Fuzzy Optimization and Decision Making

To address the impact of uncertain information reliability in decision-making environments and decision-makers’ psychology on decision results, this paper proposes a multi-criteria decision-making (MCDM) method for q-rung orthopair fuzzy Z-number (qROFZN) based on prospect–regret theory. First, a novel score function for qROFZN is developed, accompanied by a comprehensive discussion of its fundamental properties. Second, the Jaccard similarity distance measure is innovatively proposed for qROFZN, along with an analysis of its essential characteristics. Subsequently, on this basis, the prospect–regret theory is extended to the qROFZN environment, and a novel MCDM method is established. In this method, the criteria importance through inter-criteria correlation (CRITIC) weighting method and prospect theory’s weighting function are employed to determine comprehensive weights, which are then integrated with prospect–regret theory to identify the optimal solution, thereby considering both subjective and objective factors and enhancing the practical relevance of the decision results. Finally, a case study on medical diagnosis is conducted, along with comparative analyses. The experimental results are consistent with the outcomes of the referenced case, which validates the practicality and effectiveness of the proposed method, demonstrating that the qROFZN-based MCDM method can effectively capture fuzziness and reliability, thus enabling accurate decision analysis under uncertainty. Full article

Context: The Agile methodology has been prevalent in the software industry for more than two decades, marking a shift from plan-driven to market-driven approaches and introducing various challenges. While the literature identifies numerous challenges in Agile development, little attention has been given to their ranking and prioritization, which are critical for effective project management and decision making. This study fills this gap by combining empirical evidence from the literature and practitioners. Objectives: This study aims to identify and hierarchically prioritize the most recent challenges faced by Agile practitioners during product development. To achieve this, a Systematic Literature Review (SLR) was conducted using 115 published studies between 2010 and 2025 followed by empirical data collection from 30 Agile experts through semi-structured interviews conducted with practitioners from Agile companies and an online survey. This study applies Cumulative Voting (100-Dollar Test) and Multi-Criteria Decision Making (MCDM) techniques to rank and prioritize these challenges. Results: The SLR identifies several recurring Agile challenges; however, limited research has focused on their ranking and prioritization. The present study reveals new challenges, such as user interface complexities, lack of pre-development and pre-operational cost information, and lack of cost scalability at the module and feature levels. The current study identifies Inadequate Architecture (22%), Lack of Standardized Framework (18%), Communication and Coordination (16%), Poor Requirement Verification (13%), and Minimum Documentation (8%) as the most significant challenges. Conclusions: This study provides valuable insight for Agile practitioners and organizations, enabling more informed project planning, resource allocation, and strategic decision making. By focusing on the most critical challenges, teams can enhance software quality, streamline processes, and improve overall productivity in Agile environments. Full article

Split-type robots are increasingly deployed in unstructured confined environments such as underground coal mines, where autonomous navigation and cooperative tracking control remain critical challenges. This paper presents a visual target-assisted tracking control scheme for a split-type drilling robot, adopting an active leader–passive follower architecture. The leader robot performs autonomous mobility and obstacle avoidance using 3D LiDAR-based offline path generation and online optimal search. The follower robot uses AprilTag visual fiducial markers to estimate the six-degree-of-freedom relative pose via the Perspective-N-Point algorithm, and it tracks the leader using a two-dimensional fuzzy PID controller that adaptively tunes PID parameters. Extensive experiments are conducted in simulation, simulated tunnels, a large-scale robot platform, and a real drilling robot prototype. Results demonstrate that the leader achieves an average navigation error below 0.175 m, while the follower maintains an average relative tracking error within 0.06 m. The proposed method enables stable, comparable accuracy with smoother, less oscillatory response, and high-precision cooperative navigation for heavy-duty split-type robots, offering a practical solution for intelligent drilling operations in underground confined spaces. Full article

