Environmental Systems Analysis
Course Code
TL4108
Number of Credits
2
Semester
Course Type
Study Material
| Study Material | Depth |
|---|---|
| Introduction: explanation of lecture material, procedures, exams and bibliography | Express |
| Basic Principles of Systems: understanding systems, systems analysis techniques, reference systems, Delphi method | Express |
| Methods and Simulation: model basics, model dimensions, mathematical models, deterministic models, probabilistic models, dynamic models, simulation versus analytical solutions model validity. | Express |
| Optimization: decision variables, target functions, equations & inequalities constraints, mathematical solutions of linear and non-linear models. | Expert |
| Decision Analysis: decision making under certain conditions, with risk, and uncertain conditions (uncertainty); maximum criterion, minimax, probability theory, strategic Bayes, decision trees. | Expert |
| Dynamic Systems: feedback dynamics, structural systems, closed boundaries, feedback loops, symbol equations, levels and rates, information chains, Dynamo Compiler flow diagrams. | Expert |
| Dynamic Programs: program characteristics, model formulation, boundary equations/inequalities, model solutions, forward and backward dynamics; solution of the model. | Expert |
| Network Analysis: characteristics, model formulation, network analysis, algorithms, critical path, resource allocation. | Expert |
| Feasibility Analysis: technical, economic, financial, socio-political legal feasibility, sensitivity analysis. | Expert |
| Weighted Ranking Technique: determining model parameters (factors), alternative selection coefficients, decision making matrix. | Expert |
| Dynamic Systems: feedback dynamics, structural systems, closed boundaries, feedback loops, symbol equations, levels and rates, information chains, Dynamo Compiler flow diagrams. | Expert |
| Queue Model: characteristics, arrival process, queuing discipline service process, poison distribution, exponential distribution, model formulation, model solution. | Expert |
| Markov chains: linear algebra, probability theory, probability transition matrices, regular fundamental matrices and absorbing Markov. | Expert |
| Game Theory: Minimax – Miximin Strategy, Laplace Theory, Mixed Strategies and Dominant Expected Payoff, Brown's Algorithm. | Expert |
Graduate Learning Outcomes (GLO) carried by the course
| CPMK Code | Course Learning Outcomes Elements (CLO) |
|---|---|
| CPMK 1 | Students understand the dynamics of a system and technological forecasting of environmental conditions in the future |
Learning Method
- KBL, SBL, PBL, CBL.
Learning Modality
- Offline, Synchronous.
Assessment Methods
- Description, Presentation, Report.