Managing risks in complex projects using compression strategies

This research considers the important question of how a project manager should address project uncertainty during project planning. Specifically, we consider two sources of significant project risk, a disruption of the project due to an exogenous random event and the endogenous risk posed by the inherent uncertainty of activity completion times.; We develop solutions to the disruptive event problem when activity times are deterministic and there is no due date, a due date with late completion penalty, and a due date with early completion bonus and late delivery penalty. While these problems could be solved using a variety of nonlinear programming approaches, we develop an aggregate approximation of project compression costs enabling us to develop an analytical solution and gain additional managerial insights.; We develop a new efficient algorithm for solving the standard time-cost tradeoff problem when activity times are stochastic. We demonstrate the significant differences between these compression strategies and the optimal strategies for the deterministic equivalent problem (an approach commonly taken to solve this problem in practice).; Finally, we develop an algorithm for determining compression strategies for minimizing the expected total costs of a project with stochastic activity times facing the threat of a random disruptive event. We develop an efficient solution to this stochastic disruptive event problem when the project has no due date, and when it faces a due date with a late delivery penalty. We show the solution to the standard stochastic time-cost tradeoff problem is a lower bound on the solution to the stochastic disruptive event problem, and demonstrate the conditions under which they are equal.