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Author: Huiwen Jia
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Languages : en
Pages : 0
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Book Description
We consider a price-based network revenue management problem with multiple products and multiple reusable resources. Each randomly arriving customer requests a product (service) that needs to occupy a sequence of reusable resources (servers). We adopt an incomplete information setting where the firm does not know the price-demand function for each product and the goal is to dynamically set prices of all products to maximize the total expected revenue of serving customers. We propose novel batched bandit learning algorithms for finding near-optimal pricing policies, and show that they admit a near-optimal cumulative regret bound of $ tilde{O}(J sqrt{XT})$, where $J$, $X$, and $T$ are the numbers of products, candidate prices, and service periods, respectively. As part of our regret analysis, we develop the first finite-time mixing time analysis of an open network queueing system (i.e., the celebrated Jackson Network), which could be of independent interest. Our numerical studies show that the proposed approaches perform consistently well.
Author: Huiwen Jia
Publisher:
ISBN:
Category :
Languages : en
Pages : 0
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Book Description
We consider a price-based network revenue management problem with multiple products and multiple reusable resources. Each randomly arriving customer requests a product (service) that needs to occupy a sequence of reusable resources (servers). We adopt an incomplete information setting where the firm does not know the price-demand function for each product and the goal is to dynamically set prices of all products to maximize the total expected revenue of serving customers. We propose novel batched bandit learning algorithms for finding near-optimal pricing policies, and show that they admit a near-optimal cumulative regret bound of $ tilde{O}(J sqrt{XT})$, where $J$, $X$, and $T$ are the numbers of products, candidate prices, and service periods, respectively. As part of our regret analysis, we develop the first finite-time mixing time analysis of an open network queueing system (i.e., the celebrated Jackson Network), which could be of independent interest. Our numerical studies show that the proposed approaches perform consistently well.
Author: Huiwen Jia
Publisher:
ISBN:
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Languages : en
Pages : 0
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Book Description
We consider a price-based revenue management problem with finite reusable resources over a finite time horizon $T$. Customers arrive following a price-dependent Poisson process and each customer requests one unit of $c$ homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, the customer waits in a queue until the next available unit. In this paper, we assume that the firm does not know how the arrival and service rates depend on posted prices, and thus it makes adaptive pricing decisions in each period based only on past observations to maximize the cumulative revenue. Given a discrete price set with cardinality $P$, we propose two online learning algorithms, termed Batch Upper Confidence Bound (BUCB) and Batch Thompson Sampling (BTS), and prove that the cumulative regret upper bound is $ tilde{O}( sqrt{PT})$, which matches the regret lower bound. In establishing the regret, we bound the transient system performance upon price changes via a novel coupling argument, and also generalize bandits to accommodate sub-exponential rewards. We also extend our approach to models with balking and reneging customers, and discuss a continuous price setting. Our numerical experiments demonstrate the efficacy of the proposed BUCB and BTS algorithms.
Author: Yanzhe (Murray) Lei
Publisher:
ISBN:
Category :
Languages : en
Pages : 43
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Book Description
We consider a dynamic pricing problem in a system with reusable resources. Customers arrive randomly over time, according to a specified non-stationary rate, and each customer requests a service that uses a combination of different types of resources for a deterministic duration of time. The resources are reusable in the sense that they can be immediately used to serve a new customer upon the completion of the previous service. Our objective is to construct a dynamic pricing control that maximizes expected total revenues. This is a fundamental problem faced by firms in many industries. We develop real-time heuristic controls based on the solution of the deterministic relaxation of the original stochastic problem and show that they are near-optimal in the regime of large demand and large resource capacity. We further show that our results can be extended to a more general setting with heterogeneous service time and advance reservation.
Author: Sentao Miao
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Languages : en
Pages : 0
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This paper studies the classic price-based network revenue management (NRM) problem with demand learning. The retailer dynamically decides prices of n products over a finite selling season (of length T) subject to m resource constraints, with the purpose of maximizing the cumulative revenue. In this paper, we focus on nonparametric demand model with some mild technical assumptions which are satisfied by most of the commonly used demand functions. We propose a robust ellipsoid method adapted to the NRM setting in a non-trivial manner, and this algorithm achieves the regret O(n^{3.5} sqrt{T} ln^6(nT)). This is the first result which achieves the regret of the form O(poly(n,m, ln(T)) sqrt{T}) (where poly(n,m, ln(T)) is a polynomial function of n,m, ln(T)) in the current literature on nonparametric NRM problem. Furthermore, we demonstrate that the regret can be further improved to O(n sqrt{T} ln(nT)) given that the nonparametric demand is "nearly linear''. This improvement is achieved by a primal-dual algorithm which combines stochastic gradient descent and online convex optimization technique.
