ترغب بنشر مسار تعليمي؟ اضغط هنا

Product-Closing Approximation for Ranking-based Choice Network Revenue Management

672   0   0.0 ( 0 )
 نشر من قبل Thibault Barbier
 تاريخ النشر 2018
  مجال البحث
والبحث باللغة English




اسأل ChatGPT حول البحث

Most recent research in network revenue management incorporates choice behavior that models the customers buying logic. These models are consequently more complex to solve, but they return a more robust policy that usually generates better expected revenue than an independent-demand model. Choice network revenue management has an exact dynamic programming formulation that rapidly becomes intractable. Approximations have been developed, and many of them are based on the multinomial logit demand model. However, this parametric model has the property known as the independence of irrelevant alternatives and is often replaced in practice by a nonparametric model. We propose a new approximation called the product closing program that is specifically designed for a ranking-based choice model representing a nonparametric demand. Numerical experiments show that our approach quickly returns expected revenues that are slightly better than those of other approximations, especially for large instances. Our approximation can also supply a good initial solution for other approaches.



قيم البحث

اقرأ أيضاً

We study the canonical quantity-based network revenue management (NRM) problem where the decision-maker must irrevocably accept or reject each arriving customer request with the goal of maximizing the total revenue given limited resources. The exact solution to the problem by dynamic programming is computationally intractable due to the well-known curse of dimensionality. Existing works in the literature make use of the solution to the deterministic linear program (DLP) to design asymptotically optimal algorithms. Those algorithms rely on repeatedly solving DLPs to achieve near-optimal regret bounds. It is, however, time-consuming to repeatedly compute the DLP solutions in real time, especially in large-scale problems that may involve hundreds of millions of demand units. In this paper, we propose innovative algorithms for the NRM problem that are easy to implement and do not require solving any DLPs. Our algorithm achieves a regret bound of $O(log k)$, where $k$ is the system size. To the best of our knowledge, this is the first NRM algorithm that (i) has an $o(sqrt{k})$ asymptotic regret bound, and (ii) does not require solving any DLPs.
We consider the revenue maximization problem for an online retailer who plans to display a set of products differing in their prices and qualities and rank them in order. The consumers have random attention spans and view the products sequentially be fore purchasing a ``satisficing product or leaving the platform empty-handed when the attention span gets exhausted. Our framework extends the cascade model in two directions: the consumers have random attention spans instead of fixed ones and the firm maximizes revenues instead of clicking probabilities. We show a nested structure of the optimal product ranking as a function of the attention span when the attention span is fixed and design a $1/e$-approximation algorithm accordingly for the random attention spans. When the conditional purchase probabilities are not known and may depend on consumer and product features, we devise an online learning algorithm that achieves $tilde{mathcal{O}}(sqrt{T})$ regret relative to the approximation algorithm, despite of the censoring of information: the attention span of a customer who purchases an item is not observable. Numerical experiments demonstrate the outstanding performance of the approximation and online learning algorithms.
Consider a monopolist selling $n$ items to an additive buyer whose item values are drawn from independent distributions $F_1,F_2,ldots,F_n$ possibly having unbounded support. Unlike in the single-item case, it is well known that the revenue-optimal s elling mechanism (a pricing scheme) may be complex, sometimes requiring a continuum of menu entries. Also known is that simple mechanisms with a bounded number of menu entries can extract a constant fraction of the optimal revenue. Nonetheless, whether an arbitrarily high fraction of the optimal revenue can be extracted via a bounded menu size remained open. We give an affirmative answer: for every $n$ and $varepsilon>0$, there exists $C=C(n,varepsilon)$ s.t. mechanisms of menu size at most $C$ suffice for obtaining $(1-varepsilon)$ of the optimal revenue from any $F_1,ldots,F_n$. We prove upper and lower bounds on the revenue-approximation complexity $C(n,varepsilon)$ and on the deterministic communication complexity required to run a mechanism achieving such an approximation.
In community-based question answering (CQA) platforms, automatic answer ranking for a given question is critical for finding potentially popular answers in early times. The mainstream approaches learn to generate answer ranking scores based on the ma tching degree between question and answer representations as well as the influence of respondents. However, they encounter two main limitations: (1) Correlations between answers in the same question are often overlooked. (2) Question and respondent representations are built independently of specific answers before affecting answer representations. To address the limitations, we devise a novel graph-based tri-attention network, namely GTAN, which has two innovations. First, GTAN proposes to construct a graph for each question and learn answer correlations from each graph through graph neural networks (GNNs). Second, based on the representations learned from GNNs, an alternating tri-attention method is developed to alternatively build target-aware respondent representations, answer-specific question representations, and context-aware answer representations by attention computation. GTAN finally integrates the above representations to generate answer ranking scores. Experiments on three real-world CQA datasets demonstrate GTAN significantly outperforms state-of-the-art answer ranking methods, validating the rationality of the network architecture.
Airlines and other industries have been making use of sophisticated Revenue Management Systems to maximize revenue for decades. While improving the different components of these systems has been the focus of numerous studies, estimating the impact of such improvements on the revenue has been overlooked in the literature despite its practical importance. Indeed, quantifying the benefit of a change in a system serves as support for investment decisions. This is a challenging problem as it corresponds to the difference between the generated value and the value that would have been generated keeping the system as before. The latter is not observable. Moreover, the expected impact can be small in relative value. In this paper, we cast the problem as counterfactual prediction of unobserved revenue. The impact on revenue is then the difference between the observed and the estimated revenue. The originality of this work lies in the innovative application of econometric methods proposed for macroeconomic applications to a new problem setting. Broadly applicable, the approach benefits from only requiring revenue data observed for origin-destination pairs in the network of the airline at each day, before and after a change in the system is applied. We report results using real large-scale data from Air Canada. We compare a deep neural network counterfactual predictions model with econometric models. They achieve respectively 1% and 1.1% of error on the counterfactual revenue predictions, and allow to accurately estimate small impacts (in the order of 2%).
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا