Pricing and incentive design in applications of green technology subsidies and revenue management
Pricing and incentive design in applications of green technology subsidies and revenue management
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发表时间：
2012
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通讯作者：
R. Lobel
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作者：
R. Lobel
本论文探讨了企业和政策制定者在决定如何为产品定价以及如何恰当地激励消费者时面临的三个问题。在论文的第一部分，我们关注一家企业在面临不确定需求时试图动态调整价格以实现利润最大化的情况，例如航空公司销售机票或酒店预订房间。特别是，我们开发了一个基于稳健抽样的优化框架，该框架能将最坏情况下的遗憾最小化，并根据需求的实现情况动态调整价格。我们提出了一个易于处理的使用直接需求样本的优化模型，该解决方案的置信水平可从所使用的样本数量中获得。我们通过一系列数值实验以及一个使用机票数据的案例研究进一步证明了这种方法的适用性。
在论文的第二部分，我们提出了一个居民消费者采用太阳能光伏技术的模型。利用这个模型，我们为政策制定者开发了一个框架，以找到最优补贴水平，从而实现期望的采用目标。技术采用过程遵循一个离散选择模型，该模型因信息传播和边干边学等网络效应而得到加强。我们通过对德国太阳能市场的一项实证研究验证了该模型，在研究中我们估计了模型参数，生成了采用预测，并展示了如何解决政策设计问题。我们利用这个框架表明，德国目前的政策在近期可以通过提高补贴以及更快地逐步取消补贴计划来加以改进。
在论文的第三部分，我们在不确定需求的情况下，在一个两阶段博弈环境中对政府和一个行业参与者之间的相互作用进行建模。我们展示了决策的时机将如何影响生产水平和补贴计划的成本。特别是，我们表明当政府承诺一项固定政策时，它会向供应商发出信号，使其在规划期开始时生产更多。因此，平均而言，灵活的政策对政府来说比承诺的政策成本更高。
论文导师：乔治娅·佩拉基斯
头衔：威廉·F·庞兹管理学教授、运筹学与运营管理教授
This thesis addresses three issues faced by firms and policy-makers when deciding how to price products and properly incentivize consumers. In the first part of the thesis, we focus on a firm attempting to dynamically adjust prices to maximize profits when facing uncertain demand, as for example airlines selling flights or hotels booking rooms. In particular, we develop a robust samplingbased optimization framework that minimizes the worst-case regret and dynamically adjusts the price according to the realization of demand. We propose a tractable optimization model that uses direct demand samples, where the confidence level of this solution can be obtained from the number of samples used. We further demonstrate the applicability of this approach with a series of numerical experiments and a case study using airline ticketing data. In the second part of the thesis, we propose a model for the adoption of solar photovoltaic technology by residential consumers. Using this model, we develop a framework for policy makers to find optimal subsidy levels in order to achieve a desired adoption target. The technology adoption process follows a discrete choice model, which is reinforced by network effects such as information spread and learning-by-doing. We validate the model through an empirical study of the German solar market, where we estimate the model parameters, generate adoption forecasts and demonstrate how to solve the policy design problem. We use this framework to show that the current policies in Germany could be improved by higher subsidies in the near future and a faster phase-out of the subsidy program. In the third part of the thesis, we model the interaction between a government and an industry player in a two-period game setting under uncertain demand. We show how the timing of decisions will affect the production levels and the cost of the subsidy program. In particular, we show that when the government commits to a fixed policy, it signals to the supplier to produce more in the beginning of the horizon. Consequently, a flexible policy is on average more expensive for the government than a committed policy. Thesis Supervisor: Georgia Perakis Title: William F. Pounds Professor of Management Professor of Operations Research and Operations Management