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Advances in Numerical Analysis Emphasizing Interval Data
(CRC Press, 2022) Allahviranloo, T.; Pedrycz, W.; Esfandiari, A.
Numerical analysis forms a cornerstone of numeric computing and optimization, in particular recently, interval numerical computations play an important role in these topics. The interest of researchers in computations involving uncertain data, namely interval data opens new avenues in coping with real-world problems and deliver innovative and efficient solutions. This book provides the basic theoretical foundations of numerical methods, discusses key technique classes, explains improvements and improvements, and provides insights into recent developments and challenges. The theoretical parts of numerical methods, including the concept of interval approximation theory, are introduced and explained in detail. In general, the key features of the book include an up-to-date and focused treatise on error analysis in calculations, in particular the comprehensive and systematic treatment of error propagation mechanisms, considerations on the quality of data involved in numerical calculations, and a thorough discussion of interval approximation theory. Moreover, this book focuses on approximation theory and its development from the perspective of linear algebra, and new and regular representations of numerical integration and their solutions are enhanced by error analysis as well. The book is unique in the sense that its content and organization will cater to several audiences, in particular graduate students, researchers, and practitioners. © 2022 Tofigh Allahviranloo, Witold Pedrycz and Armin Esfandiari.
Analysis on the Hesitation and its Application to Decision Making
(Regional Association for Security and crisis management, 2024) Yang, Y.; Lee, S.; Kim, K.S.; Zhang, H.; Huang, X.; Pedrycz, W.
A novel score function based on the Poincaré metric is proposed and applied to a decision-making problem. Decision-making on Fuzzy Sets (FSs) has been considered due to the flexibility of the data, and it is applied to the decision-making. However, decisions with FSs are sometimes nondecisive even for different membership degrees. Hence, Intuitionistic Fuzzy Sets (IFSs) data is applied to design a score function for the decision-making with the Poincaré metric. This function is supported by the profound information of IFSs; IFSs include hesitation degree together with membership and non-membership degree. Hence, IFS membership and non-membership degree are expressed as two-dimensional vectors satisfying the Poincaré metric for simplification. At the same time, the proposed approach addresses the hesitation information in the IFS data. Next, a score function is proposed, constructed and provided. The proposed score function has a strict monotonic property and addresses the preference without resorting to the accuracy function. The strict monotonic property guarantees the preference of all attributes. Additionally, the existing problem of score function design in IFSs is addressed: they return zero scores even with different meanings for the same membership and non-membership degree. The advantages of the proposed score function over existing ones are demonstrated through illustrative examples. From the calculation results, the proposed decision score function discriminates between all candidates. Hence, the proposed research provides a solid foundation for the hesitation analysis on the decision-making problem. © 2024 The Authors.
Analytical Hierarchy Process (AHP) in Fuzzy Environment
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Shahriari, M.; Sharafi, H.; Razipour, GhalehJough, S.
This chapter provides an overview of hierarchical decision structures, focusing on the AHP and ANP methods. It explains how these structures break down complex problems and organize criteria, sub-criteria, and alternatives in a hierarchical manner. The Analytic Hierarchy Process (AHP) is explored in detail, including its step-by-step implementation process and practical examples. The chapter then introduces the Analytic Network Process (ANP), which extends AHP to decision problems with interdependencies and feedback loops. The construction of networks, calculation of priority weights, and interpretation of results within the ANP framework are discussed. Finally, Fuzzy AHP is introduced as a method that addresses the limitations of AHP by incorporating fuzzy sets theory, supported by real-world case studies. Fuzzy AHP allows decision-makers to express preferences in a flexible and nuanced manner, capturing uncertainties and ambiguity present in real-world scenarios. Its advantage lies in handling vague and imprecise information systematically, enabling the effective incorporation of subjective assessments and expert opinions © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation
(Institute of Electrical and Electronics Engineers Inc., 2023) Du, S.; Ma, X.; Li, X.; Wu, M.; Cao, W.; Pedrycz, W.
Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.
Applications of Preferential Voting in Industry and Society
(Springer Science and Business Media Deutschland GmbH, 2023) Soltanifar, M.; Sharafi, H.; Hosseinzadeh Lotfi, F.; Pedrycz, W.; Allahviranloo, T.
Preferential voting models as well as hybrid Multi-Attribute Decision Making methods based on these models are widely used as decision support tools in industry and society. In this chapter, the applications that have been presented so far in the industry and society of these models and methods are briefly stated. It should be noted that the application of preferential voting models and hybrid MADM methods presented based on them are not exclusive to the presented cases and the authors hope that this application will be expanded in the future. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Artificial intelligence for production, operations and logistics management in modular construction industry: A systematic literature review
(Elsevier B.V., 2024) Liu, Q.; Ma, Y.; Chen, L.; Pedrycz, W.; Skibniewski, M.J.; Chen, Z.-S.
Artificial intelligence (AI) has garnered significant attention within the modular construction industry, emerging as a prominent frontier development trend. A comprehensive and systematic analysis is required to gain a thorough understanding of the existing literature on the use of AI in the management of production, operations, and logistics within the modular construction industry. This review delves into the various aspects of AI implementation in this sector, adopting a critical perspective. The objective of this paper is to analyze the progress, suitability, and research patterns in the field of AI for the management of productions, operations, and logistics within the modular construction industry. First, a concise overview of AI technologies pertaining to the contemporary research on the production, operations and logistics management of the modular construction industry is provided. Second, a bibliometric analysis is performed to provide a comprehensive overview of the existing publications pertaining to this subject matter. Subsequently, this paper presents literature reviews and outlines future directions for each component, specifically AI in the context of production management, operations management, and logistics management within the modular construction industry. The review provides a valuable knowledge base and roadmap to guide future research and development efforts in AI-enhanced modular construction management. © 2024 Elsevier B.V.
Basic Concepts of Voting
(Springer Science and Business Media Deutschland GmbH, 2023) Soltanifar, M.; Sharafi, H.; Hosseinzadeh Lotfi, F.; Pedrycz, W.; Allahviranloo, T.
The problem of “election” using the aggregation of voters’ votes is one of the most important problems of group decision-making for which several models have been presented so far. Consider a group of people who need to make a group decision based on voters’ votes. Given that people have different opinions and tastes, how can they make a decision that includes all individual opinions? From such a simple issue to the choice of politicians, all of them require a collective decision. In this chapter, this concept and methods of aggregating individual votes to reach a collective decision are discussed. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Broad-deep network-based fuzzy emotional inference model with personal information for intention understanding in human–robot interaction
(Elsevier Ltd, 2024) Li, M.; Chen, L.; Wu, M.; Hirota, K.; Pedrycz, W.
A broad-deep fusion network-based fuzzy emotional inference model with personal information (BDFEI) is proposed for emotional intention understanding in human–robot interaction. It aims to understand students’ intentions in the university teaching scene. Initially, we employ convolution and maximum pooling for feature extraction. Subsequently, we apply the ridge regression algorithm for emotional behavior recognition, which effectively mitigates the impact of complex network structures and slow network updates often associated with deep learning. Moreover, we utilize multivariate analysis of variance to identify the key personal information factors influencing intentions and calculate their influence coefficients. Finally, a fuzzy inference method is employed to gain a comprehensive understanding of intentions. Our experimental results demonstrate the effectiveness of the BDFEI model. When compared to existing models, namely FDNNSA, ResNet-101+GFK, and HCFS, the BDFEI model achieved superior accuracy on the FABO database, surpassing them by 12.21%, 1.89%, and 0.78%, respectively. Furthermore, our self-built database experiments yielded an impressive 82.00% accuracy in intention understanding, confirming the efficacy of our emotional intention inference model. © 2024 Elsevier Ltd
Calculating Environmental, Social and Economic Efficiencies of a Two-Stage Supply Chain in DEA-R Using Genetic Algorithm
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Mozaffari, M.R.; Gerami, J.
