Revenue Management uses data-driven modelling and optimization methods to decide what to sell, when to sell, to whom to sell, and for which price, in order to increase revenue and profit. Hotel Revenue Management is a very complex context characterized by nonlinearities, many parameters and constraints, and stochasticity, in particular in the demand by customers. It suffers from the curse of dimensionality (Bellman 2015 ): when the number of variables increases (number of rooms, number possible prices and capacities, number of reservation rules and constraints) exact solutions by dynamic programming or by alternative global optimization techniques cannot be used and one has to resort to intelligent heuristics, i.e., methods which can improve current solutions but without formal guarantees of optimality. Effective heuristics can incorporate “learning” (“reactive” schemes) that update strategies based on the past history of the process, the past reservations received up to a certain time and the previous steps in the iterative optimization process. Different approaches can be classified according to the specific model considered (stochastic demand and hotel rules), the control mechanism (the pricing policy ) and the optimization technique used to determine improving or optimal solutions. In some cases, model definitions, control mechanism and solution techniques are strongly interrelated: this is the case of dynamic programming, which demands suitably simplified problem formulations. We design a flexible discrete-event simulator for the hotel reservation process and experiment different approaches though measurements of the expected effect on profit (obtained by carefully separating a “training” phase from the final “validation” phase obtained from different simulations). The experimental results show the effectiveness of intelligent heuristics with respect to exact optimization methods like dynamic programming, in particular for more constrained situations (cases when demand tends to saturate hotel room availability), when the simplifying assumptions needed to make the problem analytically treatable do not hold.

中文翻译：

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在酒店收益管理中将智能启发式与模拟器相结合

收入管理使用数据驱动的建模和优化方法来决定卖什么、什么时候卖、卖给谁、卖什么价格，以增加收入和利润。酒店收入管理是一个非常复杂的环境，其特点是非线性、许多参数和约束以及随机性，特别是在客户需求方面。它受到维度诅咒的影响（Bellman 2015）：当变量数量增加（房间数量、可能的价格和容量、预订规则和约束的数量）时，无法使用动态规划或替代全局优化技术的精确解决方案并且必须求助于智能启发式，即可以改进当前解决方案但没有正式保证最优性的方法。有效的启发式方法可以结合“学习”（“反应”方案），根据过程的过去历史、在特定时间之前收到的过去保留以及迭代优化过程中的先前步骤来更新策略。不同的方法可以根据所考虑的特定模型（随机需求和酒店规则）、控制机制（定价策略）和用于确定改进或最佳解决方案的优化技术进行分类。在某些情况下，模型定义、控制机制和求解技术密切相关：这就是动态规划的情况，它需要适当简化的问题表述。我们为酒店预订过程设计了一个灵活的离散事件模拟器，并通过测量对利润的预期影响（通过将“训练”阶段与从不同模拟获得的最终“验证”阶段仔细分离而获得）实验不同的方法。实验结果显示了智能启发式对于精确优化方法（如动态规划）的有效性，特别是对于更受约束的情况（需求趋于饱和酒店房间可用性的情况），当使问题可分析处理所需的简化假设不抓住。