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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]

The special issue: “Numerical Techniques Meet with OR” of the Foundations of Computing and Decision Sciences consists of two parts which are of the main theme of numerical techniques and their applications in multi-disciplinary areas. The first part of this special issue was already collected in the FCDS Vol. 46, issue 1. In this second part of our special issue editorial, a description of the special issue presents numerical methods which can be used as alternative techniques for Scientific Computing and led Operational Research applications in many fields for further investigation.

This paper addresses Acoustic Emission (AE) from Computer Numerical Control (CNC) machining operations. Experimental measurements are performed on the CNC lathe sensors to provide the power consumption data. To this end, a hybrid methodology based on the integration of an Artificial Neural Network (ANN) and a Shuffled Frog-Leaping Algorithm (SFLA) is applied to the data resulting from these measurements for data fusion from the sensors which is called SFLA-ANN. The initial weights of ANN are selected using SFLA. The goal is to assess the potency of the signal periodic component among these sensors. The efficiency of the proposed SFLA-ANN method is analyzed compared to hybrid methodologies of Simulated Annealing (SA) algorithm and ANN (SA-ANN) and Genetic Algorithm (GA) and ANN (GA-ANN).

This special issue of the Foundations of Computing and Decision Sciences, titled ”Numerical Techniques Meet with OR”, is devoted to the numerical techniques and their applications in real-world phenomena. The special issue and its editorial present numerical algorithms as they meet with different research topics such as, e.g., from operational research, supply chain management, geometrical structures and Covid-19 effects on financial applications. Besides, the special issue covers instructional information about numerical techniques which are useful for OR research problems and real-world applications on such issues.

The modeling of electricity generation plays a crucial role in investment and long-term planning in power systems, primarily due to the significant volatility associated with wind and solar energy sources. Nevertheless, forecasting wind speeds for wind turbines based on weather conditions over an extended period is difficult and not feasible. This study provides long-term projections for wind power generation derived from a 2 MW wind turbine for the upcoming year and subsequent years utilizing the Multivariate Adaptive Regression Splines (MARS), Artificial Neural Networks (ANN), Classification And Regression Tree (CART), Linear Regression (LR) and Random Forest (RF) techniques. The research is carried out in two distinct phases. During Phase 1 all considered predictive methods are compared. The research demonstrates that the MARS algorithm is a robust and efficient predictor for wind-based power generation, exhibiting strong competitiveness in its performance. During Phase 2, the MARS algorithm is employed to forecast the future 30-year wind power generation capacity lifespan hourly for nine cities in Texas, USA. It is projected that El Paso and Dallas will witness a mean rise of 8.6% in wind power capacity over three decades, while the remaining seven cities are anticipated to have an average decline of 7.7%. Hence, it is imperative to do a comprehensive and extended evaluation employing the MARS technique compared to ANN, CART, LR and RF before installing a wind turbine. This analysis would serve as a crucial resource for investors, engineers, and researchers involved in decision-making processes on wind turbine projects. [ABSTRACT FROM AUTHOR]

This study explores a Robust, Risk-aware, Resilient, and Sustainable Closed-Loop Supply Chain Network Design (3RSCLSCND) to tackle demand fluctuation like COVID-19 pandemic. A two-stage robust stochastic multiobjective programming model serves to express the proposed problems in formulae. The objective functions include minimising costs, CO2 emissions, energy consumption, and maximising employment by applying Conditional Value at Risk (CVaR) to achieve reliability through risk reduction. The Entropic Value at Risk (EVaR) and Minimax method are used to compare with the proposed model. We utilise the Lp-Metric method to solve the multiobjective problem. Since this model is complex, the Lagrange relaxation and Fix-and-Optimise algorithm are applied to find lower and upper bounds in large-scale, respectively. The results confirm the superior power of the model offered in estimating costs, energy consumption, environmental pollution, and employment level. This model and algorithms are applicable for other CLSC problems. [ABSTRACT FROM AUTHOR]