Risk-Based Decision Making Support for Construction Corporate Resource Management

• Main Author: Sheykhi, Reza
• Format: Others
• Published: FIU Digital Commons 2016
• Subjects: Decision Making; Construction; Resource Management; Optimization; Profitability; Civil Engineering; Construction Engineering and Management
• Online Access: http://digitalcommons.fiu.edu/etd/3039; http://digitalcommons.fiu.edu/cgi/viewcontent.cgi?article=4016&context=etd

Competitive bidding typically challenges contractors to stay in business by reducing contingency and limiting profit margin, which imposes more prudent resource utilization and allocation decisions during both planning and construction phases of projects. Many of these decisions must be made considering uncertainties that affect resource production and construction performance through several factors such as weather, managerial practices, job-type, and market conditions, etc. Construction decision makers will therefore have varied approaches to deal with these uncertainties based on their risk utility or perception. This research presents the development of a model for investigating the impact of risk-based approaches on construction network outcomes. The current study contributes to development of a model that enables corporate managers to understand the impact of different resource utilization and sharing policies on the overall outcome of their project and to select among optimum planning solutions that satisfy their profit margin and capital limitations. This research also enables corporate decision makers to have more realistic estimates for the profitability of their company, and understand consequences of their decisions in short and long term. Findings of this research provide decision makers with different solutions for profitability of their corporation based on non-dominated optimal time-cost trade-offs, and also broader perspective on how overall time and budget limitations, as well as risk perceptions, can affect the decision-making process. The model is verified and the results are validated through acquiring data from actual large scale construction projects in South Florida.