Least cost scheduling for repetitive projects

Abstract

Dynamic programming has been utilized to optimize scheduling of repetitive projects. The optimization criterion of existing techniques, however, is limited to minimizing the project duration and does not account for the impact of such optimization on the project cost. While the minimization of the project duration may reduce the project indirect costs, it does not guarantee a minimum total cost for the project. In practice, minimizing the overall cost of a project is frequently regarded to be more important than minimizing its duration. The objective of this paper is to present a flexible model that incorporates cost in the optimization process. In addition, the model is capable of considering the weather impact and the learning curve effect in the optimization process, simulating two important factors affecting productivity on this class of projects. The model utilizes dynamic programming and performs the solution in two stages: first, a forward path to identify local minimum conditions; and then a backward path to ensure a global minimum state. A numerical example from the literature is analyzed in order to demonstrate the use of the model, test its validity, and illustrate the significance of incorporating cost, weather impact, and the learning curve effect in the optimization process. Key words: planning and scheduling, repetitive projects, linear scheduling, cost optimization, dynamic programming, learning curve effect, weather impact.