This study proposes a simulation-based and risk-aware multi-objective optimization framework to determine the capital structure for private financing in infrastructure projects to align the interests between the lender and the shareholder. Monte Carlo simulation is used for project valuation and conditional value at risk is introduced to measure the inherent risk associated with the expected project performance. A multi-objective optimization problem is formulated, where the decision variables are the amounts of diversified debt instruments, the first objective is to maximize the net present value while minimize the conditional cash flow at risk from the shareholder's perspective, and the second objective is to maximizes the rate of return on the loans while minimize the default risk of shareholders from the lender's perspective. Fast non-dominated sorting genetic algorithm NSGA-Ⅱ is used to solve the problem. The case of Indiana Toll Road is presented as a numerical study to prove the concept.
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机译：本研究提出了一种基于模拟和风险感知的多目标优化框架，以确定基础设施项目中私人融资的资本结构，以对准贷方和股东之间的利益。 Monte Carlo仿真用于项目估值，引入风险的条件价值，以衡量与预期项目绩效相关的固有风险。制定了多目标优化问题，其中决策变量是多元化债务工具的数量，第一个目标是最大限度地提高净现值，同时最大限度地减少股东视角下风险的条件现金流量，第二个目标是最大化贷款的回报率，同时最大限度地减少贷款人视角的默认风险。快速非主导的分类遗传算法NSGA-Ⅱ用于解决问题。印第安纳省收费路的案例作为证明该概念的数值研究呈现。
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