Author: Joern Meissner
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Category :
Languages : en
Pages :
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Author: Yiwei Chen
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Category :
Languages : en
Pages : 0
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We consider a general class of price-based network revenue management problems that a firm aims to maximize revenue from multiple products produced with multiple types of resources endowed with limited inventory over a finite selling season. A salient feature of our problem is that the firm does not know the underlying demand function that maps prices to demand rate, which must be learned from sales data. It is well known that for almost all classes of demand functions, such as linear, exponential, multinomial logit and nested logit models, the revenue rate function is not concave in the products' prices but is concave in products' market shares (or price-controlled demand rates). This creates challenges in adopting any stochastic gradient descent based methods in the price space. We propose a novel nonparametric learning algorithm termed online inverse batch gradient descent (IGD) algorithm. This algorithm proceeds in batches. In each batch, the firm implements each product's perturbed prices, and then uses the sales information to estimate the market shares. Leveraging these estimates, the firm carries out a stochastic gradient descent step in the market share space that takes into account the relative inventory scarcity for the entire horizon, and then inversely maps the updated market shares back to the price space to obtain the prices for the next batch. Moreover, we also propose an inventory adjusted algorithm (IGD-I) that the feasible market share set is dynamically adjusted to capture the real-time relative inventory scarcity for the remaining season. For the large scale systems wherein all resources' inventories and the length of the horizon are proportionally scaled by a parameter $k$, we establish a dimension-independent regret bound of $O( k^{4/5} log k)$. This result is independent of the number of products and resources and works for continuum action-set prices and the demand functions that are only once differentiable. Our theoretical result guarantees the efficacy of both algorithms in the high dimensional systems where the number of products or resources is large and the prices are continuous. Our algorithms also numerically outperform the existing algorithms in the literature.
Author: Omar Besbes
Publisher:
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Category :
Languages : en
Pages : 0
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We consider a general class of network revenue management problems, where mean demand at each point in time is determined by a vector of prices, and the objective is to dynamically adjust these prices so as to maximize expected revenues over a finite sales horizon. A salient feature of our problem is that the decision maker can only observe realized demand over time but does not know the underlying demand function that maps prices into instantaneous demand rate. We introduce a family of “blind” pricing policies that are designed to balance trade-offs between exploration (demand learning) and exploitation (pricing to optimize revenues). We derive bounds on the revenue loss incurred by said policies in comparison to the optimal dynamic pricing policy that knows the demand function a priori, and we prove that asymptotically, as the volume of sales increases, this gap shrinks to zero.
Author: Dirk Sierag
Publisher:
ISBN:
Category :
Languages : en
Pages : 26
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This paper proposes and analyses a pricing-based revenue management model that allows flexible products on a network, with a non-trivial extension to group reservations. Under stochastic demand the problem can be solved using a dynamic programming formulation, though it suffers from the curse of dimensionality. The solution under deterministic demand gives an upper bound on the stochastic problem, and serves as a basis for two heuristics, which are asymptotically optimal. Numerical studies show that the heuristics perform well, even under uncertainty in demand. Moreover, neglecting flexible products can lead to substantial revenue loss.
Author: Stanislav Ivanov
Publisher: Zangador
ISBN: 9549278638
Category : Travel
Languages : en
Pages : 205
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Book Description
This research monograph aims at developing an integrative framework of hotel revenue management. It elaborates the fundamental theoretical concepts in the field of hotel revenue management like the revenue management system, process, metrics, analysis, forecasting, segmentation and profiling, and ethical issues. Special attention is paid on the pricing and non-pricing revenue management tools used by hoteliers to maximise their revenues and gross operating profit. The monograph investigates the revenue management practices of accommodation establishments in Bulgaria and provides recommendations for their improvement. The book is suitable for undergraduate and graduate students in tourism, hospitality, hotel management, services studies programmes, and researchers interested in revenue/yield management. The book may also be used by hotel general managers, marketing managers, revenue managers and other practitioners looking for ways to improve their knowledge in the field.
Author: Özalp Özer
Publisher: OUP Oxford
ISBN: 0191634263
Category : Business & Economics
Languages : en
Pages : 976
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Book Description
The Oxford Handbook of Pricing Management is a comprehensive guide to the theory and practice of pricing across industries, environments, and methodologies. The Handbook illustrates the wide variety of pricing approaches that are used in different industries. It also covers the diverse range of methodologies that are needed to support pricing decisions across these different industries. It includes more than 30 chapters written by pricing leaders from industry, consulting, and academia. It explains how pricing is actually performed in a range of industries, from airlines and internet advertising to electric power and health care. The volume covers the fundamental principles of pricing, such as price theory in economics, models of consumer demand, game theory, and behavioural issues in pricing, as well as specific pricing tactics such as customized pricing, nonlinear pricing, dynamic pricing, sales promotions, markdown management, revenue management, and auction pricing. In addition, there are articles on the key issues involved in structuring and managing a pricing organization, setting a global pricing strategy, and pricing in business-to-business settings.