In this chapter, first the inputs and outputs of the supply chain are divided based on sustainability factors. Then, the environmental, social and economic efficiency scores are calculated. In addition, in the evaluation of a supply chain, the multiplier form of DEA-R model in an output-orientation is utilized. Due to the time limit for accessing data and for avoiding pseudo inefficiencies, DEA-R models in the supply chain are proposed based on the determined sustainability factors. At the end, the supply chains of 20 fire stations in in the city of Shiraz, Iran, at the end of 2017 are evaluated using DEA-R models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
A Centralized Resource Allocation Approach for Two-Stage Data Envelopment Analysis
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Mozaffari, M.R.; Gerami, J.
In this chapter, two-stage network models based on non-radial SBM models in DEA and DEA-R are presented. These models are also presented based on central resource allocation models. Two-stage network performance evaluation models with the structure of central resource allocation, by solving a model, the targets corresponding to the DMUs in the first, second and overall stages are achieved. The two-stage network DEA-R models are in the form of a linear programming problem and obtains the efficiency and targets for each of the DMU in the first stages of the second and overall stages. Also, the central resource allocation (CRA) models achieve the targets corresponding to each of the DMUs. In this study, non-radial models are used based on the slacks of input and output components, and in this way, we recognize all inefficiency factors. In following, a case study is presented for the evaluation of education units in Iran. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
The Complex Proportional Assessment (COPRAS) in Uncertainty Environment
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Shahriari, M.; Sharafi, H.; Razipour, GhalehJough, S.
In this chapter, the algorithm of COmplex PRoportional ASsessment (COPRAS) method with crisp data is explained and then it is applied to select among varied smart phones considering five different attributes. Afterward, Fuzzy COPRAS method is explained in detail. To choose the best investment, Fuzzy COPRAS method has been used, taking into account the conflicting criteria and according to a group of the experts’ opinion. Fuzzy COPRAS provides valuable tools for addressing multi-criteria decision-making challenges and fuzzy decision-making scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Concept-cognitive learning survey: Mining and fusing knowledge from data
(Elsevier B.V., 2024) Guo, D.; Xu, W.; Ding, W.; Yao, Y.; Wang, X.; Pedrycz, W.; Qian Y.
Concept-cognitive learning (CCL), an emerging intelligence learning paradigm, has recently become a popular research subject in artificial intelligence and cognitive computing. A central notion of CCL is cognitive and learning things via concepts. In this process, concepts play a fundamental role when mining and fusing knowledge from data to wisdom. With the in-depth research and expansion of CCL in scopes, goals, and methodologies, some difficulties have gradually emerged, including some vague terminology, ambiguous views, and scattered research. Hence, a systematic and comprehensive review of the development process and advanced research about CCL is particularly necessary at the moment. This paper summarizes the theoretical significance, application value, and future development potential of CCL. More importantly, by synthesizing the reviewed related research, we can acquire some interesting results and answer three essential questions: (1) why examine a cognitive and learning framework based on concept? (2) what is the concept-cognitive learning? (3) how to make concept-cognitive learning? The findings of this work could act as a valuable guide for related studies in quest of a clear understanding of the closely related research issues around concept-cognitive learning. © 2024 Elsevier B.V.
Constructing order-2 information granules of linguistic expressions with the aid of the principle of justifiable granularity
(Elsevier B.V., 2024) Huang, T.; Pedrycz, W.; Zhang, Q.; Tang, X.; Yang, S.
