between each q-ROFN and the maximum element (1,0).Secondly, a novel inverse score function for q-ROFNs are put forward by the distance factor and hesitancy degree, and the monotonicity of the inverse score function are discussed. Finally, a new ranking criterion of q-ROFNs is given by the new score formula, and then the rationality of the ranking criterion is proved, and through the method of solving the conditional extreme value of multivariate function (Lagrange multiplier method), there is indeed an extreme value point (corresponding to the maximum q-ROFN) on υ = μ. Besides, a q-rung orthopair fuzzy decision-making method for multi-source information is given by the new ranking criterion and aggregation operations, and the comparison of examples shows that the inverse score function and decision-making method do have many advantages in the q-ROFN environment. [ABSTRACT FROM AUTHOR]

• Proposing a physical asset criticality analysis decision model based on BIA. • Risk Appetite and physical asset continuity measures are related. • Defining sub-criteria for impact and likelihood estimation in criticality analysis. The paper tackles the issue of managing physical assets in asset-intensive industries, where continuity plans are crucial for business operations during disruptions. However, resource constraints often hinder companies from implementing these plans for all their assets. Moreover, efforts to maximize resource utilization efficiency and minimize environmental impacts are limited to isolated disciplines, necessitating an integrated approach across natural, social, and technological systems. To address these challenges, this study proposes a framework based on a hybrid fuzzy BWM-TOPSIS methodology for identifying critical physical assets. The framework incorporates Business Impact Analysis (BIA) to determine essential products, a physical asset risk assessment matrix (PARAM) to assess critical assets based on likelihood and impact, and a mathematical model to estimate asset continuity parameters. The framework is applied to a petrochemical company in a case study, offering a guide for estimating the probability and impact of physical asset failure while accounting for inherent uncertainties. By adopting this framework, companies can efficiently manage their physical assets, prioritize continuity plans, and address resource constraints and uncertainties. This research contributes to practical tools and approaches that assist industries in sustaining operations and ensuring business continuity during disruptions. [ABSTRACT FROM AUTHOR]

This paper presents a framework to build home automation systems reactive to voice for improved comfort and autonomy at home. The focus of this paper is on the context-aware decision process which must reason from uncertain facts inferred from real sensor data. This framework for building context aware systems uses a hierarchical knowledge model so that different inference modules can communicate and reason with same concepts and relations. The context-aware decision module is based on a Markov Logic Network, a recent approach which make it possible to benefit from formal logical representation and to model uncertainty of this knowledge. In this work, uncertainty of the decision model has been learned from data. Although some expert systems are able to deal with uncertainty, the Markov Logic Network approach brings a unified theory for dealing with logical entailment, uncertainty and missing data. Moreover, the ability to use a priori knowledge and to learn weights and structure from data make this model appealing to address the challenge of adaptation of expert systems to new applications. Finally, the framework has been implemented in an on-line system which has been evaluated in a real smart home with real naive users. Results of the experiment show the interest of context-aware decision making and the advantages of a statistical relational model for the framework. [ABSTRACT FROM AUTHOR]

Rare events, especially those that could potentially negatively impact society, often require humans’ decision-making responses. Detecting rare events can be viewed as a prediction task in data mining and machine learning communities. As these events are rarely observed in daily life, the prediction task suffers from a lack of balanced data. In this paper, we provide an in depth review of rare event detection from an imbalanced learning perspective. Five hundred and seventeen related papers that have been published in the past decade were collected for the study. The initial statistics suggested that rare events detection and imbalanced learning are concerned across a wide range of research areas from management science to engineering. We reviewed all collected papers from both a technical and a practical point of view. Modeling methods discussed include techniques such as data preprocessing, classification algorithms and model evaluation. For applications, we first provide a comprehensive taxonomy of the existing application domains of imbalanced learning, and then we detail the applications for each category. Finally, some suggestions from the reviewed papers are incorporated with our experiences and judgments to offer further research directions for the imbalanced learning and rare event detection fields. [ABSTRACT FROM AUTHOR]

