Project Scheduling: Time-Cost Tradeoff Problems

Abstract

We design and implement new methods to solve multiobjective time-cost tradeoff (TCT) problems in project scheduling using evolutionary algorithm and its hybrid variants with fuzzy logic, and artificial neural networks. We deal with a wide variety of TCT problems encountered in real world engineering projects. These include consideration of (i) nonlinear time-cost relationships of project activities, (ii) presence of a constrained resource apart from precedence constraints, and (iii) project uncertainties. We also present a hybrid meta heuristic (HMH) combining a genetic algorithm with simulated annealing to solve discrete version of multiobjective TCT problem. HMH is employed to solve two test cases of TCT.KeywordsPareto FrontProject ScheduleManagement ExperienceProject NetworkWeather ConditionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.