Resource allocation is problem common to all providers of logistical services. Even a small percentage of resource allocation process efficiency, given the same underlying resources, will have significant impact on the bottom line. As long as real time data about shifting business conditions are available as inputs, a more accurate model can be deployed to enhance the efficiency of available resources.

For example:

• In the case of a container terminal operator, the model is about minimizing the crane operation costs by locating and stacking containers optimally according to the berth schedule.
• In the case of an airline courier, the model is about maximizing revenue by allocating cargo space according to the demand of different routes and freight categories.

In these cases the computational efficiency of the model is key to a quicker turnaround time or a higher accuracy and complexity of the problem at hand. The efficient use of parallel computing can solve these models, and others, in order to make the mose out of your limited resources.