Preface to the Special Issue on Computational Performance Analysis based on Novel Intelligent Methods: Exploration and Future Directions in Production and Logistics

• Autorzy: Goli Alireza , Tirkolaee Erfan Babaee , Weber Gerhard-Wilhelm

Identyfikatory

DOI

10.2478/fcds-2022-0005

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

CPA; artificial intelligence; Operational Research

• Wydawca: Wydawnictwo Politechniki Poznańskiej
• Czasopismo: Foundations of Computing and Decision Sciences
• Rocznik: 2022
• Tom: Vol. 47, No. 2
• Strony: 107--110
• Opis fizyczny: Bibliogr. 14 poz.

Twórcy

autor

Goli Alireza

• Department of Industrial Engineering, University of Isfahan, Isfahan, Iran

• https://orcid.org/0000-0001-9535-9902

autor

Tirkolaee Erfan Babaee

• Department of Industrial Engineering, Istinye University, Istanbul, Turkey

• https://orcid.org/0000-0003-1664-9210

autor

Weber Gerhard-Wilhelm

• Faculty of Engineering Management, Poznan University of Technology, Poland

• https://orcid.org/0000-0003-0849-7771

Bibliografia

• [1] Budi, H. S., et al. Development of an adaptive genetic algorithm to optimize the problem of unequal facility location. Foundations of Computing and Decision Sciences, 47, 2, 2022.
• [2] Dastan, M., Davoodi, S. M. R., Karbassian, M., and Moeini, S. Developing a mathematical model for a green closed-loop supply chain with a multi-objective gray wolf optimization algorithm. Foundations of Computing and Decision Sciences, 47, 2, 2022.
• [3] Ding, H., Liu, Y., Zhang, Y., Wang, S., Guo, Y., Zhou, S., Liu, C. Data-driven evaluation and optimization of the sustainable development of the logistics industry: case study of the Yangtze River Delta in China. Environmental Science and Pollution Research, 2022, 1-15.
• [4] Goli, A., Tirkolaee, E. B., Weber, G. W. A perishable product sustainable supply chain network design problem with lead time and customer satisfaction using a hybrid whale-genetic algorithm. In Logistics operations and management for recycling and reuse. Springer, Berlin, Heidelberg, 2020, 99-124.
• [5] Noer, Z., et al. A new model for scheduling operations in modern agricultural processes Foundations of Computing and Decision Sciences, 47, 2, 2022.
• [6] Pan, Y. H., Qu, T., Wu, N. Q., Khalgui, M., Huang, G. Q. Digital twin based real-time production logistics synchronization system in a multi-level computing architecture. Journal of Manufacturing Systems, 58, 2021, 246-260.
• [7] Ren, S., Zhang, Y., Liu, Y., Sakao, T., Huisingh, D., Almeida, C. M. A comprehensive review of big data analytics throughout product lifecycle to support sustainable smart manufacturing: A framework, challenges and future research directions. Journal of Cleaner Production, 210, 2019, 1343-1365.
• [8] Syah, R., et al. Design a multi period closed-loop supply chain program to supply recycled products. Foundations of Computing and Decision Sciences, 47, 2, 2022.
• [9] Syah, R., et al. Optimizing the multi-level location-assignment problem in queue networks using a multi-objective optimization approach. Foundations of Computing and Decision Sciences, 47, 2, 2022.
• [10] Syah, R., et al. Designing a green supply chain transportation system for an automotive company based on bi-objective optimization. Foundations of Computing and Decision Sciences, 47, 2, 2022.
• [11] Tikhonov, A. I., Sazonov, A. A., Kraev, V. M., and Kuzmina-Merlino, I. The Main Trends and Challenges in the Development of the Different Industries During the COVID-19 Pandemic. Foundations of Computing and Decision Sciences, 47,2, 2022.
• [12] Tirkolaee, E. B., Goli, A., Weber, G. W., Szwedzka, K. A novel formulation for the sustainable periodic waste collection arc-routing problem: A hybrid multi-objective optimization algorithm. In Logistics Operations and Management for Recycling and Reuse, Springer, Berlin, Heidelberg, 2020, 77-98.
• [13] Umeuzuegbu, J. C., Okiy, S., Nwobi-Okoye, C. C., Onukwuli, O. D. Computational modeling and multi-objective optimization of engine performance of biodiesel made with castor oil. Heliyon, 7, 3, 2021.
• [14] Verma, S., Sharma, R., Deb, S., Maitra, D. Artificial intelligence in marketing: Systematic review and future research direction. International Journal of Information Management Data Insights, 1, 1, 2021.