LINEAR OPTIMIZATION PROBLEM WITH DATA UNCERTAINTY
<p align ="justify">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 situatio...
Saved in:
| Main Author: | |
|---|---|
| Format: | Final Project |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/31515 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | <p align ="justify">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<p align ="justify"> |
|---|