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 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.
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.
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 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.
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