A fuzzy clustering-based genetic algorithm approach for time–cost–quality trade-off problems: A case study of highway construction project

Recently government agencies have started to utilize innovative contracting methods that provide incentives for improving construction quality. These emerging contracting methods place an enormous pressure on the contractors to improve construction quality. For a general contractor, which subcontracts most tasks of a project and invites a number of bids, choosing an appropriate bid which satisfies the time, cost and quality of construction project is complex and challenging. To solve this problem involving conflicting objectives, a fuzzy clustering-based genetic algorithm (FCGA) approach is proposed in this paper. A case study of highway construction is used to demonstrate the applicability of the proposed approach. A comparative study is conducted over three test cases involving varying dimensions and complexities to test performance of the proposed FCGA against existing approaches. Results reveal that the FCGA is capable of generating better Pareto front than other existing approaches.