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One of the greatest challenges in service optimization is increasing customer service levels while reducing operational costs. It is often difficult – if not impossible – to achieve one without sacrificing the other.

The reason is the inherent conflict between sending the most cost-effective resource and sending the best qualified one. Service operations must also take into account a number of other factors when dispatching field resources, including geography, parts required, breaks, unforeseen emergencies, service levels, and other constraints. A relatively small workload of 100 service calls in a single eight hour day makes it virtually impossible to effectively balance all of the simultaneous calculations and decisions required to ensure happy customers at the lowest possible cost.

Some scheduling systems with less evolved optimization methods solve this problem by narrowing down the possible choices in a sequential step by step approach, where each step attempts to identify the best technician and/or time, based on a single rule. The result is that later in the process, only those technicians remaining as candidates from the previous step can be considered. When one considers even a simple example of overtime versus geographic location, it is obvious that this approach often results in sub-optimal schedules.