New aggregation operators (AOs) for interval-valued intuitionistic fuzzy sets (IVIFS) have been developed, offering advancements in multi-attribute group decision-making (MAGDM). IVIFS employs intervals for membership and non-membership grades, providing a robust framework to handle uncertainties inherent in real-world scenarios. This study introduces operational laws for interval-valued intuitionistic fuzzy values (IVIFVs), formulated on the Frank T-norm and T-conorm, and presents a generalization of the Maclaurin symmetric mean (MSM) operator tailored for these values. Named the interval-valued intuitionistic fuzzy Frank weighted MSM (IVIFFWMSM) and interval-valued intuitionistic fuzzy Frank MSM (IVIFFMSM), these operators incorporate new operational principles that enhance the aggregation process. The effectiveness of these operators is demonstrated through their application to a MAGDM problem, where they are compared with existing operators. This approach not only enriches the theoretical landscape of fuzzy decision-making models but also provides practical insights into the optimization of market risk.
Computer-Aided Design (CAD) is employed extensively to facilitate design processes through software tools, serving as an indispensable component in Reverse Engineering (RE) across various sectors. This study elucidates the integration of RE and CAD in constructing generic product models for the manufacturing industry, particularly through the enhancement of the Feature-Based Design (FBD) approach. The Characteristic Product Features (CPF) methodology, pivotal in this research, enhances FBD by enabling the creation of parametrically defined generic features. Such features encapsulate a range of parameters including geometrical dimensions, topological constraints, and requirements for material properties and functionality, all dictated by the parametric model established. The methodology affords mechanical engineers enhanced capabilities to devise specific or customized manufacturing processes, applicable in domains spanning CAD, Computer-Aided Manufacturing (CAM), and Computer-Aided Engineering (CAE). The practical application of CPF within CAD is exemplified through the development of a three-dimensional geometrical model of an extruder screw utilized in polymer extrusion, illustrating the potential for tailored process innovation in manufacturing.
In this study, an economical prototype of a uniaxial shake table, named the Eastern Mediterranean University (EMU) shake table, was developed using an Arduino platform for the simulation of sinusoidal waves and scaled earthquake data. The table incorporates a ball-screw mechanism actuated by a stepper motor. Simulations were conducted using sinusoidal signals and earthquake data for three distinct seismic events, recorded at discrete timestamps. The performance of the shake table was assessed by analyzing the discrepancies between the input signals and the table's outputs.In sinusoidal mode, a feedforward gain was computed to achieve the desired output amplitude values. Furthermore, a decreasing trend in the error between input and output acceleration values was observed. The table, without any payload, achieved an acceleration of 0.8 g at a frequency of 14.5 Hz and an amplitude of 1 mm. However, the effectiveness of earthquake simulations was constrained by the storage capacity of the Arduino Uno and the motor's performance capacity. Iterative methods were necessary for each earthquake simulation to determine the minimal timestep size that the motor could optimally handle. The methodology for simulating earthquakes was elaborated, identifying limitations and suggesting areas for future enhancement. The major constraints of the project were cost, time, and resource availability.
In the pursuit of sustainable urban development, the implementation of cleaner propulsion systems in public transportation emerges as a critical strategy to reduce urban pollution and emissions. This study focuses on the City of Niš, where conventional propulsion vehicles, predominantly buses, contribute significantly to environmental degradation. The necessity to adopt alternative propulsion systems is underscored by the myriad of limitations and uncertainties that accompany such a transition. To address this complexity, the criteria importance through intercriteria correlation (CRITIC) method was employed to derive weight coefficients, while the evaluation based on distance from average solution (EDAS) method was utilized to select optimal propulsion systems. These methodologies facilitated a comprehensive evaluation of alternatives, including buses, electric trolleybuses, and trams, for both city and suburban public transport. The integration of these multi-criteria decision-making techniques enabled a systematic analysis of each alternative against established criteria, thereby assisting in the identification of the most advantageous propulsion systems. This approach not only aids in making informed decisions that align with sustainability objectives but also contributes significantly to mitigating the environmental impact of urban transport. The findings from this study provide a foundational framework that supports decision-makers in the strategic implementation of environmentally sustainable transport solutions in urban settings.
