An Interval Parameter Two-Stage Stochastic Approach to Optimize Budget and Schedule in Construction Management

An interval parameter two-stage stochastic model for project budgeting and scheduling is developed to dealing with the uncertainties residing in the project management. The model focuses on the probability distribution in activity durations and the uncertainties expressed as intervals in costs. It minimizes the inexact costs (direct costs, indirect costs and penalties) with reference to the specified project completion time and the durations of activities estimated from two-stage stochastic programming. The proposed approach for budgeting and scheduling is a hybrid of two stage stochastic programming and inexact optimization. Solutions obtained from the model will provide a reasonable crashing time plan to accomplish given projects on specific time and come up a lowest cost plan for tardiness. This approach can effectively reflect the interactive relationships among all the uncertain system components. The plan provides useful decision support for project managers through these post-optimality analyses. The developed model is applied to a case study to illustrate its feasibility of dealing the actual project management decision problems. The paper implements the model to a gas pipeline construction project with specified completion time and milestone allocating tasks as the case study. The proposed model provides a systematic framework that facilitates the decision making process and enable project managers to justify the range of the solutions when the decision variables are intervals. *Corresponding author: Gordon Huang, Institute for Energy, Environment and Sustainable Communities, University of Regina, 3737 Wascana Parkway, Regina, SK, S4S 0A2, Canada; Tel: 306-585-4111; E-mail: [email protected] Received March 02, 2016; Accepted April 25, 2016; Published April 27, 2016 Citation: Zhu J, Huang G (2016) An Interval Parameter Two-Stage Stochastic Approach to Optimize Budget and Schedule in Construction Management. J Civil Environ Eng 6: 228. doi:10.4172/2165-784X.1000228 Copyright: © 2016 Zhu J, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.