This paper introduces an extension of a well-known Multiple Criteria Decision Aiding method, namely the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Most of the TOPSIS applications assume that preferences are monotonic for each evaluation criterion and that qualitative scales are converted into quantitative ones before the method is applied. However, both assumptions have been subject of discussion and criticism in the literature. To this solution, this paper introduces a normalization technique based on simulations that permit taking into account non-monotonic preferences as well as qualitative criteria. An additional novelty lies in the integration of the Multiple Criteria Hierarchy Process, which extends the applicability of the method to problems in which criteria are hierarchically structured. To deal with robustness concerns, the Stochastic Multicriteria Acceptability Analysis will be used in the new proposal, giving information in statistical terms on the goodness of the considered alternatives. The new method has been applied to evaluate a set of banks listed in the LSE's FTSE350 Index. [ABSTRACT FROM AUTHOR]

• The paper explores a new family of methods for heterogeneous MAGDM. • Diversified opinions are unified into random values to retain structural feature. • The SPM is developed to measure the superiority probability between alternatives. • Probabilistic rank is introduced to fit with heterogeneous decision environment. Heterogeneous multi-attribute group decision making (heterogeneous MAGDM) is a hot topic in complicated decision-making environment. This paper proposes a new approach for heterogeneous MAGDM problems, including three main research points. Firstly, different from the commonly used unified transformation process, the study provides a randomization method to transform heterogeneous opinions into random values, which can keep the structural features of the original data as much as possible. Secondly, the random values are integrated into superiority-probability-based pairwise comparison matrix (SPM) by combining stochastic simulation and multi-attribute decision making methods. Then, a maximization-similarity-based consensus approach is developed to obtain the collective SPM. The collective SPM can not only objectively present the superiority relationship between any two alternatives, but also balance the opinions of multiple experts. Thirdly, a new type of ranking, probabilistic rank order, is proposed, which is more consistent with the heterogeneous and uncertain characteristics of the decision-making opinions. At last, an example regarding missile weapon system selection is chosen from previous studies to illustrate the validity of the proposed methods. Another example about the predictive evaluation of city development potential is constructed to further illustrate the practical application prospects of the research methods. [ABSTRACT FROM AUTHOR]

• The paper explores a new family of methods for heterogeneous MAGDM. • Diversified opinions are unified into random values to retain structural feature. • The SPM is developed to measure the superiority probability between alternatives. • Probabilistic rank is introduced to fit with heterogeneous decision environment. Heterogeneous multi-attribute group decision making (heterogeneous MAGDM) is a hot topic in complicated decision-making environment. This paper proposes a new approach for heterogeneous MAGDM problems, including three main research points. Firstly, different from the commonly used unified transformation process, the study provides a randomization method to transform heterogeneous opinions into random values, which can keep the structural features of the original data as much as possible. Secondly, the random values are integrated into superiority-probability-based pairwise comparison matrix (SPM) by combining stochastic simulation and multi-attribute decision making methods. Then, a maximization-similarity-based consensus approach is developed to obtain the collective SPM. The collective SPM can not only objectively present the superiority relationship between any two alternatives, but also balance the opinions of multiple experts. Thirdly, a new type of ranking, probabilistic rank order, is proposed, which is more consistent with the heterogeneous and uncertain characteristics of the decision-making opinions. At last, an example regarding missile weapon system selection is chosen from previous studies to illustrate the validity of the proposed methods. Another example about the predictive evaluation of city development potential is constructed to further illustrate the practical application prospects of the research methods. [ABSTRACT FROM AUTHOR]

Multi-objective optimization in the intuitionistic fuzzy environment is the process of finding a Pareto-optimal solution that simultaneously maximizes the degree of satisfaction and minimizes the degree of dissatisfaction of an intuitionistic fuzzy decision. In this paper, a new method for solving multi-objective programming problems is developed that unlike other methods in the literature, provides compromise solutions satisfying both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. This method combines the advantages of the intuitionistic fuzzy sets concept, goal programming, and interactive procedures, and supports the decision maker in the process of solving programming problems with crisp, fuzzy, or intuitionistic fuzzy objectives and constraints. A characteristic of the proposed method is that it provides a well-structured approach for determining satisfaction and the dissatisfaction degrees that efficiently uses the concepts of violation for both objective functions and constraints. Another feature of the proposed method comes from its continuous interaction with the decision maker. In this situation, through adjusting the problem's parameters, the decision maker would have the ability of revisiting the membership and non-membership functions. Therefore, despite the lack of information at the beginning of the solving process, a compromise solution that satisfies the decision maker's preferences can be obtained. A further feature of the proposed method is the introduction of a new two-step goal programming approach for determining the compromise solutions to multi-objective problems. This approach ensures that the compromise solution obtained during each iterative step satisfies both the conditions of intuitionistic fuzzy efficiency and Pareto-optimality. The application of the proposed model is also discussed in this paper. [ABSTRACT FROM AUTHOR]

