MING ZHOU



MING ZHOU
ASSOCIATE PROFESSOR



        Dr. Ming Zhou received a Ph.D. in Systems and Industrial Engineering from the The University of Arizona. Currently he is an associate professor in the IMT department, and taught courses in computer graphics, reliability and failure analysis, statistics for experimental research, advanced computer aided design, graphical analysis calculus, experimental design and process analysis, etc. Dr. Zhou has also taught courses in engineering statistics, industrial engineering methods, CAD/CAM, computer programming before he join the faculty of ISU. He has also given workshop lectures on design of experiment (DOE) and computer aided design/modeling. Dr. Zhou is actively conducting applied research in several areas (see description below). He is a member of IIE and INFORMS.

 

CURRICULUM DEVELOPMENT

  • Dr. Ming Zhou has proposed the following courses for undergraduate or graduate studies:
  • Graduate students and senior undergraduates are also welcome to take independent study course with Dr. Zhou, and may select a topic from, but not limited to, the following areas: computer integrated manufacturing (CIM), CAD (geometric modeling, graphics, analysis), computer simulation, design/analysis of production/manufacturing systems, statistical quality control (SPC), design of experiment (DOE), industrial application of artificial intelligence (e.g., expert systems), production planning and inventory control, project management and scheduling, applied optimization, fuzzy-logic and neural network modeling, decision-making analysis, integrated design and analysis of distribution packaging.

CURRENT RESEARCH INTERESTS/ACTIVITIES

  • Decision models and optimization in computer-aided process planning (CAPP)
  • Fuzzy logic and optimization models for the QFD implementations in design and quality planning/improvement
  • Network flow models/heuristics in planning QC operations in general manufacturing systems
  • Neural network models for package performance prediction and package design
  • Data mining and rule formation with neural networks, knowledge extraction from massive data/database
  • Modeling and analysis of engineering design process/methodology
  • Artificial Intelligence (AI) in the design and control of engineering systems
  • Simulation modeling/analysis of discrete production/manufacturing systems
  • AI and knowledge-based system for design and optimization of food extrusion process
  • Combining GIS and GPS technology with operations research in production and distribution planning

GRANTS/PROPOSALS

  • Research grant from University Research Council (URC), $2350, 1996. Process Selection and Tolerance Allocation for Rotational Surfaces.
  • Research grant from URC, $2800, 1997. A Fuzzy Set Theory Based Methodology for Target Value Problem in Quality Improvement.
  • 1997 PIF Award from Indiana State University, $4160. Integrated Design, Modeling and Analysis in Distribution Packaging.
  • Research grant from Kellogg Institute, Inc., $58,740. April, 1999. AI and Knowledge-based System for Design and Optimization of Food Extrusion Process.
  • Lilly Faculty Fellowship, $5000, May, 1999. Investigation and Development of Data Mining Models for Analyzing First-Year Student Profile Data.

SELECTED PUBLICATIONS

  • "Integrated Operations Planning and Manufacturing Cell Formation", Proceedings, ORSA Technical Session On Manufacturing Management, June, 1994, pp. 96-103.
  • "Formation of General GT Cells: An Operation-based Approach", International Journal of Computers and Industrial Engineering, Vol. 34, No. 1, pp. 147-157, 1998.
  • "Formation of Independent Flow-line Cells based on Operations Requirement and Machine Capability", IIE Transactions, Vol. 30, pp.319-329, 1998. This paper received "Best Paper Award" from IIE Transactions.
  • "A Parallel Station Heuristic For The Mixed-Model Production Line Balancing Problem", International Journal of Production Research, Vol. 35, No. 11, 1997, pp. 3095-3105.
  • "Process Selection and Tolerance Allocation for Rotational Surfaces", Proceedings, 6th Industrial Engineeing Research Conference, May, 1997, pp. 942-947.
  • "Fuzzy Logic Based Models for Quality Planning and Improvement", Intelligent Engineering Systems Through Artificial Neural Networks, Volume 7, 1997, pp. 311-316.
  • "A Fuzzy Set Theory Based Approach for Target Improvement Problem in Design and Quality Planning", International Journal of Industrial Engineering, Vol. 5, No. 4, 1998, pp. 278-287.
NEW COURSES
  • Reliability and Failure Analysis____ Introduce the concept and principles of reliability, failure modes and mechanisms, with emphasis on the applied techniques to determine time-to-failure, failure rate, and reliability of conponents/systems, including probability distribution models, construction of reliability bath-tube curve (RBTC), failure mode and effect analysis (FMEA), fault tree analysis (FTA), etc. This course is designed for both undergraduate and graduate students (Master) as well as engineers/managers in service.
  • Experimental Design and Process Analysis ____ Introduction to the design and analysis of engineering experiments for industrial applications. Topics include single factor models, blocking design, factorial design and regression analysis. Emphasis is given to the modeling of industrial application problems. Computer aided modeling and analysis is also introduced. The course is designed for graduate students (particularly QSS major) as well as engineers/managers in service.
  • Reliability, Maintainability and Serviceability____ This course is designed for graduate students, particularly doctoral students in Quality Systems Specialization (QSS). It emphasizes the methodology used in analyzing manufacturing and other production systems based on reliability, maintainability and serviceability concepts and principles.

CONTACT INFORMATION

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