Assoc. Prof. Dr Mohammad Ali Saniee Monfared , Alzahra university, Iran
Assoc. Prof. Dr. Mohammad Ali Saniee Monfared is an accomplished academic and industry expert with over 20 years of experience. 🌍🔧 With a Ph.D. in Manufacturing and Mechanical Engineering from the University of Birmingham, UK (1997), and dual MSc degrees in System Engineering and Industrial Engineering, he bridges academia and industry seamlessly. 📊🛠 He has worked in tire, automotive, electronics, and cosmetic manufacturing. His expertise spans risk assessment, predictive analytics, and reliability engineering, highlighted by groundbreaking projects in Iran’s gas and steel industries. 🚀📉 A passionate educator, he teaches advanced courses in reliability, stochastic processes, and maintenance planning. 🎓✨
Publication Profile
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Qualification
Assoc. Prof. Dr. Mohammad Ali Saniee Monfared is a seasoned professional with over 20 years of experience spanning industry and academia 🌟📚. He excels at transforming complex engineering challenges into predictive analytics solutions 📊⚙️. Dr. Monfared is known for crafting statistical models to address intricate problems and developing testbeds to verify and validate these solutions using advanced machine learning techniques 🤖📐. His expertise lies in bridging theoretical concepts with practical applications, delivering impactful results. Dr. Monfared’s dedication to innovation and structured problem-solving makes him a highly respected figure in his field 🚀✨.
Education
Assoc. Prof. Dr. Mohammad Ali Saniee Monfared is a distinguished academic with a Ph.D. from the University of Birmingham, UK (1997) in Manufacturing and Mechanical Engineering 🎓⚙️. He earned his first MSc in Industrial Engineering & Operations Research from Sharif University in Tehran, Iran (1991) 📊🇮🇷, and his second MSc in System Engineering from the University of Regina, Canada (1994) 🌍🔧. With extensive expertise in engineering and operations, Dr. Monfared has significantly contributed to his field through research and teaching. His international education underscores his commitment to advancing knowledge and innovation in engineering disciplines 🌟📚.
Experience
Assoc. Prof. Dr. Mohammad Ali Saniee Monfared boasts diverse industrial experience, including 8 years in tire and rubber manufacturing, 2 years in the automotive sector, 2 years in electronics, and 2 years in cosmetic and soap production 🏭🚗📟🧼. Now a respected academic, he teaches graduate courses such as Reliability Engineering, Advanced Maintenance Planning, Stochastic Processes, and RCM 📚🔧. His undergraduate teachings include Engineering Statistics, Inventory Planning, and Advanced Operations Research 📊📐. Dr. Monfared’s rich professional background enriches his lectures, combining practical expertise with academic excellence, making him a vital contributor to engineering education 🌟.
Recent Projects with Industries
Assoc. Prof. Dr. Mohammad Ali Saniee Monfared showcases exceptional problem-solving and industry relevance in his recent projects. 🌟 His groundbreaking “Multi-perspective Risk Assessment in the Gas Industry” (2021-2023) analyzed a city gate station from 12 stakeholder viewpoints, a first in the field. 🚧📊 In 2022, he developed an innovative risk-based maintenance model for a 35-year-old city gate station, enhancing safety and mitigating catastrophic risks. 🔧⚙️ Additionally, his 2020 project on reliability-based maintenance for a seal gas compressor improved reliability by 15% using a redundancy model. 🚀📈 These achievements highlight his ingenuity and commitment to advancing engineering practices. 👨🔬✨
Research Focus
Assoc. Prof. Dr. Mohammad Ali Saniee Monfared’s research primarily focuses on network analysis, reliability, and optimization, with applications spanning academic performance, power grids, water distribution systems, and road networks. 🖧🔍 His work explores vulnerability assessment using complex network theory 🌐, optimization techniques ⚙️, and adaptive systems 📈. Dr. Monfared’s interdisciplinary contributions include advancing sensor placement for contamination detection 🚰, controlling multi-electron dynamics in molecular systems 🧬, and developing frameworks for manufacturing automation 🤖. His research integrates statistical mechanics, evolutionary algorithms, and time-series analysis to enhance system reliability and efficiency 🔬📊. His impactful publications reflect innovative solutions in engineering and science. 🚀✨
Publication Top Notes
Network DEA: an application to analysis of academic performance
Topology and vulnerability of the Iranian power grid
A complex network theory approach for optimizing contamination warning sensor location in water distribution networks
Comparing topological and reliability-based vulnerability analysis of Iran power transmission network
Controlling the multi-electron dynamics in the high harmonic spectrum from N2O molecule using TDDFT
Design of integrated manufacturing planning, scheduling and control systems: a new framework for automation
Fuzzy adaptive scheduling and control systems
A new adaptive exponential smoothing method for non-stationary time series with level shifts
An improved evolutionary algorithm for handling many-objective optimization problems
Road networks reliability estimations and optimizations: A Bi-directional bottom-up, top-down approach