Logical conditions to link the various elements of a business decision are very common practice in leadership positions. For example, a company can only to and from warehouses that are open ship; patients need MRIs can only service in hospitals, the MRI machines have, in relation to an old power station can decide to close it, or retrofit, but you of course can not retrofit a closed system. The list goes on. The quantitative modeling of such situations is a natural step to use if statements such as IF (a … Read More »

Logical conditions to link the various elements of a business decision are very common practice in leadership positions. For example, a company can only to and from warehouses that are open ship; patients need MRIs can only service in hospitals, the MRI machines have, in relation to an old power station can decide to close it, or retrofit, but you of course can not retrofit a closed system. The list goes on. The quantitative modeling of such situations, a natural step is to use if statements such as IF (a warehouse in the city of N is open, then we can be to / from the ship, otherwise no shipments made to / from a warehouse in N are) . In optimization models, however if statements nonlinearity lead with all the associated challenges. Fortunately, almost all logical conditions are modeled linearly with binary variables. This note describes some useful techniques to do that.
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Anton Ovchinnikov
5 pages.
Release Date: 11 April 2012. Prod #: UV6347-PDF-ENG
With binary variables represent logical conditions in Optimization Models HBR case solution