Bayesian Markov Chain Monte Carlo Model for Determining Optimum Tender Price in Multifamily Housing Projects

Abstract

This study presents a strategy model for determining the optimum tender price that reflects appropriate profit and risk contingencies in competitive tendering according to the Bayesian Markov Chain Monte Carlo (BMCMC) model. The BMCMC approach is known to be theoretically optimal for handling tender-price problems. The BMCMC model provides a practical solution that can reflect not only objective information but also subjective experience and knowledge. The BMCMC model allows contractors to estimate the tender price more accurately by reflecting the prior distribution function on key factors. Conclusively, this model was found to improve decision-making processes for setting an optimum tender price. An applied example showed that the proposed methods are feasible.