Advanced Models for Better Decisions
1. Problems require yes or no decisions. These models use variables that can only take on two values, zero or one, they are called binary variables.
Anything that has to do with choosing an item or not requires binary variables. Models with binary variables are part of what is called integer programming. Solving integer programming problems is exponentially more difficult than solving linear programming problems. This is why these problems are often solved using heuristic optimization. When a Heuristic search ends, it can not guarantee that the optimal solution has been found. However, these techniques normally find optimal solutions, or they provide very good approximations.
2. Predictive and prescriptive analytics can be combined in a single model. This will illustrate how techniques don't live in isolation, they can work together to tackle complex problems.
Here, combine simulation and optimization and create models that use predictive and prescriptive tools to deal with the reality of complex decision problems.
Anything that has to do with choosing an item or not requires binary variables. Models with binary variables are part of what is called integer programming. Solving integer programming problems is exponentially more difficult than solving linear programming problems. This is why these problems are often solved using heuristic optimization. When a Heuristic search ends, it can not guarantee that the optimal solution has been found. However, these techniques normally find optimal solutions, or they provide very good approximations.
2. Predictive and prescriptive analytics can be combined in a single model. This will illustrate how techniques don't live in isolation, they can work together to tackle complex problems.
Here, combine simulation and optimization and create models that use predictive and prescriptive tools to deal with the reality of complex decision problems.
