Decision Sciences Journal
Volume 30, Number 1
Winter 1999
The Effects of Learning, Forgetting, and Relearning on Decision
Rule Performance in Multiproject Scheduling
Robert Ash
Business Department, College of Business and Economics, University
of Idaho, Moscow, ID 83844-3161, bobash@uidaho.edu
Dwight E. Smith-Daniels
Department of Management, College of Business, Arizona State
University, Tempe, AZ 85287-4006, dwight.smith-daniels@asu.edu
ABSTRACT. Product development occurs in multiproject
environments where preemption is often allowed so that critical
projects can be addressed immediately. Because product development
is characterized by time-based competition, there is pressure
to make decisions quickly using heuristics methods that yield
fast project completion. Preemption heuristics are needed both
to choose activities for preemption and then to determine which
resources to use to restart preempted activities. Past research
involving preemption has ignored any completion time penalty
due to the forgetting experienced by project personnel during
preemption and the resulting relearning time required to regain
lost proficiency. The purpose of this research is to determine
the impact of learning, forgetting, and relearning (LFR) on project
completion time when preemption is allowed. We present a model
for the LFR cycle in multiproject development environments. We
test a number of priority rules for activity scheduling, activity
preemption, and resource assignment subsequent to preemption,
subject to the existence of the LFR cycle, for which a single
type of knowledge worker resource is assigned among multiple
projects. The results of the simulation experiments clearly demonstrate
that LFR effects are significant. The tests of different scheduling,
preemption, and resource reassignment rules show that the choice
of rule is crucial in mitigating the completion time penalty
effects of the LFR cycle, while maintaining high levels of resource
utilization. Specifically, the worst performing rules tested
for each performance measure are those that attempt to maintain
high resource utilization. The best performing rules are based
on activity criticality and resource learning.
Subject Areas: Heuristics and Project Management. |