The effects of learning, forgetting, and relearning on decision rule performance in multiproject scheduling

Product development occurs in multiproject environments where preemption is often allowed so that critical projects can be addressed immediately Becau se product development is characterized by time-based competition, there is pressure to make decisions quickly using heuristics methods that yield fas t project completion. Preemption heuristics are needed both to choose activ ities for preemption and then to determine which resources to use to restar t preempted activities. Past research involving preemption has ignored any completion time penalty due to the forgetting experienced by project person nel during preemption and the resulting relearning time required to regain lost proficiency. The purpose of this research is to determine the impact o f learning, forgetting, and relearning (LFR) on project completion time whe n preemption is allowed. We present a model for the LFR cycle in multiproje ct development environments. We lest a number of priority rules for activit y scheduling, activity preemption, and resource assignment subsequent to pr eemption, subject to the existence of the LFR cycle, for which a single typ e of knowledge worker resource is assigned among multiple projects. The res ults of the simulation experiments clearly demonstrate that LFR effects are significant. The tests of different scheduling, preemption, and resource r eassignment rules show that the choice of rule is crucial in mitigating the completion time penalty effects of the LFR cycle, while maintaining high l evels of resource utilization. Specifically, the worst performing rules tes ted for each performance measure are those that attempt to maintain high re source utilization. The best performing rules are based on activity critica lity and resource learning.