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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]