Strength and adaptability of problem-space based neighborhoods for resource-constrained scheduling

In this paper, Resource Constrained Scheduling (RCS) consists of scheduling activities on scarce resources, each activity may require more than one resource at a time, and each resource is available in the same quantity throughout the planning period. This paper described a methodology for RCS that can be easily adapted to consider different regular measures of performance. The solution approach is local search using a recent development published in the literature; namely, problem-space based neighborhoods. Computational results are encouraging when searching these spaces using simple local search techniques. Further improvements are explored through the use of a genetic algorithm. In both cases, close-to-optimal solutions are found for standard problems from the literature. The adaptability of the methodology is demonstrated using makespan and mean tardiness as performance measures.