The effect of learning and cost optimization on line of balance scheduling - Zahran, Kareem Essam (AUC Knowledge Fountain, 2014)
The increase in populations throughout the past years introduced the need for improving and increasing countries’ infrastructure. Moreover, there has been an increasing demand for dwelling units ranging from small economic housing blocks to high-rise buildings and residential villa compounds. These projects are known by their repetitive nature, where a constant unit is repeated several times. Scheduling techniques that consider the learning effect as an influencing parameter in calculating productivity rates can improve the effectiveness of the scheduling process. This achieves more realistic forecasts resulting in optimum utilization of resources and better control during implementation phases. Linear scheduling techniques are being used in scheduling projects of a repetitive nature. The aim of this research is to develop a framework which helps schedulers include the learning effect and cost/time optimization in line of balance scheduling during the planning and monitoring phases of construction projects. This framework achieves several objectives such as (1) improving the performance of the LOB scheduling by considering the learning effect to develop more realistic schedules, (2) supporting decision makers in achieving schedules that would optimize time and cost, and (3) incorporating actual performance data on site by continuously updating the learning rates based on actual data extracted from monitoring activities on site. A spreadsheet modeling tool was used to apply the developed approach. Furthermore, a hypothetical case study was applied to verify the developed approach and to prove that the model gives accurate results. The developed tool proved to provide the planner/scheduler with near optimum costs, durations, and resources due to applying learning and cost/time optimization. One of the major achievements of this tool is that it presents a realistic LOB Schedule that is continuously updated to reflect both current and forecasted productivity rates of activities until the end of the project. Furthermore, the developed model was validated through (1) applying it on two real life case studies where their outputs were compared to actual results and (2) distributing a questionnaire to construction experts to get their feedback on the developed framework and model. Results of the validation show that the system is robust, flexible, gives accurate results and is considered useful for the industry.