Welcome to Wood 492 – Modelling for Decision Support.
Instructor: Dr. Taraneh Sowlati
Contact Info: I have an open door policy and would be happy to help students outside the classroom. My contact info:
Room: FSC 2931
Lectures: Monday, Wednesday 11:30 – 13:00 (FSC 1001)
Lab: Friday 11:00 – 13:00 (FSC 1404, 1406)
Teaching Assistant: Krishna Malladi
Room: FSC 2943, Email: firstname.lastname@example.org
Useful books and references:
- Williams, H.P. Model Building in Mathematical Programming. Fifth Edition. Wiley. 2013. UBC Library Electronic Resource.
- Baker, K. R. Optimization Modeling with Spreadsheets. Second Edition. Wiley. 2011. UBC Library Electronic Resource.
- Ramamurthy P., Operations Research, New Age International Publisher, 2007, Available online: http://site.ebrary.com/lib/ubc/detail.action?docID=10367718
- Dykstra, D.P. Mathematical Programming for Natural Resource Management. McGraw-Hill. 1984.
- Winston, W.L., Operations Research: Applications and Algorithms, Third Edition, ITP, 1994.
Applications of mathematical modeling, optimization, and simulation in forest planning and manufacturing; formulating models and interpreting results for decision support.
The main objective of the course is to introduce the concepts and techniques of mathematical programming and their applications in forestry. The main emphasis is on developing suitable models, using proper tools to solve them, and analyzing and presenting the results for decision making.
Upon successful completion of this course, students will have the skills to:
- Develop and solve mathematical models (linear programming, integer programming, mixed integer programming, and multi-objective programming models) to optimize forest planning and wood manufacturing activities
- Conduct sensitivity analysis to determine the impact of changes in model’s parameters on the final solution
- Incorporate uncertainty into decision making models using Monte Carlo Simulation
There will be a mid-term and a final exam on the material presented in the course, including the readings, and class discussions/presentations. To pass the course, students need to pass the final exam, in case they fail the final exam, their course mark will be the same as their final exam mark. All the lecture notes are available for downloading from http://wood492.forestry.ubc.ca. This will save you most of the note taking, but will require that you take an active part in the classroom dialogue.
In order to succeed in this course, students need to be actively engaged in class discussions and activities, and facilitate the learning of others. Class attendance is mandatory.
Weekly Lab assignments
The lab section provides practical application of the lecture material to decision support problems. Students will develop and solve mathematical programming and simulation models to support decision making process. Lab assignments are normally completed during the lab sessions. Due to space limitation in the computer lab, assignments will be done in pairs. Lab attendance is required.
Case study and presentations
Students will work in groups and give a presentation during the class on Tuesday. The details and requirements will be handed out to students and explained in the class.
Late submission policy
Please note that when a deadline is set for the submission of lab assignments, you must submit them on or before that deadline or there will be a significant penalty, 30% off!
|Case study and presentation||10%|
Introduction to the course
– Course syllabus and expectations
Introduction to operations research and decision support systems
|Friday Sept. 8: No lab|
Sept. 11, 13, 15
– Develop models
– Solve LP models with 2 variables graphically
– Use Excel Solver to solve LP models
|Friday Sept. 15: First lab|
Sept. 18, 20, 22
– Shadow prices
– Range of feasibility
– Range of optimality
Sept. 25, 27, 29
Applications of LP
– Timber harvest scheduling optimization (Model I)
Oct. 2, 4, 6
Applications of LP
– Veneer production
Oct. 11, 13
Oct. 11: Case study presentations
Oct. 16, 18, 20
|Oct. 16, 18: Case study presentations|
Oct. 23, 25, 27
Oct. 27: Midterm
Oct. 30, Nov. 1, 3
– Minimum Spanning Tree
– Shortest Path
– Maximum Flow
|Oct. 30, Nov. 1: Case study presentations|
Nov. 6, 8, 10
– Goal programming
|Nov. 6, 8: Case study presentations|
Nov. 15, 17
– Monte Carlo Simulation and uncertainties in models
|Nov. 15: Case study presentations|
Nov. 20, 22, 24
|Guest Speaker/Other models (if time permits)||Nov. 20, 22: Case study presentations|
Nov. 27, 29, Dec. 1
Nov. 27, 29:
Case study presentations
Dec. 1: Review