To capture collective opinions/evaluations in a collection of individual linguistic expressions, this study proposes an approach to construct order-2 information granules by extending the numerical data-based principles of justifiable granularity to a linguistic data-based one. First, the two key criteria of the principle of justifiable granularity, namely coverage and specificity, are formally defined in the context of order-2 information granules. Second, three order-2 information granules construction models by maximizing the product of coverage and specificity are developed for coping with one-dimensional direct linguistic expressions, linguistic preference relations, and multi-dimensional direct linguistic expressions, respectively. Third, considering that the developed order-2 information granules construction models exhibit a non-linear uncertain objective function and equality constraints, the constrained multi-swarm PSO without velocity is improved with the use of the ? constrained handling technique for effectively solving the models. Case studies on the considered three types of linguistic expressions show the applicability of the proposed models and ensuing algorithm. The superiority of the improved algorithm in handling the proposed models is demonstrated by comparison with the original one. The effectiveness of the proposed models in terms of abnormal corrective ability and balance of coverage and specificity is verified by comparing with a family of Top-n methods. The originality of this study lies in the construction of an operational entity, namely order-2 information granule, that reflects the group opinion without specifying the formalism of individual linguistic expressions, which provides an effective and efficient way to aggregate ubiquitous linguistic information for subsequent computation, reasoning and decision-making. © 2024 Elsevier B.V.
Construction metaverse: Application framework and adoption barriers
(Elsevier B.V., 2024) Chen, Z.-S.; Chen, J.-Y.; Chen, Y.-H.; Pedrycz, W.
This paper addresses the limited research on the metaverse's application in the construction industry. It aims to investigate how the metaverse can empower construction, identify adoption barriers, and determine the most significant barriers. We propose a novel application framework of construction metaverse based on cyber-physical-social systems, identify 17 barriers using the political-economic-social-technological framework, and employ an expert survey and bi-objective optimization to rank the barriers. Results indicate that scalability, lack of policy incentives, and immature business models are the most critical barriers. The findings provide valuable insights for researchers, practitioners, and policymakers in the construction industry, helping to allocate resources effectively and drive metaverse development. The study's importance lies in its potential to guide successful metaverse integration in construction, leading to improved efficiency and innovation. This research inspires future work on specific metaverse applications in construction and interdisciplinary research to understand and overcome the identified barriers. © 2024 Elsevier B.V.
Cost and Revenue Efficiency Based on MOLP in DEA-R Models
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Mozaffari, M.R.; Gerami, J.
In this chapter, cost and revenue efficiency models are presented in DEA and DEA-R, then using the multi-objective linear programming (MOLP) structure, cost and revenue efficiency models are proposed in DEA and DEA-R. Because in the MOLP structure, Pareto-optimal solutions are important, the score of cost efficiency and revenue with the MOLP structure can be useful for the decision maker (DM). Also, the overall efficiency model is also proposed by considering the prices of inputs and outputs based on the structure of MOLP. Finally, the 31 branches of the Commercial Bank of China, the efficiency of the cost of revenue are compared in DEA and DEA-R. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
The Criteria Importance Through Inter-Criteria Correlation (CRITIC) in Uncertainty Environment
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Shahriari, M.; Sharafi, H.; Razipour, GhalehJough, S.
The chapter begins by discussing the conventional CRITIC method and its algorithm, providing a foundation for understanding the subsequent introduction of the fuzzy variant. An example scenario is presented to demonstrate the evaluation and prioritization of projects based on seven specific criteria. The CRITIC method is applied, allowing decision-makers to assess the relative importance of each criterion and make informed decisions regarding project selection. Subsequently, the chapter introduces the fuzzy extension of the CRITIC method. The algorithm of the fuzzy CRITIC is explained in detail, highlighting the utilization of fuzzy data to represent uncertain and imprecise information in decision-making. To illustrate the application of the fuzzy CRITIC method, a case study is presented involving the search for an optimal location for a solar farm. Fuzzy trapezoidal data is utilized to capture the inherent uncertainty associated with various location factors. The combination of theoretical explanations, algorithmic discussions, and practical examples offers a comprehensive understanding of the fuzzy CRITIC method's potential for decision-making in complex and uncertain environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Determining the Production Possibility Set for Ratio Data: A Novel Hybrid DEA-R Approach
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Mozaffari, M.R.; Gerami, J.
