Development of a systematic approach for uncertainty assessment in scheduling multiple repetitive construction processes by using fuzzy set theory

A growing portion of the U.S. Interstate highway system needs major rehabilitation and/or reconstruction. Many highway segments are located in or near major urban corridors serving high traffic volumes throughout the day and performing as critical links for local and through traffic. Shutting down a highway segment for rehabilitation and/or reconstruction work for extended periods of time will have as consequences considerable inconvenience and increased delays for road users, major community disruption, and significant economic loss. Applications of procedures to improve planning, scheduling, and control of highway construction and maintenance projects can reduce these inconveniences. In order to achieve significant reductions in highway construction project times a broader, systematic approach to the problem is proposed. Planning and scheduling of transportation construction projects is facilitated using the linear scheduling method (LSM) because of its visual and intuitive capabilities. One of the main problems with LSM is that to date there is no formal method developed that could allow LSM to determine uncertainties in time completion. In engineering, uncertainties can be attributed mainly to ambiguity, likelihood, approximations and inconsistency. Several formal techniques for managing the different types of uncertainty have been developed but there has not been any consensus on the appropriateness of such techniques so far. It seems that there is no best theory of uncertainty and therefore the choice of the most appropriate technique depends upon the specific problem. In response to these problems the fuzzy set theory is utilized to propose an efficient and systematic procedure for uncertainty assessment. The assessment procedure proposed is a heuristic tool that can process linguistic terms as used by experts allowing the quantification of expert knowledge in estimating and assessing uncertainty in scheduling repetitive construction projects.