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Öğe 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.Öğe 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.Öğe 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.Öğe 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.Öğe Foundations of Decision(Springer Science and Business Media Deutschland GmbH, 2023) Hosseinzadeh Lotfi, F.; Allahviranloo, T.; Pedrycz, W.; Shahriari, M.; Sharafi, H.; Razipour, GhalehJough, S.Decisions unfold, guided by science’s light, in choices, science’s wisdom resides, for Guiding paths with less clarity. In this enlightening chapter on the Foundations of Decision Making, readers will embark on a journey through the intricate web of decision theory and decision science. Delving into the profound philosophy that underlies these concepts, we'll unravel the fundamental principles that guide human choices. With a systematic exploration of reputable domains and practical applications, readers will witness the transformative power of these principles in diverse contexts, from economics to psychology and beyond. We'll navigate through the Hierarchy of Decisions, dissecting the layers of choices from the mundane to the monumental, and in the process, gain invaluable insights into the art and science of decision-making. As we traverse this intellectual landscape, readers will acquire the skills to evaluate options with precision and make well-informed decisions based on robust reasoning. By the end of this chapter, readers will be able to comprehend and appreciate the fundamental principles and concepts that form the foundation of decision-making. They will develop a solid understanding of key components such as decision theory, rationality, and cognitive biases. With this knowledge, readers will acquire the necessary analytical skills to effectively recognize the complexity of decision-making scenarios, evaluate alternative options, and make informed decisions based on sound reasoning and evidence. Moreover, readers will gain a broader perspective on the significance of decision-making across various aspects of life, including personal, professional, and societal contexts. This chapter will lay a strong groundwork for readers’ journey towards becoming more confident and adept decision-makers © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Fuzzy Introductory Concepts(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 fundamentals of fuzzy theory and introduces the concept of fuzzy type-2. Fuzzy sets, which allow for handling uncertainty and vagueness in data, are discussed as an extension of traditional binary set theory. Arithmetic operations on fuzzy sets, such as union, intersection, and complement, enable logical reasoning and informed decision-making based on fuzzy information. The chapter also covers triangular and trapezoidal fuzzy numbers, which provide a means to represent imprecise and uncertain quantities. These fuzzy numbers are essential for quantifying and reasoning with fuzzy data, with applications in various domains for modeling real-world phenomena. Furthermore, the chapter focuses on the ranking of fuzzy numbers using different functions. Techniques and tools for ranking fuzzy numbers are examined, offering insights into comparing and ordering fuzzy data based on their degrees of membership and uncertainty. This information is valuable for decision-making processes when dealing with fuzzy information. Overall, the chapter provides a comprehensive introduction to fuzzy theory and its practical applications, particularly in fuzzy type-2. By understanding fuzzy sets, arithmetic operations, triangular and trapezoidal fuzzy numbers, and the ranking of fuzzy numbers, readers gain a solid foundation in utilizing fuzzy logic and reasoning. This knowledge empowers them to make more informed decisions in domains where imprecise and uncertain data are prevalent. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe The Measuring Attractiveness by a Categorical Based Evaluation Technique (MACBETH) 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 introduces the Fuzzy MACBETH (Measuring Attractiveness by a Categorical Based Evaluation Technique) method. It begins by providing an overview of the MACBETH method, emphasizing its principles and applications in decision-making contexts. Then proceeds to outline the six-step algorithm of the MACBETH method, offering a comprehensive understanding of its systematic process. This algorithm serves as a guide for readers to grasp the practical implementation of MACBETH in evaluating alternatives based on multiple criteria. The fuzzy MACBETH method is discussed in detail, emphasizing its ability to handle uncertainty and vagueness in decision-making situations. To illustrate its practicality, the method is applied to choose a manager for the Research and Development (R&D) department of a company. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Multi Attributive Border Approximation Area Comparison (MABAC) 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 introduces the concept of Fuzzy MABAC (Multi-Attributive Border Approximation Area Comparison), an innovative Multiple Attribute Decision Making (MADM) method. The chapter begins by providing a comprehensive overview of the MABAC method. To illustrate the practical implementation of MABAC, a numerical example is presented, utilizing crisp data. Building upon the understanding of MABAC, the chapter then explore the intricacies of fuzzy MABAC. The algorithm for fuzzy MABAC is elucidated and its handling in decision-making problems involving imprecise or uncertain data is demonstrated. To illustrate the efficacy of fuzzy MABAC, the method is applied to rank bank clerks according to four criteria. The step-by-step process of employing fuzzy MABAC to determine rankings is discussed. By the end of this chapter, readers will have a comprehensive understanding of both MABAC and fuzzy MABAC, and their practical applications in MADM. The numerical example and the real-life application in ranking bank clerks highlight the potential of fuzzy MABAC as an effective decision-making tool in complex scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe