Win-win pricing method for BOT projects using a simulation-based evolutionary optimization. Issue 2 (1st February 2020)

• Record Type: Journal Article
• Authors: Tavakoli, Nastaran; Hosseini Nourzad, Seyed Hossein
• Is Part Of: Construction management and economics. Volume 38:Issue 2(2020)
• Page Start: 157
• Page End: 171
• Publication Date: 2020-02-01
• Subjects: Build-Operate-Transfer -- multi-objective optimization -- evolutionary algorithms -- concession pricing -- simulation-optimization; Construction industry -- Management -- Periodicals; Construction industry -- Finance -- Periodicals; 338.47624
• DOI: 10.1080/01446193.2019.1657234
• Languages: English
• ISSNs: 0144-6193

Abstract

Abstract: Several failures have been reported in maintaining the Build-Operate-Transfer (BOT) projects viability through their life cycle. BOT contracts are one of the popular tools to respond to fund deficiencies in infrastructure projects, thus it is crucial to determine optimum concession terms for them. In previous research, the value of social benefits has mostly been neglected in the decision-making process, which results in unfair distribution of benefits between the government and the concessionaire. Therefore, a pricing framework based on a Simulation Multi-Objective Optimization (SMOO) method is developed in this research which includes the value of social benefits as well as the effects of uncertainties in BOT projects. The application of the model to a real-world project confirms that it presents a win-win solution in which the benefits of the two contracting parties are simultaneously maximized compared to the results obtained by using conventional methods. The results confirm the contribution of this research to reach a consensus on concession price and to keep the project viability as a result of its ability to cope with the high volatilities through simulation techniques. Furthermore, it can help the decision-makers to either seek a balance between both sides' interests or determine the concession price based on their preferential strategies.