Resource Optimization in Business Processes

Traditionally, the optimal resource allocation in a business process is determined by manually exploring a number of options, but whether this leads to the optimal solution is questionable; it is possible that an unexplored option produces even better results. Therefore, an automated approach is needed that explores all possible options in a structured manner to find the best one. To address this need this paper proposes three iterations of a search strategy for resource optimization that searches through the space of possible resource allocations to find the best one. The performance of the search strategy is evaluated theoretically with guided experiments. The experiments show that the proposed search strategies scale very well and obtain the optimal value in only a fraction of the number of steps needed by an exhaustive exploration of the solution space. In this manner, this paper also forms the basis for future research into resource optimization with extensions into multi-process optimization, advanced, and operational resource optimization.