Determination Optimal Service Level on BOT Transport Project Contract Based on Zhang Model

Subject Areas : Financial Economics

محمد کیان ¹ , بابک حاجی کریمی ² , محمدمهدی مظفری ³

1 - گروه مدیریت صنعتی، مدیریت مالی، واحد ابهر، دانشگاه آزاد اسلامی، ابهر، ایران.
2 - گروه مدیریت صنعتی،واحد ابهر،دانشگاه آزاد اسلامی ،ابهر ،ایران
3 - استادیار، دانشکده علوم اجتماعی، دانشگاه بین المللی امام خمینی(ره)، قزوین، ایران.

Received: 2021-04-01 Accepted : 2021-06-07 Published : 2021-08-23

Keywords: G28, M41, L12, JEL Classification: M14,

Abstract :

In many of developed and developing countries, nowadays, B.O.T  conctract is a way in which government performe their essential project as transport one through observing investment from privait firms, instead these privit firms earn the main investment as well as their profit from user fees. For many BOT transport projects, the government imposes service level requirement on the private firm so that project capacity and stochastic demand can be better matched. Service level can be defined as the probability of project capacity satisfying uncertain demand. Due to high risck of this kind of contaracts, the governmet, for many B.O.T transport projects, imposes service level requirement on the private firm so that project capacity and stochastic demand can be better matched. Service level is defined as the probability of project capacity satisfying uncertain demand. In this research as a determination optimal service level on B.O.T transport project contract based on Zhang model (Case Study: Tehran Fleet Electronic Ticket Contract) is conducted. The results suggest that the presence of service level requirement improves B.O.T h user fee and project capacity while decreases firm profit. Moreover, two formulas, to predict the servive level based on onther requlations, have been generated from the data of the project, using Gene EXperssion Programming through MATAL. The correlation coefficients of proposed relationship were relatively high, indicating the dependence of service level to project capacity, user fee, privit firm’s prfite, the number of users, demand uncertainty.

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