Contractor Selection in Gas Well-drilling Projects with Quality Evaluation Using Neuro-fuzzy Networks

Contractor selection for a project is an important decision, one for the project time and cost, next for the quality obtained by the project. Although the project managers can easily determine the project time and cost, the quality is usually undefined especially for un-experienced managers. With a learnable property, an approach is first introduced in this paper to quantify the quality obtained for a gas well drilling project. Then, based on these three objectives (time, cost, and quality), a contractor selection problem is converted to an optimization problem. Next, the NSGA-II algorithm is utilized for solution. At the end, a sensitivity analysis is performed to select the parameters of the algorithm.