Greedy algorithms for the multi-capacitated metric scheduling problem

Abstract

This paper investigates the performance of a set of greedy al-gorithms for solving the Multi-Capacitated Metric Scheduling Problem (MCM-SP). All algorithms considered are variants of ESTA (Earliest Start Time Algorithm), previously proposed in [3]. The paper starts with an analysis of ESTA's performance on diffierent classes of MCM-SP problems. ESTA is shown to be effiective on several of these classes, but is also seen to have difficulty solving problems with heavy resource contention. Several possibilities for improving the basic algorithm are investigated. A ffirst crucial modiffication consists of substituting ESTA's pairwise analysis of resource conflicts with a more aggregate and thus more powerful Minimal Critical Set (mcs) computation. To cope with the combinatorial task of enumerating mcss, several approximate sam-pling procedures are then defined. Some systematic sampling strategies, previously shown effiective on a related but diffierent class of scheduling problem, are found to be less effiective on MCM-SP. On the contrary, a randomized mcs sampling technique is introduced, forming a variant of ESTA that is shown to be quite powerful on highly constrained problems.