Modeling bidding decisions and bid markup size for construction projects: A fuzzy approach. (August 2022)

• Record Type: Journal Article
• Authors: Zaqout, Ibrahim S.; Islam, Muhammad Saiful; Hadidi, Laith A.; Skitmore, Martin
• Is Part Of: Engineering applications of artificial intelligence. Volume 113(2022)
• Publication Date: 2022-08
• Subjects: Construction contract -- Auction -- Bidding decisions -- Bid markup -- Expert judgment -- Fuzzy logic; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2022.104982
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Previous studies of construction contract auction bidding have mainly focused on finding factors affecting the markup and decision to bid (d2b), without considering expert weight and critical factors as input variables for estimating the size of markup needed. This study develops a 3-step Mamdani-type of Fuzzy Inference System (FIS) identifying critical factors and predicting markups for construction projects. The first step models the wights of 31 construction experts based on their characteristics (i.e., experience and academic qualifications). The second step models frequency, severity, and importance weights for each factor to find the rank and priority of the factors, revealing the critical factors to be current workload, project (contract) size, need for work, availability of labor and staff required, project owner, and duration. The third step takes the importance weight of each factor from the previous step and the contractor's evaluation of the frequency of the factors as input variables to predict the bid markup. The model is demonstrated and tested with two actual construction projects having actual markups of 20% and 45%, and the predicted markups are found to be 26.3% and 42% respectively, which ensures reliable outcomes in assessing contractors' bidding decisions. It is a novel model that can simultaneously identify critical factors and predict an optimal markup in assisting contractors' d2b for the construction auctions and developing risk management plans. Future research can optimize this model by incorporating competitors' bids and enhancing prediction accuracy to guarantee the lowest price with a reasonable profit margin. Highlights: Robust Mamdani-type fuzzy inference model for estimating project markup. A small group of experts in a project can assess the markup factors. The model can rank critical factors and estimate project markup together. Only a factor's occurrence probability is required as input estimating project markup.