### 99

The number of ECE ILLINOIS' faculty members.

Title | Rubric | Section | CRN | Type | Hours | Times | Days | Location | Instructor |
---|---|---|---|---|---|---|---|---|---|

Techniques for Engrg Decisions | ECE307 | G | 39322 | LEC | 3 | 0930 - 1050 | T R | 3015 ECE Building | George Gross |

Modeling of decisions in engineering work and the analysis of models to develop a systematic approach to making decisions. Fundamental concepts in linear and dynamic programming; probability theory; and statistics. Resource allocation; logistics; scheduling; sequential decision making; siting of facilities; investment decisions; application of financial derivatives; other problems for decision making under uncertainty. Case studies from actual industrial applications illustrate real-world decisions. Course Information: Prerequisite: ECE 210; credit or concurrent registration in ECE 313.

Power and Energy Systems

This course is concerned with modeling of decisions and analysis of models to develop a systematic approach to making decisions. This course introduces probability theory as the fundamental mathematical basis for the development of techniques for solving typical problems faced in making engineering decisions in industry and government. The aim of this course is to teach students to think structurally about decision-making problems. Extensive use of case studies gets students involved in real world situations.

- Nature of engineering decisions; structuring of decisions; role of models; interplay of economics and technical/engineering considerations; decision making under certainty and uncertainty; good decisions vs. good outcomes; tools
- Resource allocation decision making using the linear programming framework; problem formulation; duality; economic interpretation; sensitivity analysis; interpretation of results
- Scheduling and assignment decisions using network flow concepts; transshipment problem formulation and solution; application to matching decisions; network optimization; scheduling application
- Sequential decision making in a dynamic programming framework; nature of dynamic programming approach; problem formulation; solution procedures
- Probability theory; random variables; probability distributions; expectation; conditional probability; moments; convolution
- Statistical concepts; data analysis; statistical measures; estimation
- Application of probabilistic concepts to the modeling of uncertainty in decision amking; modeling of the impacts of uncertainty; application to siting, investment and price volatility problems
- Decision making under uncertainty; decision trees; value of information; uses of data; sensitivity analysis and statistics

Decision topics include research allocation, logistics, scheduling, sequential decision making, siting of facilities, investment decisions and other problems for decision making under uncertainty.

- Resource allocation decision making using the linear programming framework: problem formulation; basic approach; duality; economic interpretation; sensitivity analysis; interpretation of results
- Scheduling and assignment decisions using network flow concepts: trans-shipment problem formulation and solution; application to matching decisions; network optimization; scheduling applications
- Sequential decision making in a dynamic programming framework: nature of dynamic programming approach; problem formulation; solution procedures; key limitations
- Probability theory: random variables; probability distribution; expectation; conditional probability; moments; convolution
- Statistical concepts: data analysis; statistical measures; estimation
- Application of probabilistic concepts to the modeling of uncertainty in decision making: modeling of the impacts of uncertainty; applications to siting, investment and price volatility problems
- Decision making under uncertainty: decision trees; value of information; uses of data; sensitivity analysis and statistics
- Case Studies

- Nature of engineering decisions; structuring of decisions; role of models; interplay of economics and technical/engineering considerations; decision making under certainty and uncertainty; good decisions vs. good outcomes; tools
- Resource allocation decision making using the linear programming framework; problem formulation; duality; economic interpretation; sensitivity analysis; interpretation of results
- Scheduling and assignment decisions using network flow concepts; transshipment problem formulation and solution; application to matching decisions; network optimization; scheduling application
- Sequential decision making in a dynamic programming framework; nature of dynamic programming approach; problem formulation; solution procedures
- Probability theory; random variables; probability distributions; expectation; conditional probability; moments; convolution
- Statistical concepts; data analysis; statistical measures; estimation
- Application of probabilistic concepts to the modeling of uncertainty in decision amking; modeling of the impacts of uncertainty; application to siting, investment and price volatility problems
- Decision making under uncertainty; decision trees; value of information; uses of data; sensitivity analysis and statistics
Topics:

Decision topics include research allocation, logistics, scheduling, sequential decision making, siting of facilities, investment decisions and other problems for decision making under uncertainty.- Resource allocation decision making using the linear programming framework: problem formulation; basic approach; duality; economic interpretation; sensitivity analysis; interpretation of results
- Scheduling and assignment decisions using network flow concepts: trans-shipment problem formulation and solution; application to matching decisions; network optimization; scheduling applications
- Sequential decision making in a dynamic programming framework: nature of dynamic programming approach; problem formulation; solution procedures; key limitations
- Probability theory: random variables; probability distribution; expectation; conditional probability; moments; convolution
- Statistical concepts: data analysis; statistical measures; estimation
- Application of probabilistic concepts to the modeling of uncertainty in decision making: modeling of the impacts of uncertainty; applications to siting, investment and price volatility problems
- Decision making under uncertainty: decision trees; value of information; uses of data; sensitivity analysis and statistics
- Case Studies and Presentations

This course is an elective for both electrical and computer engineering majors. The goals are to provide the students with systematic approaches to making decisions, and to provide case studies for illustrations of these concepts.

**A. After the first four weeks of class, the students should be able to do the following:**

1. Perform fundamental resource allocation analysis using linear programming (a,c,e,k)

2. Make scheduling and assignment decisions using network flow concepts (a,c,e)

3. Model the decision process in a programming framework (a,j,k)

**B. After the first eight weeks of class, the students should be able to do all of the items** **listed under A above, plus the following:**

1. Perform sequential decision making in a dynamic programming environment (a,c,e)

2. Use deterministic programming techniques to solve decision making problems (a,c,e,k,j,m)

3. Perform fundamental probability analysis (a, c, e, l)

**C. After the first 12 weeks of class, the students should be able to do all of the items listed** **under A and B above, plus the following:**

1. Perform basic statistical analysis including estimation (a, c, e, l)

2. Apply probabilistic concepts to the modeling of uncertainty in decision-making (a, c, e, l)

3. Apply conditional probability to engineering decision-making problems (a,c,e,l,k,m)

**D. After the full 15 weeks of class and laboratory experiment #4, the students should be able to do all of the items listed under A, B, and C above, plus the following:**

1. Perform decision making under certainty and uncertainty for case studies (a, c, e, k)

2. Present results of a decision making process (b, d, f, g)

3. Prepare presentations of case studies and demonstrate the skills to effectively articulate key points and convince others of the soundness of recommended actions (a,b,c,d,e,f,g,h,i,j,h,l,m)

3/14/2016by George Gross

DEPARTMENT OF ELECTRICAL

AND COMPUTER ENGINEERING

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