Inventory management constitutes a fundamental decision-making problem, especially in systems which must guarantee high availability levels. In complex multi-echelon networks, in case of shortage at a location, resupplying from a near location on the same echelon rather than from the original supplier at the upper echelon, would be a potential faster and then more profitable policy. A wide interest in literature shows the importance of this policy, i.e. the lateral transhipment (LTR), as a means to reduce the inventory costs. This paper deals with unidirectional LTR, which often represent a reasonable policy in scenarios where backorders have different effects on the system. Based on METRIC, this paper defines a system-approach model for determining the stock levels of repairable items in a complex network, by a genetic algorithm optimization process. The model considers non-zero maintenance time for each item and different skills of maintenance centres, in a multi-echelon single-indenture system, with unidirectional LTR allowed. The expert system developed in the paper assists the decision maker to define the inventory levels for the maintenance sites across the system, taking advantage of the LTR to reduce costs and enhance availability. A case study of a European airline shows the relevance of the developed expert system, also considering its reproducibility to face different industrial contexts. [ABSTRACT FROM AUTHOR]

As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by [Vaidya, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1–29.]. To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total). In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP’s variant); (iv) Chang’s extent analysis method, which is used for FPCMs’ weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature). [ABSTRACT FROM AUTHOR]

Timely knowledge sharing in disaster management (DM) is clearly vital, but it is remains challenging. Roles involved in DM processes often cut across many organizational boundaries and are dynamic. Knowledge involved is enormous and diverse. It includes information related to varieties of disasters, roles descriptions, plans and operations. Alas, practices may also vary across different regions and authorities. This paper makes a crucial contribution to address the knowledge sharing challenge by providing a knowledge based systems approach to facilitate structuring, storing and reusing DM knowledge. The contribution of this paper is three folds: Firstly, it presents a metamodel-based architecture suitable for various distributed knowledge sharing settings; Secondly, it presents an actual implementation of such a system, the Disaster Management Knowledge Repository (DMKR1.0). DMKR facilitates collaboration and DM knowledge sharing using a tailored DM language. This offers a flexible structure to allow the storage and retrieval not only of observed and measured data, but also interpretative and inferred information of the disaster management knowledge. Thirdly, the paper provides disaster management exemplars of how DMKR users can easily instantiate DM models to communicate and to generalize their knowledge for the benefit of sharing it within their community. This presents a compelling evidence of the soundness and the effectiveness of the overall approach to DM knowledge sharing. [ABSTRACT FROM AUTHOR]

Integrated energy system (IES) plays a vital role in achieving energy revolution and the goals of carbon peak and carbon neutrality. The optimal planning of IES is of great significance for improving the overall efficiency of the system and promoting its sustainable development. Focusing on this issue, this paper proposes a planning framework integrating multi-objective optimization with fuzzy multi-criteria decision making (MCDM). In this framework, IES planning is modeled as a multi-objective optimization problem that, for the first time, simultaneously minimizes energy consumption, carbon emissions, and economic costs. Thereafter, the optimization problem is solved by a multi-objective state transition algorithm based on decomposition (MOSTA/D), which generates a Pareto set that realizes multiple conflicting objective tradeoffs. Furthermore, to comprehensively evaluate the Pareto optimal solutions, an evaluation criteria system is established from various perspectives, and a novel MCDM approach is proposed. This approach combines the analytic network process-entropy weighting technique, which takes into account the correlation between criteria as well as subjective preference and objective information, with fuzzy TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) for scientifically ranking and selecting solutions under uncertainty. The simulation results of an IES planning case study demonstrate that the optimal scheme determined by the proposed method achieves the best overall benefit for IES, with significant annual economic cost savings, primary energy savings, and carbon dioxide emission reduction rates of 2.27%, 40.36%, and 56.25%, respectively, proving the effectiveness and superiority of the proposed method. [ABSTRACT FROM AUTHOR]

