A Sample-path Optimization Approach for Optimal Resource Allocation in Stochastic Projects

Abstract

The purpose of this research has been to develop an optimization method that can be utilized to determine optimal resource allocations for projects in an uncertain (stochastic) environment. The project under consideration is modeled as a stochastic activity network (SAN) where the workload requirements for each activity are assumed to be random with some specified distribution. Our concern is the time/cost tradeoff problem where the project manager can affect the duration of each activity in the project by allocating more or less of a scarce resource to the competing activities (at some cost). The objective is therefore to minimize the total expected cost of the project by assigning the resource to the various activities while simultaneously respecting precedence relationships among the activities and constraints on the total resource available. In particular we would like to analyze stochastic projects of reasonable size (>100 activities) and provide an optimization tool that achieves results in sufficiently small amount of time to make its application practical for realistic project management scenarios.