Efficiently matching a continuous flow of assignments with resources represents a challenge in itself. Consideration often has to be given to conflicting objectives, such as response times versus resource costs, which creates an even more complex situation. Our optimal solution balances conflicting objectives and delivers tailormade schedules.

A representative mathematical model is created based on an analysis of the problem, which is expressed as an optimisation problem that we can then solve. The challenge lies in choosing the right data to describe the problem and ensuring that the model has the right level of detail. If the model is too detailed, it becomes too expensive to use and maintain. If the model is too simple, important aspects are omitted, rendering the result unusable. This is where our skills and experience are a great asset.

We solve challenges in scheduling that

1. Have conflicting objectives, such as resource costs versus delivery quality

2. Are complex and cannot be solved satisfactorily using other solutions

3. Involve expensive resources

4. Recur, so that savings are realised on an ongoing basis