Over the past few decades, extensive attention has been given by researchers and practitioners to the development and application of multi-criteria decision-making (MCDM) methods within intuitionistic fuzzy environments across a wide range of fields and disciplines. This challenging research area has emerged as one of the most prominent topics, and its importance and popularity are expected to continue growing in the future. The elliptic intuitionistic fuzzy set (EIFS) addresses complex, multidimensional, non-symmetrical vagueness and uncertainty more effectively than other traditional intuitionistic fuzzy sets (IFSs). Sustainable renewable energy source selection is a critical decision-making (DM) process aiming to identify the most suitable energy alternative. The process of selecting sustainable renewable energy sources necessitates a comprehensive assessment of numerous criteria, which encompass environmental ramifications, economic feasibility, and societal acceptance. Contemporary research suggests novel methodologies to enhance this selection process, highlighting the need for an MCDM framework that integrates a variety of factors. This study presents an innovative DM framework for sustainable renewable energy source selection based on EIFS and a newly developed aggregation operator, the Elliptic Intuitionistic Fuzzy Weighted Muirhead Mean Aggregation (EIFWMMA) operator. These mechanisms expand upon conventional intuitionistic fuzzy frameworks by employing an elliptical portrayal of membership and non-membership degrees, facilitating a more accurate and lifelike representation of uncertainty and hesitation in evaluations by experts. To enhance computational efficiency, the framework weaves together machine learning-driven dimensionality reduction and weight optimization strategies of principal component analysis (PCA) for DM. The suggested operators are employed in an MCDM scenario centered around the selection of sustainable renewable energy sources, where the hierarchy of alternatives is established through score values derived from EIFWMMA. A comparative exploration of Circular Intuitionistic Fuzzy Sets (C-IFSs) and Interval-Valued Intuitionistic Fuzzy Sets (IVIFSs) uncovers that the elliptical formulation yields consistently reliable, precise, and geometrically comprehensible results. The findings affirm that EIFS-based operators offer a resilient, adaptable, and broadly applicable strategy for tackling MCDM challenges amidst uncertainty. The Min–Max normalization method is employed to validate our proposed methodology for identifying alternatives within the MCDM paradigm. It also improves accuracy, stability, and scalability in comparison to conventional approaches. Full article

Refined decision-making of the design intent is a key factor affecting the iterative design of complex equipment products. While current research on design intent decision-making generally emphasizes methodological innovation, it often neglects the individualized and fuzzy expressive characteristics of cognitive agents, as well as the actual status of the research object. This oversight leads to uncertainty in both design intent and design outcomes. To address these issues, in this paper, a refined decision-making method for the multimodal fuzzy design intent of complex products based on noncooperative–cooperative game serialization is proposed. First, through scenario analysis, the fuzzy design intent evaluation process of different cognitive agents is transformed into a cooperative game model based on a fuzzy network, achieving a preliminary assessment of design intent. On this basis, a noncooperative game-based refined matching and decision-making model for design intent across different dimensions is constructed, thereby completing the final design intent decision-making for a specific product model. Finally, the proposed method is applied to the design intent decision-making process of a CKA6180 CNC machine tool, yielding the conclusion that the two design intents of “good protective performance” and “grand appearance” should be prioritized, thereby verifying the practicality and effectiveness of the method. The analysis of the results reveals the following: ① The application of scenario analysis theory enables a more comprehensive and precise characterization of the design intents of different cognitive agents; ② The construction of a model combining a fuzzy network with a cooperative game facilitates a more complete representation and evaluation of multimodal fuzzy design intent data; ③ The integration of a refined design concept with a noncooperative game model leads to more definitive design intent decision outcomes, thereby reducing the “disturbance” of experience dependence in the early design phase and consequently enhancing subsequent design satisfaction. Full article

In agile, adaptive, and hybrid project management, the Weighted Shortest Job First (WSJF) technique is increasingly being used to prioritize the most relevant business opportunities for organizations. However, this decision-making process often involves the evaluation of multiple interconnected factors whose interactions can influence outcomes in unforeseen ways. Traditional decision-making models tend to assume independence between variables for the sake of simplicity and tractability. In real-world contexts, however, variables rarely operate in isolation; their interdependence introduces complexities that challenge the validity, robustness, and practical applicability of conventional decision-making tools. The objective of this research is to address the problem of interdependence among WSJF variables. To achieve this, Fuzzy Cognitive Mapping (FCM) was applied to evaluate the impact and influence of interdependencies during the decision-making process. The findings demonstrate that incorporating FCM into WSJF yields a 76% correlation in prioritization order with the best outcomes, compared to linear WSJF, while revealing a 24% variation that highlights areas for further study. This evidence indicates that accounting for interdependence leads to more efficient and reliable decision-making than traditional approaches. Full article