Approximation Algorithms for the Discrete Time-Cost Tradeoff Problem
Mathematics of Operations Research
Activity time-cost tradeoffs under time and cost chance constraints
Computers and Industrial Engineering
A Stochastic Branch-and-Bound Approach to Activity Crashing in Project Management
INFORMS Journal on Computing
Two-Stage Fleet Assignment Model Considering Stochastic Passenger Demands
Operations Research
Bounding the expected time-cost curve for a stochastic pert network from below
Operations Research Letters
A heuristic approach for resource constrained project scheduling with uncertain activity durations
Computers and Operations Research
Computers and Operations Research
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Resource allocation in project networks allows for the control of the processing time of an activity under time-cost tradeoff settings. In practice, project decisions are made in advance when activity durations are still highly uncertain. Given an activity-on-node project network and a set of execution modes for each activity, we consider the problem of deciding how and when to execute each activity to minimize project completion time or total cost with respect to captured activity durations. The inherent stochasticity is represented using a set of discrete scenarios in which each scenario is associated with a probability of occurrence and a realization of activity durations. In this paper, we propose a path-based two-stage stochastic integer programming approach in which the execution modes are determined in the first stage while the second stage performs activity scheduling according to the realizations of activity durations, hence, providing flexibility in the scheduling process at subsequent stages. The solution methodology is based on a decomposition algorithm which has been implemented and widely tested using a large number of test instances of varying size and uncertainty. The reported computational results demonstrate that the proposed algorithm converges fast to the optimal solution and present the benefits of using the stochastic model as opposed to the traditional deterministic model that considers only expected values of activity durations.