Download or read book Combined Dynamic Pricing and Inventory Control written by and published by . This book was released on 2000 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Control with Learning written by Nicholas C. Petruzzi and published by . This book was released on 1997 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Control with Learning written by Nicholas C. Petruzzi and published by . This book was released on 1996 with total page 50 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Control with Fixed Ordering Cost and Incomplete Demand Information written by Boxiao Chen and published by . This book was released on 2020 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the periodic review dynamic pricing and inventory control problem with fixed ordering cost. Demand is random and price dependent, and unsatisfied demand is backlogged. With complete demand information, the celebrated (s,S,p) policy is proved to be optimal, where s and S are the reorder point and order-up-to level for ordering strategy, and p, a function of on-hand inventory level, characterizes the pricing strategy. In this paper, we consider incomplete demand information and develop online learning algorithms whose average profit approaches that of the optimal (s,S,p) with a tight O ̃(√T) regret rate. A number of salient features differentiate our work from the existing online learning researches in the OM literature. First, computing the optimal (s,S,p) policy requires solving a dynamic programming (DP) over multiple periods involving unknown quantities, which is different from the majority of learning problems in operations management that only require solving single-period optimization questions. It is hence challenging to establish stability results through DP recursions, which we accomplish by proving uniform convergence of the profit-to-go function. The necessity of analyzing action-dependent state transition over multiple periods resembles the reinforcement learning question, considerably more difficult than existing bandit learning algorithms. Second, the pricing function p is of infinite dimension, and approaching it is much more challenging than approaching a finite number of parameters as seen in existing researches. The demand-price relationship is estimated based on upper confidence bound, but the confidence interval cannot be explicitly calculated due to the complexity of the DP recursion. Finally, due to the multi-period nature of (s,S,p) policies the actual distribution of the randomness in demand plays an important role in determining the optimal pricing strategy p, which is unknown to the learner a priori. In this paper, the demand randomness is approximated by an empirical distribution constructed using dependent samples, and a novel Wasserstein metric based argument is employed to prove convergence of the empirical distribution.

Download or read book Dynamic Pricing and Inventory Control written by Elodie Adida and published by VDM Publishing. This book was released on 2007 with total page 288 pages. Available in PDF, EPUB and Kindle. Book excerpt: (cont.) We introduce and study a solution method that enables to compute the optimal solution on a finite time horizon in a monopoly setting. Our results illustrate the role of capacity and the effects of the dynamic nature of demand. We then introduce an additive model of demand uncertainty. We use a robust optimization approach to protect the solution against data uncertainty in a tractable manner, and without imposing stringent assumptions on available information. We show that the robust formulation is of the same order of complexity as the deterministic problem and demonstrate how to adapt solution method. Finally, we consider a duopoly setting and use a more general model of additive and multiplicative demand uncertainty. We formulate the robust problem as a coupled constraint differential game. Using a quasi-variational inequality reformulation, we prove the existence of Nash equilibria in continuous time and study issues of uniqueness. Finally, we introduce a relaxation-type algorithm and prove its convergence to a particular Nash equilibrium (normalized Nash equilibrium) in discrete time.

Download or read book Dynamic Pricing With Infrequent Inventory Replenishments written by Boxiao Chen and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a joint pricing and inventory control problem where pricing can be adjusted more frequently, such as every period, than inventory ordering decisions, which are made every epoch that consists of multiple periods. This is motivated by many examples, especially for online retailers, where price is indeed much easier to change than inventory level, because changing the latter is subject to logistic and capacity constraints. In this setting, the retailer determines the inventory level at the beginning of each epoch and solves a dynamic pricing problem within each epoch with no further replenishment opportunities. The optimal pricing and inventory control policy is characterized by an intricate dynamic programming (DP) solution. We consider the situation where the demand-price function and the distribution of random demand noise are both unknown to the retailer, and the retailer needs to develop an online learning algorithm to learn those information and at the same time maximize total profit. We propose a learning algorithm based on least squares estimation and construction of an empirical noise distribution under a UCB framework and prove that the algorithm converges through the DP recursions to approach the optimal pricing and inventory control policy under complete demand information. The theoretical lower bound for convergence rate of a learning algorithm is proved based on the multivariate Van Trees inequality coupled with some structural DP analyses, and we show that the upper bound of our algorithm's convergence rate matches the theoretical lower bound.

Download or read book Dynamic Pricing and Inventory Control for Multiple Products written by Dimitris Bertsimas and published by . This book was released on 2014 with total page 20 pages. Available in PDF, EPUB and Kindle. Book excerpt: A periodical multi-product pricing and inventory control problem with applications to production planning and airline revenue management is studied. The objective function of the single-period model is shown to be convex for certain types of demand distributions, thus tractable for large instances. A heuristic is proposed to solve the more complex multi-period problem, which is an interesting combination of linear and dynamic programming. Numerical experiments and theoretical bounds on the optimal expected revenue suggest that the extent to which a dynamic policy based on a stochastic model will outperform a simple static policy based on a deterministic model depends on the level of demand variability as measured by the coefficient of variation.

