Scheduling by iterative partition of bottleneck conflicts

Abstract

The author describes conflict partition scheduling (CPS), a novel methodology that constructs solutions to scheduling problems by repeatedly identifying bottleneck conflicts and posting constraints to resolve them. The identification of bottleneck conflicts is based on a capacity analysis using a stochastic simulation methodology. Once a conflict is identified, CPS does not attempt to resolve it completely; instead it introduces constraints that merely decrease its criticality. By reducing the amount by which each scheduling decision prunes the search space, CPS tries to minimize the chance of getting lost in blind alleys. The effectiveness of CPS was demonstrated by an experimental analysis that compared CPS to two current scheduling methods: micro-opportunistic constraint-directed search and min-conflict iterative repair. CPS is shown to perform better than both on a standard benchmark of constraint satisfaction scheduling problems