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Discussions of the resiliency, sustainability, and agility of supply chains are important in the research and management of supply chains in these difficult times, considering the ongoing pandemic of COVID-19. A viable supply chain is often characterized by resiliency, sustainability, and agility in its network design. Resiliency is essential because disruption and demand fluctuations are forced upon SCs, and the effects of these for many managerial supply chains are unknown. In addition, applying novel technology in the supply chain, such as blockchain, Internet-of-Things (IoT), and artificial intelligence (AI) as agility tools can assist and enable the transition to lean production. This special issue of the Foundations of Computing and Decision Sciences is dedicated to advancements in this fields. Besides, the special issue covers instructional information about OR techniques which are useful for addressing real-world applications on such challenges.

This special issue of the Foundations of Computing and Decision Sciences, titled “Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics”, is devoted to the application of Computational Performance Analysis (CPA) for real-life phenomena. The special issue and its editorial present novel intelligent methods as they meet with various research topics in production and logistics, especially in terms of challenges, limitations and future trends. This special issue aims to bring together current progress on the CPA, organization management, and novel models and solution techniques that can contribute to a better understanding of the CPA systems and delineate useful practical strategies. Methodologically interesting and well-documented case studies are highly recommended. Additionally, the special issue covers innovative cutting-edge research methodologies and applications in the related research field.

We introduce the concept of logarithmic square root neutrosophic normal interval-valued set (LO-SRNSNIVS) based on multiple attribute decision-making (MADM) problems that can be solved in our communication. The logarithmic square root neutrosophic sets (LO-SRNSSs) are new approach of the interval-valued neutrosophic sets (IVNSSs) and neutrosophic sets (NSSs). We will examine a number of aggregating operators (AOs). The LO-SRNSNIV using weighted (LO-SRNSNIVW) operators of this article. Now, we presented logarithmic square root NSNIVW averaging (LO-SRNSNIVWA), logarithmic square root NSNIVW geomentric (LO-SRNSNIVWG), logarithmic square root generalized NSNIVW averaging (LO-SRGNSNIVWA) and logarithmic square root generalized NSNIVW geometric (LO-SRGNSNIVWG). We also develop an algorithm to solve MADM problems. A real-world scenario is given with examples, as well as explanations of how Euclidean distance (ED) and Hamming distance (HD) are applied. We will explain the properties of these sets using various algebraic structures. The notion for the scoring function, accuracy function and operational laws of the LO-SRNSNIVS. In addition to their simplicity and practicality, they can help determine which option is best to pursue. Thus, the results of the described models are more understandable and closely related to integer $ \Psi $. A few proposed and existing models are also compared in order to demonstrate their validity and applicability. [ABSTRACT FROM AUTHOR]

Symbolic simplification of analog circuits is a computationally challenging problem due to the exponential growth of the number of symbolic terms with the circuit size. In recent years, researchers have proposed various methods to address this problem, but these methods often require matrix- or graph-based symbolic analysis methods, which can be computationally expensive and memory-intensive, especially for real-size analog circuits. To overcome these limitations, we introduce a new methodology called Direct Simplified Symbolic analysis (DSSA). The proposed DSSA method views simplified symbolic circuit analysis as a modeling problem and directly produces the most significant terms of the transfer function, without the need for traditional circuit analysis. One of the main advantages of DSSA is that it significantly reduces computational complexity and required memory compared to the existing techniques. This is achieved by generating a dataset using the Monte-Carlo simulation method and performing a genetic algorithm to solve the established modeling problem. The objective is to minimize the average numerical error between the simplified symbolic expression and the exact numeric expression for all data points. The proposed method has been tested on five circuits in MATLAB, and the results clearly demonstrate its performance against existing methods. The findings by the DSSA algorithm across five circuits reveal 0.64 dB and 1.36 dB variations for the average and maximum dc-gain, respectively. Moreover, the DSSA algorithm exhibits an average pole/zero error of 6.8% and a maximum pole/zero displacement of 16.8%. It has the potential to improve the efficiency and accuracy of symbolic analysis, making it a promising tool for circuit designers and engineers. [ABSTRACT FROM AUTHOR]