WhoAmI & Research Focus
Bryan A. Norman Ph.D.
Associate Professor
Department of Industrial Engineering
University of Pittsburgh
1033 Benedum Hall
Pittsburgh, PA. 15261
412-624-9841, 412-624-9831 (fax)
banorman@pitt.edu
Research Focus: Systems modeling, analysis, and optimization using computational intelligence (evolutionary algorithms, tabu search, artificial neural networks) combined with classical operations research methods. Application areas of interest include: production and personnel scheduling, process planning, facility layout and design, and manufacturing process control.
Ph.D. in Industrial and Operations Engineering
University of Michigan, Ann Arbor, 1995
Dissertation Title: "Scheduling Using the Random Keys Genetic Algorithm."
National Science Foundation Fellow
Masters of Science in Industrial Engineering
University of Oklahoma, Norman, 1991
Centennial Research Fellow
Bachelor of Science in Industrial Engineering
University of Oklahoma, Norman, 1988
National Merit Scholar
Current Research Activities
Facility Layout
I am currently studying the optimization of facility layouts with unequal size departments, input and output stations in each department, and aisle structures. A new method has been proposed which examines the entire design problem simultaneously, rather than sequentially. I am also enhancing this problem to consider uncertainty in the anticipated product demand which will impact the flow through the facility. This is joint work with Alice Smith.
Scheduling
I am currently investigating several topics in scheduling including:
1. Chemical plant scheduling including job sequencing and the allocation of storage tanks between different reactor vessels. This is leading to general work in sequencing and buffer allocation.
2. I am also extending my research concerning the scheduling of multiple spindle CNC machines to include tooling allocation and process planning decisions.
3. I am studying a personnel scheduling problem for a company that provides corporate training. The problem has characteristics found in exam timetabling problems but includes additional monetary objectives that complicate the problem.
Process Control
I am currently involved in a project to improve control of a hot rolling process. This involves developing a model of the process and determining the key inputs and outputs of the process. This project is still in the early stages.
Other research projects
Assembly Line Balancing
Network Reliability
Design of Wireless Communication Networks
Norman, Bryan A. and James C. Bean, "A Random Keys Genetic Algorithm for Job Shop Scheduling Problems," Engineering Design and Automation Journal, Vol. 3, No. 2, 1997.
Norman, Bryan A. and Alice. E. Smith, "Considering Risk Trade-offs in Unequal Area Block Layout Design," 6th Industrial Engineering Research Conference, May 17-18, 1997, Miami Beach, FL.
Norman, Bryan A. and James C. Bean, "Operation Sequencing and Tool Assignment for Multiple Spindle CNC Machines," 1997 IEEE International Conference on Evolutionary Computation, April 13-16, 1997, Indianapolis, IN.
Norman, Bryan A. and Alice E. Smith, "Random Keys Genetic Algorithm With Adaptive Penalty Function For Optimization Of Constrained Facility Layout Problems," 1997 IEEE International Conference on Evolutionary Computation, April 13-16, 1997, Indianapolis, IN.
Norman, Bryan A. "Scheduling Flowshops With Finite Buffers And Sequence Dependent Setup Times". Technical Report 96-08, Department of Industrial Engineering, University of Pittsburgh, 15261, 1996.
Norman, Bryan A. and James C. Bean, "Scheduling Operations on Parallel Machine Tools," Technical Report 95-09, Department of Industrial Engineering, University of Pittsburgh, 15261, 1995. Under revision IIE Transactions.
Norman, Bryan A. and James C. Bean, "Random Keys Genetic Algorithm for Complex Scheduling Problems," Technical Report 96-7, Department of Industrial Engineering, University of Pittsburgh, 15261, 1996. Under revision Naval Research Logistics.
Norman, Bryan A., "Scheduling Using the Random Keys Genetic Algorithm," Unpublished doctoral dissertation, University of Michigan, 1995.
Bean, James C., Atidel. B. Hadj-Alouane, and Bryan Norman, "Rescheduling Disrupted Production Systems," Proceedings of the 1994 NSF Design and Manufacturing Grantees Conference, 1994.
Norman, Bryan A. and James C. Bean, "A Genetic Algorithm Code for Scheduling Problems: Parallel Computing Version," Technical Report 94-23, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, 48109-2117, 1994.
Norman, Bryan A. and James C. Bean, "A Genetic Algorithm Code for Scheduling Problems: Serial Computing Version," Technical Report 94-22 , Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, 48109-2117, 1994.