The Multi-Objective Optimization Ratio Analysis (MOORA) 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 MOORA (Multi-Objective Optimization on the Basis of Ratio Analysis) method as a powerful tool for solving multi-criteria problems. The MOORA method provides a systematic approach to decision-making by considering multiple criteria and their relative importance. Through an example from the real world, the application of the MOORA method is demonstrated, highlighting its effectiveness in ranking alternatives and aiding decision-making processes. Furthermore, this chapter discusses the extension of the MOORA method in fuzzy environment. Incorporating fuzzy sets and fuzzy numbers, enables the MOORA method accommodate vague and subjective assessments of criteria and alternatives. A real-world example is presented to showcase the application of the fuzzy MOORA method and illustrate its advantages and limitations in dealing with fuzzy data. Additionally, preferential voting has been used for aggregating the rankings. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Non-Compensatory Methods 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 discusses non-compensatory methods with fuzzy indicator values. These methods are applicable to problems where we want to select an alternative among the available choices. The methods discussed in this chapter include fuzzy Lexicographic, fuzzy Dominance, fuzzy Max–Min, fuzzy Conjunctive Satisfying, and fuzzy Disjunctive Satisfying. In all methods, the assumption is that all indicator values are fuzzy. Non-compensatory methods have been developed using fuzzy ranking functions, and examples are provided for each method. Different ranking functions are used to solve the examples. In all examples, the results depend not only on the fuzzy values of the indicators but also on the ranking function. All non-compensatory methods are utilized in a fuzzy environment to choose one alternative from the available choices. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Simple Additive Weighting (SAW) Method 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 explores the expansion of the Simple Additive Weighting (SAW) method in interval and fuzzy environments. The SAW method, a popular decision-making approach, is enhanced by incorporating interval arithmetic and fuzzy logic. Real-world examples illustrate the practical implications of the expanded SAW method, demonstrating its versatility in diverse domains. The abstract highlights the benefits and challenges of the interval and fuzzy SAW method and emphasizes its potential as a valuable tool for informed decision-making in uncertain and imprecise contexts. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Technique for Order Preferences by Similarity to Ideal Solutions (TOPSIS) 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 investigate the world of Fuzzy TOPSIS, exploring its philosophical underpinnings, applications, and practical examples. The chapter begins with an exploration of the existential philosophy underlying the TOPSIS method, setting the stage for the subsequent discussions. The TOPSIS method is then introduced as a powerful tool for ranking alternatives, and its application in the chapter focuses on utilizing the TOPSIS method to determine the order of preference among alternatives. The method provides a systematic approach for decision-making, facilitating the selection of the most favorable alternative. The main highlight of the chapter lies in the comprehensive examination of the challenges and considerations associated with implementing Fuzzy TOPSIS. The chapter addresses the various attitudes and perspectives that decision-makers face when dealing with the uncertainties and imprecisions inherent in real-world decision scenarios. Moreover, the chapter presents an in-depth analysis of the Group Fuzzy TOPSIS method, enabling multiple decision-makers to collaboratively evaluate alternatives. This approach offers a valuable framework for addressing complex decision-making scenarios where diverse opinions and preferences are involved. In addition to Group Fuzzy TOPSIS, the chapter explores the Intuitionistic Fuzzy TOPSIS method, providing an alternative approach to tackle decision-making problems under ambiguity and intuition-based judgments. To illustrate the practical application of Fuzzy TOPSIS, the chapter concludes with two compelling case studies. These examples showcase the versatility and effectiveness of Fuzzy TOPSIS in different contexts, demonstrating its applicability in diverse domains. Furthermore, the application of TOPSIS in fuzzy data envelopment analysis has been discussed. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe VIKOR Method 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 VIKOR method in deterministic and fuzzy modes, showcasing examples and discussing preference voting and data envelopment analysis. The integration of preference voting enhances decision-making by incorporating subjective judgments, while data envelopment analysis ensures the reliability and completeness of information. The chapter highlights the practicality and versatility of Fuzzy VIKOR and provides insights for future research in this field. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.Öğe Weight Determination Methods 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 focuses on the determination of fuzzy weights in multi-criteria decision making (MCDM). Various methods for fuzzy weight determination are explored, with a particular emphasis on the fuzzy least square error method and the fuzzy BWM (Best Worst Method). The chapter presents theoretical explanations of these methods and provides practical examples to illustrate their application. Through the examination of these methods and their solutions, readers gain insights into the process of assigning weights to criteria in MCDM problems, considering the inherent uncertainty and imprecision in decision-making situations. The chapter aims to enhance readers’ understanding of fuzzy weight determination methods and their potential applicability in real-world decision-making scenarios. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
