Project management for uncertainty with multiple objectives optimisation of time, cost and reliability

This research adopts an approach that uses computer simulation and statistical analysis of uncertain activity time, activity cost, due date and project budget to address quality and the learning process with regard to project scheduling. Since the learning process affects the scheduling problem, a Cobb–Douglas multiplicative power model is used to represent the relationship between the dependent variable, which is the standard deviation of activity time, and the independent variables, which are the cumulative trials and the mean of activity time. The mean value and standard deviation are used to randomly generate activity times for project scheduling analysis. Response surface methodology (RSM) is used in order to develop a rationale of the time-cost trade-off problem. The solutions found with RSM are optimised only for a single objective, such as project completion time, total project cost, completion time probability and total cost probability. Thus, multiple objectives for further optimisation become necessary and a limited project budget, restricted completion time, allowable total cost probability and acceptable completion time probability have to be considered at the same time as the learning effect. With response functions from RSM, compromise programming is adopted in order to formulate the proposed project scheduling problem for multi-objective optimisation.