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A practical introduction to constraint programming using CP-SAT and Python
We discuss constraint programming, an approach to solve discrete optimization problems, and illustrate how it works with a scheduling code example implemented in Python with the CP-SAT solver.
Or you're a global shipping company that assigns packages to their delivery trucks and has to choose the best routes in order to save gas and reduce driver overtime. The store owner is not keen on paying overtime wages, so they wish to cap each employee's schedule to a maximum of 40 hours per week (10 work shifts): With the infeasible status returned to us previously, we are forced to inform Emma that she can't take the whole week off, otherwise it would be impossible to fill the schedule without violating some of the other constraints.
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