Many problems in this world that have these conflicting objectives that may be solved. But many of these problems are often treated as a single-objective optimization problem with the objective to transfer all the constraints. The general form of Multi-Objective Optimization are as follows :
where :
: vector of n decision variables
: variable limit and restrict each decision variable xi. This limitation is the decision variable space D
: constraint functions
In a multi obejctive programming approach, optimal solution is a solution that delivers maximum results for all destinations simultaneously. In general, there is no optimal solution to the issue of multi-purpose. In cases like this, which might be an efficient solution. Efficient solution is a solution that can not be increased further achievements in one of the goals without lowering the achievement of other objectives (trade off). Area-efficient solution is not a point of particular solutions but it is a set of solution points that form a line or area efficient solutions. So in other words, the best solution or solutions “optimum” in the context of multi-objective (an efficient solution) is something to be desired (preferred), understood, accepted, supported and implemented by decision makers with confidence.
This is one of those things that sounds like theory gone mad with some overplay of long words to try to make things complex but when you get your head into it the concept makes sense. I am sure it needs to be simplified a little before presenting it to decision makers though!
Sorax recently posted..PC TV Software
[...] rational consideration, because Programa linear analysis is limited to a single destination (single objective function). In the real world issues facing decision-makers with different goals and objectives as [...]