LINEAR OPTIMIZATION PROBLEM WITH DATA UNCERTAINTY
Optimization problem under uncertainty is a problem that characterized with solution that we do not have full knowledge of the effects of the application of that solution. This kind of problem is one of the problems that often happen in real life. In this situation, a decision maker has to decide...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Subjects: | |
| Online Access: | https://digilib.itb.ac.id/gdl/view/33768 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Optimization problem under uncertainty is a problem that characterized with
solution that we do not have full knowledge of the effects of the application of
that solution. This kind of problem is one of the problems that often happen in real
life. In this situation, a decision maker has to decide on plan that gives the best
return. Stochastic programming is a method that can be used on the optimization
problem with data uncertainty. Parameter with uncertainty can be modelled as
random variable with probability. This paper, Dempster-Shafer Theory is used to
calculate the probability of each scenario of paramaters with data uncertainty
based on historical data. This model then developed to pessimistic approach and
optimistic approach to solve the linear optimization problem with data
uncertainty. Minimax regret is an approach that gives minimum regret on worstcase
scenario. These three approaches, pessimistic, optimistic, and minimax
regret, are options with the effects of the solution for a decision maker on deciding
the plan to take |
|---|