Solving fuzzy project scheduling problems with parallelized kangaroo algorithm

Today, as a result of ongoing globalization, companies do not have to only compete on the local market but worldwide. Globalization also caused an increase in product variety while shortening product life. Consequent confusion and uncertainty made predicting the future and planning accordingly much more difficult. To survive in such environment, companies should make the right decisions, and this is increasing the importance of project scheduling. Because classic planning and scheduling methods fail to keep up in such an environment, to make right decisions and to draw accurate plans for future requires an unconventional approach. Using exact methods to solve these problems is not feasible, due to required long computational time and realism of the project. An alternative way to solve this uncertainty is the fuzzy set theory, which is a natural way to solve uncertainty. By using this method, it is possible to make more effective and efficient schedules for a project. In this thesis, the problem of a new product development project with fuzzy activity durations and fuzzy resource requirements studied. Fuzzy set theory is used to model the uncertain and flexible temporal information, and parallelized kangaroo algorithm is used to determine minimum schedule risk, and find a starting time for each activity, that maximizes the minimum satisfaction value of all constraints of all activities. The proposed method may help the project managers to choose a schedule in an uncertain scheduling environment with the lowest probability of being late.