PROJECT SCHEDULING WITH MODULAR PROJECT COMPLETION ON A BOTTLENECK RESOURCE

In this paper, we model a research-and-development project as consisting of several
modules, with each module containing one or more activities. We examine how to schedule the
activities of such a project in order to maximize the expected profit when the activities have a probability
of failure and when an activity’s failure can cause its module and thereby the overall project
to fail. A module succeeds when at least one of its constituent activities is successfully executed. All
activities are scheduled on a scarce resource that is modeled as a single machine. We describe various
policy classes, establish the relationship between the classes, develop exact algorithms to optimize
over two different classes (one dynamic program and one branch-and-bound algorithm), and examine
the computational performance of the algorithms on two randomly generated instance sets.