Leemis, Lawrence M. and Bryan. A. Norman, "Software for the Analysis of Survival Data," Proceedings of the 23rd Annual Midwest Section Meeting, American Society of Engineering Education, April 1988.
Funded Research
Norman, Bryan A. and Richard E. Billo, "An Intelligent System for Hot Rolling Process Control," Ben Franklin Technology Center of Western Pennsylvania.
Bryan A. Norman, "A Comprehensive Scheduling and Forecasting System for KnowledgeSoft," KnowledgeSoft Inc., $12,086.
For Heuristic Optimization here are the notes to download: complexity-heuropt.doc. Sorry for the delay in preparing them. I look forward to discussing tabu search and complexity with you in lecture!
Undergraduate
Course Outline
This course provides an introduction to facility layout and location. The course topics tentatively include: an overview of why facility layout is important, activity relationships, space and personnel requirements, manufacturing flow patterns, layout procedure types - both construction and improvement algorithms, manual and computerized layout techniques, modular facilities (QAP), single and multiple facility location, and material handling system design. Some of these topics may be deleted or added depending upon time constraints. We will also discuss the use of the LayOPT software packages for constructing facility layouts.
Prerequisites: IE 1021
Course Outline
This course provides an introduction to general operations research (OR) methodology, with primary emphasis on linear programming (LP). The course topics tentatively include: a brief history and philosophy of OR; modeling and formulation of linear programs; graphical solution methods for linear programs; the simplex method; sensitivity analysis; transportation and assignment problems; network models; and integer programming. Some of these topics may be deleted or added depending upon time constraints. We will also discuss the use of software packages, LINDO and/or STORM, for solving linear programs.
Prerequisites: Math 0250, IE 1021
To get to my Engr 0020 home page select here Engr 0020 Home Page.
Course Description
This course is designed for students majoring in engineering. Topics include: data analysis, probability, random variables, discrete and continuous probability distributions, estimation, hypothesis testing, correlation, linear regression, and statistical process control.
Course Objectives
1. To acquaint the students with the fundamental concepts of probability and statistics.2. To develop an understanding of the role of statistics in engineering.
3. To provide an understanding of the processes by which real-life statistical problems are analyzed.
4. To familiarize students with computer-based statistical analysis through the Statistix software package.
Prerequisites: None
Graduate
To get to my IE 3082 home page select here IE 3082.
Course Outline
Scheduling machines, operations, and personnel continues to be an important consideration for many companies in both manufacturing and service settings. In this course we will examine solution methodologies for a wide range of different scheduling problems. A general outline of the topics to be covered is provided below. The course material will include recent papers from the literature. There will also be a term project that will permit students to both apply their understanding of existing solution methodologies and to explore new solution methodologies.
1. Introduction
2. Single-machine sequencing with independent jobs
3. Optimization methods for the single-machine problem
4. Heuristic methods for the single-machine problem
5. Earliness and tardiness penalties
6. Extensions of the basic model
7. Parallel-machine models
8. Flow shop scheduling
9. Lot streaming procedures for the flow shop
10. The job shop problem
11. Scheduling groups of jobs
12. Simulation models for the dynamic job shop
13. Stochastic scheduling models
Prerequisites: Students taking this course should have a general knowledge of optimization theory and linear programming similar to that found in IE 2001.
Course Goals
- Develop an awareness of network flow formulations for a variety of real life problems.
- Understand the use of network flow algorithms to solve such problems.
- Develop the ability to program network flow problems.
- Develop the ability to understand and implement research papers related to network analysis.
Course Outline
- Network Formulations of Some Problems
- Combinatorial Optimization
- Graph Theory Concepts
- Linear Programming and Network Flows
- Shortest Path Algorithms
- Maximum Flow Algorithms
- Minimum Cost Flow Problem
- Minimum Spanning Tree Algorithms
- Convex Cost Flow Problems
- Generalized Network Flows
- Traveling Salesman Problem
Prerequisites: IE 2001
Married to Melissa M. Wotring-Norman, June 1987. We recently celebrated our eleventh anniversary!
(Scan in a picture of Melissa later)
I have two great kids!
Alexander, born 10-23-93.
Jacob, born 12-15-95.
Here are the brothers together at home.
Here are the brothers having fun on a tractor.
When I have free time I like to- See my wife
- Play with my kids
- Play basketball
- Play chess