Project scheduling based on resource leveling using fuzzy ranking and genetic algorithm

Abstract

Project planning and scheduling is often addressed in the paradigm of combined and fuzzy optimization, which is largely owing to the inherent features of the combination and the ensuing uncertainty in determining the variables, none more significant than the timetables and deadlines of activities. As such, the purpose of the current study is to present a novel metaheuristic model for the aforementioned problem by applying fuzzy ranking and genetic algorithm on the resource leveling indicator. This method will be case-studied on fuzzy numbers needed to express uncertain variables in the real world. Project planning and scheduling using resource leveling and the fuzzy approach is of paramount significance to the industry, as it has shown to greatly ensure the proper and effective use of resources. This research seeks to propose a new model for project scheduling in which the uncertainty of the timetable of the activities and resource levelling are examined at the same time. To generate the initial population in the genetic algorithm, parallel fuzzy prioritization method is used to optimally level the resources, while fuzzy theory is further employed to model the uncertainty of the duration of activities.