A Resource Leveling Model Based On Genetic Algorithms: Activity Splitting Allowed

The goal of resource leveling is to minimize the deviation between the resource requirements and the desired resource profile to prevent problems that caused by fluctuation of required resources. It is a common problem and arises frequently in real situations and therefore has been studied numerous times. Almost in all of these studies and the resulting solutions, there exist a common element, which is once an activity is started, it cannot be stopped and restarted again. That is, it cannot be split. In many instances in actual construction, there exist activities that can be split. So, it seems to be very useful to develop a model for resource leveling problems that allows certain activities to be split. An important interesting property that such a model should have is its simplicity. By simplicity we mean its ease of use by inexpert users without strong mathematical background. This paper presents a model based on genetic algorithms to level resources that permits selected activities to stop and restart. This splitting of activities results in improvement to the leveling solution that is traditionally achieved when splitting is not allowed. Examples are presented that illustrate the improvement in the solution obtained from the proposed model compared to models that do not allow splitting and compares the result to that obtained using commercially available software. The model presented here is very efficient and effective. Therefore, it can be used in real situations to automatically solve resource leveling problems efficiently to obtain effective results.