In some organizations, performance evaluation is measured using ratios such as the ratio of outputs to inputs or vice versa (leverage ratio and quick ratio). While a handful of papers have been published on the DEA-R research stream, thus far the role of each input in terms of the production of each output has been neglected. Therefore, this chapter revisits the axiomatic structure that underlies DEA and Ratio Analysis (RA) models in terms of their resulting Production Possibility Set (PPS) and develops a novel hybrid DEA-RA approach capable of measuring the share of each index (i.e. input/output or output/input ratios) in the overall efficiency measure. As a consequence of this novel perspective on efficiency decomposition, weight restrictions for each specific input can be determined in accordance with its share in producing a given output. A case study on an auto spare-parts company is presented to illustrate the applicability of the new DEA-RA approach, while providing means for a robustness analysis with previous DEA-R models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Efficient cloud data center: An adaptive framework for dynamic Virtual Machine Consolidation
(Academic Press, 2024) Rozehkhani, S.M.; Mahan, F.; Pedrycz, W.
Cloud computing is a thriving and ever-expanding sector in the industry world. This growth has sparked increased interest from organizations seeking to harness its potential. However, the sheer volume of services and offerings in this field has resulted in a noticeable surge in related data. With the rapid evolution and growing demand, cloud computing resource management faces a fresh set of challenges. Resource limitations, such as high maintenance costs, elevated Energy Consumption (EC), and adherence to Service Level Agreements (SLA), are critical concerns for both the cloud computing industry and its user organizations. In this context, taking a proactive approach to resource management and Virtual Machine Consolidation (VMC) has become imperative. The logical management of resources and the consolidation of Virtual Machines (VMs) in a manner that aligns with the requirements and demands of service providers and users have garnered widespread attention. The goal of this proposed paper is to focus on addressing the VMC problem within a unified framework, divided into two main phases. The first phase deals with host workload detection and prediction, while the subsequent phase tackles the selection and allocation of appropriate VMs. In our proposed method, for the first time, we use a Granular Computing (GRC) model, which is an efficient, scalable, and human-centric computational approach. This model exhibits behaviors similar to intelligent human decision-making, as it can simultaneously consider all factors and criteria involved in the problems. We evaluated our proposed method through simulations using CloudSim on various types of workloads. Experimental results demonstrate that our proposed algorithm outperforms other algorithms in all measurement metrics. © 2024 Elsevier Ltd
Elimination Choice Translating Reality (ELECTRE) in Uncertainty Environment
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Shahriari, M.; Sharafi, H.; Razipour, GhalehJough, S.
This chapter explores the application of the ELECTRE method in both precise and fuzzy modes for decision-making. It discusses the concepts, strengths, and weaknesses of the method. The chapter covers the precise mode, addressing issues such as score assignment and rank reversal. It also introduces the fuzzy mode, incorporating fuzzy logic and sets for handling uncertainty. Practical examples demonstrate the effectiveness of ELECTRE in various domains. This chapter serves as a valuable resource for researchers and practitioners seeking to utilize ELECTRE for informed decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
Finding Suitable Target in DEA and DEA-R
(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Mozaffari, M.R.; Gerami, J.
Organizations may have two strategies in DEA, D.E.A.-R. to find suitable targets for DMUs. In the first strategy, efficient, inefficient units are separated using the efficiency scale of classical models, then the target is determined for all inefficient units. In the second strategy, the DMUs are categorized based on the efficiency scale, then the target is found in each category. Therefore, the target DMUs do not need to be on the efficiency frontier. In this section, MOLP models are used to find suitable targets in DEA, D.E.A.-R. on the efficiency frontier. In conclusion, the target for 41 Chinese commercial banks in 2022 is obtained based on MOLP models. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

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