The Service Delivery Scheduling (SDS) problem is an extension of the classic Job Shop Scheduling (JSS) problem, a well-known NP-hard problem in optimization even when static data are provided. SDS extends the problem domain by introducing the notion of different classes of resources to be scheduled, each with its own scheduling and availability characteristics. The telecommunications industry provides an example domain with a need to optimize SDS on an ongoing basis for scheduling the provisioning and delivery of services to customers. Much effort and research to solve such problems in both artificial intelligence and operations research fields have focused on finding a near-optimal solution for a specific instantiation of simplified, real-world data. However, the business climate is very dynamic, especially in the telecommunications industry with the recent introduction of more competitive forces in the marketplace. A Decision Support System (DSS) is needed to guide the business decisionmaker in determining resource needs and projected service provisioning times. This approach also allows the businessperson to model potential scenarios and select the best-fit solution given the current business climate. A prototypical DSS is described and implemented within a parallel programming environment. Two alternative algorithms for schedule building are explored, with variations on the treatment of transaction priorities and the level of parallelization. The concepts of high-level and low-level overconstraining are discussed with appropriate priority modelling applied using user-designed priority classes. This user-centered, evolving solution approach supports the whole notion of a DSS by leveraging the user's domain knowledge as means to narrow the solution space.