a multi-objectives weighting genetic algorithm for scheduling resource-constraint project problem in the presence of resource uncertainty

Scarce resources may cause delay in completion of a project. in this research, a multi-objective decision making model is developed for scheduling multi-mode resource constraint scheduling problem in the presence of uncertain resources. the objectives are profit, execution cost and completion time. to develop this idea, a multi-objective non-linear mixed integer programming model is developed where resource availability is not deterministic and expressed by triangular probability function. then, a multi-objective weighting genetic algorithm is proposed (mowga) which is flexible enough to be used in real projects. to verify the performance of the proposed method, a number of experiments are solved and results are analyzed. the outcomes indicated that while resource uncertainty increases, higher complexity in schedules is observed. it is also found that optimizing one objective function does not necessarily resulted in optimizing the others. the mowga is then successfully applied for a project with real data