Effective management of supply chains, pivotal for sustaining business operations, is increasingly challenged by rising costs and complexity in logistics processes. Performance-Based Logistics (PBL) emerges as a critical strategy to enhance logistical effectiveness and competitiveness by focusing on performance targets rather than merely procuring products or services for maintenance and repair. This study examines the implementation of PBL in manufacturing enterprises and explores the factors influencing its benefits. By employing the polytopic fuzzy Stepwise Weight Assessment Ratio Analysis (SWARA) method, a sophisticated Multi-criteria Decision Analysis (MCDA) technique, criteria were weighted to determine their impact on PBL effectiveness. It was found that the paramount criterion affecting PBL advantages is the capability to manage operations more effectively, whereas the reduction in system lifecycle costs through savings in labor and training was identified as the least impactful. This analysis not only underscores the necessity of designing reliable systems that align with customer expectations but also highlights the added value PBL provides by integrating reduced support elements essential for logistics and sustainability. The findings advocate for meticulous emphasis on PBL practices within business models to optimize operational efficiency and strategic advantage.
This investigation delves into the noise attenuation capabilities of an innovatively designed muffler, which integrates additional piping and perforation to augment sound reflection. The enhanced muffler's design was rigorously simulated using the Helmholtz equation through the application of COMSOL Multiphysics software, aiming to delineate its acoustic performance relative to conventional models. The analysis underscored the superior efficacy of the optimized model in elevating transmission loss, diminishing acoustic pressure, and concurrently attenuating noise and frequency levels. A comparative evaluation of the transmission loss between the traditional and the novel muffler revealed a significant amelioration in the latter, highlighting its advanced noise reduction capabilities. The study further illuminated that exhaust pressure and back pressure contribute to acoustic wave generation, prompting the optimization of the muffler design to mitigate pressure, thereby circumventing potential damage. Notably, despite the analytical complexity, the construction of the proposed muffler remains straightforward, representing a pivotal advantage. This research contributes to the acoustic engineering field by presenting a muffler design that not only significantly reduces noise pollution but also demonstrates an ease of construction, making it a viable solution for widespread application. The findings advocate for the muffler's potential in enhancing acoustic comfort and environmental compliance in automotive and industrial settings.
The Spherical Fuzzy Set (SFS) framework extends the Picture Fuzzy Set (PFS) concept, offering enhanced precision in handling data characterized by conflict and uncertainty. Furthermore, similarity measures (SMs) are crucial for determining the extent of resemblance between pairs of fuzzy values. While existing SMs evaluate similarity by measuring the distance between values, they sometimes yield results that are illogical or unreasonable, due to certain properties and operational complexities. To address these anomalies, this paper introduces a parametric similarity measure based on three adjustable parameters ($\alpha_1, \alpha_2, \alpha_3$), allowing decision-makers to fine-tune the measure to suit various decision-making styles. This paper also scrutinizes existing SMs from a mathematical standpoint and demonstrates the efficacy of the proposed SM through mathematical modeling. Finally, we apply the proposed SM to tackle Multi-Attribute Decision-Making (MADM) problems. Comparative analysis reveals that our proposed SM outperforms certain existing SMs in the context of SFS-based applications.
This research employs a data-driven approach to optimize the plasma arc cutting process. The evaluation of cut quality is based on six output characteristics, while the input parameters include stand-off distance, cutting current, and cutting speed. The output metrics consist of the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ). Given the complexity of the process and the multitude of involved processing parameters, it is imperative to develop an optimization model to ensure the production of undisturbed structures. The primary aim of this study is to identify the most critical factors that facilitate optimal conditions for plasma arc cutting. The research goal is to determine the influence of input parameters on the plasma arc cutting quality using an adaptive neural fuzzy inference system (ANFIS). It has been found that the material removal rate (MRR), surface roughness, bevel angle, slag formation, kerf width, and heat-affected zone (HAZ) are predominantly affected by the interplay of cutting current and stand-off distance. Ideally, the best predictive model for various attributes, such as MRR, bevel angle, slag formation, surface roughness, kerf width, and HAZ, is one that synergistically combines cutting current and stand-off distance. This study, which evaluates multiple input parameters simultaneously, is expected to attract significant attention as it represents a pioneering small-scale investigation in the field.