Atanassov's intuitionistic fuzzy sets play a vital role in handling vagueness and uncertainty successfully as they associate an additional non-membership degree to each element of the universal set. Interval-valued intuitionistic fuzzy (IVIF) sets are more generalized quantities in this direction as they use intervals (⊂ [ 0 , 1 ]) to define the membership and non-membership grades of each element of the set. However, without defining basic arithmetic operations on the IVIF values/sets, the study is incomplete. This paper broadly examines the division and subtraction operations over any arbitrary IVIF values/sets using the concept of Hamming distance between them. A deterministic linear optimization model is proposed and solved in order to obtain the complete expressions for these operations. Next, various fundamental properties and relationships are examined and proved for the proposed arithmetic operations over IVIF values/sets. Furthermore, an example of a multi-criteria decision-making problem under uncertain conditions is studied and analyzed using the developed arithmetic operations on IVIF numbers. • Derived the generalized subtraction and division operations for IVIF values/sets. • The notion of Hamming distance and dual-simplex algorithm are used. • A deterministic LPP is proposed to obtain the expressions for the operations. • The fundamental properties and relationships for the basic operations are explored. • A case study of multi-criteria decision-making problems has been discussed. [ABSTRACT FROM AUTHOR]

The classification of huge complex datasets to extract the information needed for making informed decisions has gained a lot of importance in today's scenario due to the outspurt of data. With an increase in dataset size, the means of obtaining the desired information via data clustering techniques also became complex. This paper illustrates a framework for selecting the best combination of the Kernel function and the Lattice structure for better classification based on the size of the data. The performance of the SOM majorly depends on the choice of Kernel Function, Lattice Structures, and the size of the datasets. The types of kernel functions deployed in this study are Gaussian, Exponential, and Laplacian Kernel along with the lattice Structures such as hexagonal, Rectangular, and Square which result in nine combinations respectively. These nine combinations of the Kernel Function and the Lattice Structure have been tested on datasets ranging from small to large size using the performance metrics. A frequency table has been arrived at based on the classification of the dataset size, combinations of Kernel functions, and Lattice Structures. The results of this study infer that the combination of Gaussian Kernel -Square Lattice performs better for small-size datasets wherein the Exponential and Laplacian Kernel functions along with Hexagonal Lattice perform better for mid-size datasets, while the Laplacian Kernel – Square Lattice perform better for large size datasets. [ABSTRACT FROM AUTHOR]

In high-end equipment project selection, which involves high technical complexity, significant capital investment, and cross-industry applications, it is essential to involve experts from diverse fields in the decision-making process. To address the challenge of large-scale group decision-making, this paper proposes a three-way large-scale group decision-making model for triple-path consensus reaching process based on a rewards-and-punishments mechanism. The model consists of three stages. Firstly, the group consensus degree is calculated based on the initial input. If the specified threshold is not met, the consensus adjustment iteration stage is entered and decision makers are divided into three regions based on the Pareto principle. Secondly, separate calculations are conducted for the moving benefits and costs of decision makers in different regions, with the total benefits and costs used as targets for the NSGA-II algorithm. The Pareto solution set is obtained, and the solution with the least number of moving objects is selected as the three-way decision-making movement strategy. This process is repeated until the consensus threshold is reached or one of the regions is empty. Finally, based on the final adjustment results, scores are calculated for each project and a ranking is established as a selection suggestion. To demonstrate the feasibility and effectiveness of the model, ten high-end equipment projects are taken as examples, and the sensitivity of the model is tested by adjusting the number of decision makers. In conclusion, the proposed three-way large-scale group decision-making model based on a rewards-and-punishments mechanism for triple-path consensus reaching process is an effective and feasible approach for high-end equipment project selection. [ABSTRACT FROM AUTHOR]