AI Helps Solve Complex Problems Faster and Better

The optimization challenge of successfully routing holiday packages is so complex that specialized software is frequently used to find a solution.

A mixed-integer linear programming (MILP) solver divides a large optimization issue into smaller chunks.

The procedure is so time-consuming that companies frequently have to halt the programme mid-stream, accepting a solution that isn't perfect but is the best that could be achieved in a certain length of time.

MIT and ETH Zurich found a critical intermediary stage in MILP solvers that has so many alternative answers that unravelling it takes a tremendous amount of time, slowing the whole process.

To simplify this procedure, the researchers applied a filtering strategy, followed by machine learning to determine the best answer for a given type of problem.

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