The 8th International Conference on System Reliability and Safety
Sicily, Italy - November 20-22, 2024

Mohammad Ali Saniee Monfared, Alzahra University, Iran

 

Dr. Ali Monfared is an Associate Professor of Industrial Engineering at the University of Alzahra, Tehran, Iran. He has a background in Manufacturing and Industrial Engineering, Reliability and Maintenance Management, Risk Analysis, Multiple Objective Optimization and Game Theory.
In tandem with his academic commitments, Ali steadfastly maintained an unwavering connection with industry. He extended retraining opportunities to professionals across diverse sectors and provided advisory consultancy services to a spectrum of fields and industries, most recently including the automotive sector, steel plants, petrochemicals, and gas processing units.
Dr. Monfared's trajectory embodies a distinctive fusion of academic erudition and practical industry engagements, serving as a testament to his unwavering dedication to both education and the tangible application of knowledge across a myriad of sectors. Dr. Ali Monfared has published papers in academic journal such as Reliability Engineering and Safety System, Expert Systems with Application, OR Spectrum, Computer and Industrial Engineering, Applied Soft Computing, IET Generation, Transmission & Distribution.
(See https://scholar.google.com/citations?user=lpmL1BQAAAAJ&hl=en; or alternatively https://scimet.alzahra.ac.ir/MohammadAli_SanieeMonfared). A recent paper of relevance is Developing a Bi-Objective Maintenance Optimization Model for Process Industries by Prioritizing Resilience and Robustness Using Dynamic Bayesian Networks, Z. Alipour, M.A.S. Monfared, S.E. Monabbati, Computer and Industrial Engineering, January 2023, DOI: 10.2139/ssrn.4611884.

Speech Title: The Importance of Resilience and Robustness alongside Optimality in Maintenance Planning for the Process Industry
Abstract: Historically, when we talk about maintenance planning, our aim is to demonstrate the optimality of the plan within a mathematical model. The output of such a model determines the optimal timing for performing tasks such as inspection, repair, replacement, adjustment, and other services on equipment. Such an optimal plan should cover at least one of our major objectives in planning. This objective could be minimizing costs or risks, or maximizing reliability or availability. However, recent industrial experiences have shown that the optimality of such plans fails when exposed to environmental disruptions and dynamic load changes. As a result, maintenance planning requires simultaneous optimization, resilience, and robustness, which is the topic of this presentation.