We study network revenue management problems motivated by applications such as railway ticket sales and hotel room bookings. Requests, each requiring a resource for a consecutive stay, arrive sequentially with known arrival probabilities. We investigate two scenarios: the accept-or-reject scenario, where a request can be fulfilled by assigning any available resource; and the BAM-based scenario, which generalizes the former by incorporating customer preferences through the basic attraction model (BAM), allowing the platform to offer an assortment of available resources from which the customer may choose. We develop polynomial-time policies and evaluate their performance using approximation ratios, defined as the ratio between the expected revenue of our policy and that of the optimal online algorithm. When each arrival has a fixed request type (e.g., the interval of the stay is fixed), we establish constant-factor guarantees: a ratio of 1 - 1/e for the accept-or-reject scenario and 0.25 for the BAM-based scenario. We further extend these results to the case where the request type is random (e.g., the interval of the stay is random). In this setting, the approximation ratios incur an additional multiplicative factor of 1 - 1/e, resulting in guarantees of at least 0.399 for the accept-or-reject scenario and 0.156 for the BAM-based scenario. These constant-factor guarantees stand in sharp contrast to the prior nonconstant competitive ratios that are benchmarked against the offline optimum.

翻译：我们研究受铁路售票和酒店客房预订等应用启发的网络收益管理问题。每个请求需要连续占用一种资源，请求以已知的到达概率顺序到达。我们研究两种场景：接受或拒绝场景，其中可通过分配任意可用资源来满足请求；以及基于BAM的场景，该场景通过基本吸引力模型（BAM）纳入客户偏好，从而推广了前者，允许平台提供可用资源组合供客户选择。我们开发了多项式时间策略，并使用近似比（定义为策略的期望收益与最优在线算法的期望收益之比）评估其性能。当每个到达请求具有固定请求类型（例如，住宿区间固定）时，我们建立了常数因子保证：接受或拒绝场景的近似比为1 - 1/e，基于BAM的场景为0.25。我们进一步将这些结果扩展到请求类型随机（例如，住宿区间随机）的情况。在此设置下，近似比会产生额外的乘法因子1 - 1/e，从而得到接受或拒绝场景至少0.399、基于BAM的场景至少0.156的保证。这些常数因子保证与先前以离线最优解为基准的非常数竞争比形成鲜明对比。