In the field of industrial buildings, notably within warehouse settings, the optimization of floor space emerges as a paramount concern. The deployment of equipment facilitating continuous transport is mandated to not only augment throughput but also to economize on spatial allocation. Within this spectrum, continuous vertical conveyors, particularly of the paternoster variety, have been adopted as a quintessential solution. This study delineates the design intricacies of a paternoster continuous vertical conveyor, elucidating the methodology employed in calculating its maximal throughput, movement resistance, and the requisite power for its electric motor. Through a rigorous analytical approach, the performance of the paternoster conveyor is meticulously evaluated and juxtaposed against alternative continuous vertical conveyor systems. The findings underscore the paternoster conveyor's efficacy in achieving high throughput efficiency while conserving space, thus reaffirming its utility in industrial warehousing. The evaluation employs comparative metrics to highlight the paternoster system's superiority in specific operational parameters. This analysis contributes to the corpus of knowledge by providing a comprehensive examination of paternoster conveyors, thereby aiding in the selection of efficient transport solutions within the constraints of warehouse space optimization.
Against the backdrop of current market demands for a variety of products in small batches, traditional single-variety assembly lines are transitioning to variable production lines to accommodate the manufacturing of multiple similar products. This paper discusses the production unit as a microcosm of the variable production line, which boasts advantages such as smaller line scale, short setup times for changeovers, and ease of product scheduling. A mathematical model for splitting variable production lines into production units is established, with solutions at two levels: resource allocation and product scheduling. The upper-level model focuses on determining the number of production units and the distribution scheme of operators and equipment across multiple channels; the lower-level model addresses the product allocation problem, which is characterized by multiple stages, divisibility, variable batch sizes, and minimum batch size constraints. The solution approaches include a branch and bound method for small-scale problems to obtain optimal solutions, and an improved particle swarm optimization (PSO) algorithm for medium to large-scale problems to find near-optimal solutions. The innovation of the paper lies in the construction of the variable production line splitting model and the optimization algorithms for resource allocation and product scheduling.
In the realm of smart vehicle navigation, both in known and unknown environments, the crucial aspects encompass the vehicle's localization using an array of technologies such as GPS, cameras, vision systems, laser, and ultrasonic sensors. This process is pivotal for effective motion planning within the vehicle's free configuration space, enabling it to adeptly avoid obstacles. The focal point of such navigation systems lies in devising a path from an initial to a target configuration, striving to minimize the path length and the time taken, while simultaneously circumventing obstacles. The application of metaheuristic algorithms has been pivotal in this regard. These algorithms, characterized by their ability to exploit initial solutions and explore the environment for feasible pathways, have been extensively utilized. A significant body of research in robotics and automation has focused on evaluating the efficacy of population-based algorithms including Genetic Algorithm (GA), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Firefly Algorithm (FA), and Whale Optimization Algorithm (WOA). Additionally, trajectory-based methods such as Tabu Search (TS) and Simulated Annealing (SA) have been scrutinized for their proficiency in identifying short, feasible paths among the plethora of solutions. There has been a surge in the enhancement and modification of these algorithms, with a multitude of hybrid metaheuristic algorithms being proposed. This review meticulously examines various metaheuristic algorithms and their hybridizations, specifically in their application to the path planning challenges faced by smart vehicles. The exploration extends to the comparison of these algorithms, highlighting their distinct advantages and limitations. Furthermore, the review delves into potential future directions in this evolving field, emphasizing the continual refinement of these algorithms to cater to the increasingly complex demands of smart vehicle navigation.
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