Hands-On Mathematical Optimization with Python

In all the optimization problems discussed so far, we treated the quantities in the problem description as exact, but, in reality, they cannot always be trusted or assumed to be what we think. Uncertainty might negatively affect solutions to an optimization problem in the following forms:

• Estimation/forecast errors (increasingly important in an ML-driven world):

  • – in a production planning problem, future customer demand is a forecast;

  • – in a vehicle routing problem, travel times along various roads are real-time updated forecasts;

  • – in a wind farm layout problem, power production levels are based on wind forecasts.

• Measurement errors:

  • – a warehouse manager might have errors in the data records regarding current stock levels;

  • – the concentration level of a given chemical substance is different from expected.

• Implementation errors:

  • – a given quantity of an ingredient is sent to production in a chemical company, but due to device errors, a slightly smaller amount is actually received;

  • – electrical power sent to an antenna is subject to the generator’s errors.