MILP issues have a remarkable number of possible arrangements. For example, say a mobile sales rep needs to track down the most limited way to visit a few urban communities and afterward return to their city of beginning. Assuming there are numerous urban communities which could be visited in any request, the quantity of potential arrangements may be more prominent than the quantity of iotas in the universe.
“These issues are called NP-hard, and that implies it is improbable there is an effective calculation to settle them. We can only hope to achieve suboptimal performance when the problem is large enough, as Wu explains.
A MILP solver utilizes a variety of methods and useful stunts that can accomplish sensible arrangements in a manageable measure of time.