Improved Strategy for Resource Allocation in Repetitive Projects Considering the Learning Effect

Abstract

Considering the learning effect while planning and scheduling repetitive construction projects can help provide a realistic forecast of their duration and resource requirements. This paper has developed an improved learning curve (LC) to replace the log-linear LC for monitoring improvement in workers’ performance. The improved LC assumes that the manual work time of the specific unit converges to a nonzero constant rather than zero under a large number of repetitions. It also takes into account the influence of workers’ prior experience and machinery in the learning process. An improved line-of-balance (LOB) model is then presented by integrating the proposed LC and a mechanism of resource allocation. The proposed LOB model can make use of the learning effect to minimize the total resource usage of a project, while meeting the requirements for work continuity and the target deadline of each activity. An illustrative example has been cited to demonstrate the capability of the model. The technique can help planners use the learning effect to get a more realistic and optimal schedule about resource utilization.