Download or read book Optimal Policies for Dynamic Pricing and Inventory Control with Nonparametric Censored Demands written by Boxiao Chen and published by . This book was released on 2020 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the fundamental model in joint pricing and inventory replenishment control under the learning-while-doing framework, with T consecutive review periods and the firm not knowing the demand curve a priori. At the beginning of each period, the retailer makes both a price decision and an inventory order-up-to level decision, and collects revenues from consumers' realized demands while suffering costs from either holding unsold inventory items, or lost sales from unsatisfied customer demands. We make the following contributions to this fundamental problem as follows:1. We propose a novel inversion method based on empirical measures to consistently estimate the difference of the instantaneous reward functions at two prices, directly tackling the fundamental challenge brought by censored demands, without raising the order-up-to levels to unnaturally high levels to collect more demand information. Based on this technical innovation, we design bisection and trisection search methods that attain an O(T^{1/2}) regret, assuming the reward function is concave and only twice continuously differentiable.2. In the more general case of non-concave reward functions, we design an active tournament elimination method that attains O(T^{3/5}) regret, based also on the technical innovation of consistent estimates of reward differences at two prices.3. We complement the O(T^{3/5}) regret upper bound with a matching Omega(T^{3/5}) regret lower bound. The lower bound is established by a novel information-theoretical argument based on generalized squared Hellinger distance, which is significantly different from conventional arguments that are based on Kullback-Leibler divergence. This lower bound shows that no learning-while-doing algorithm could achieve O(T^{1/2}) regret without assuming the reward function is concave, even if the sales revenue as a function of demand rate or price is concave.Both the upper bound technique based on the "difference estimator" and the lower bound technique based on generalized Hellinger distance are new in the literature, and can be potentially applied to solve other inventory or censored demand type problems that involve learning.

Download or read book Integrating Dynamic Pricing and Inventory Control for Fresh Agri Product Under Consumer Choice written by Hawking Wang and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: In this article, we investigate a joint pricing and inventory problem for a retailer selling fresh-agri products (FAPs) with two-period shelf lifetime in a dynamic stochastic setting, where new and old FAPs are on sale simultaneously. At the beginning of each period, the retailer makes ordering decision for new FAP and sets regular and discount prices for new and old inventories, respectively. After demand realisation, the expired leftover is disposed and unexpired inventory is carried to the next period, for continuing selling. Unmet demand of all FAPs is backordered. The objective is to maximise the total expected discount profit over the whole planning horizon. We present a price dependent, stochastic dynamic programming model taking into account zero lead-time, linear ordering costs, inventory holding and backlogging costs, as well as disposal cost. As the influence of the perishability, each customer selects his preferred choice based on the utility of product price and quality. By the way of constructing demand rate vector, the original formulation can be transferred to be jointly concave and tractable. Finally, we characterise the optimal policy and develop effective methods to solve the problem. We also conduct numerical studies to further characterise the optimal policy, and to evaluate the loss of efficiency under static policies when compared to the optimal dynamic policy.

Download or read book Combined Dynamics Pricing and Inventory Control written by Aliza Heching and published by . This book was released on 2000 with total page 404 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Pricing and Inventory Control for Perishable Products Under Uncertain and Time Dependent Demand written by Halit Bayer and published by . This book was released on 2016 with total page 162 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Optimal Inventory Control in the Presence of Dynamic Pricing and Dynamic Advertising written by and published by . This book was released on 2015 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Dynamic Revenue and Inventory Management Models written by Yifeng Liu and published by . This book was released on 2014 with total page 127 pages. Available in PDF, EPUB and Kindle. Book excerpt: Effective pricing and inventory controls are very important for the success of a company, especially in an environment with many uncertainties such as random demand and fluctuating cost. In this work, we first consider pure dynamic pricing. Indeed, we consider three cases: markup in which price can only go up, markdown in which price can only go down, and reversible pricing in which price can go either direction. We also consider a joint pricing and inventory control model in which the raw material price evolves as a Markov process. For this model, we suppose production is make-to-order, so that the conversion from raw material to finished product is carried out only when demand arrives. For the pure pricing model, we establish the optimality of threshold-like policies. We also develop efficient and numerically stable algorithms. For the make-to-order joint inventory-pricing model, we demonstrate the optimality of a base-stock-list-price policy. In addition, we identify conditions under which policy parameters would exhibit monotone trends. Moreover, we showed the significant benefit of adopting cost-dependent base-stock list-price policy.

Download or read book Periodic Review Inventory Control and Dynamic Pricing for Perishable Product Under Uncertain and Time Dependent Demand written by Sajjad Rahimi and published by . This book was released on 2014 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Data Based Dynamic Pricing and Inventory Control with Censored Demand and Limited Price Changes written by Boxiao Chen and published by . This book was released on 2020 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt: A firm makes pricing and inventory replenishment decisions for a product over T periods to maximize its expected total profit. Demand is random and price sensitive, and unsatisfied demands are lost and unobservable (censored demand). The firm knows the demand process up to some parameters and needs to learn them through pricing and inventory experimentation. However, due to business constraints the firm is prevented from making frequent price changes, leading to correlated and dependent sales data. We develop data-driven algorithms by actively experimenting inventory and pricing decisions and construct maximum likelihood estimator with censored and correlated samples for parameter estimation. We analyze the algorithms using the T-period regret, defined as the profit loss of the algorithms over T periods compared with the clairvoyant optimal policy that knew the parameters a priori. For a so-called well-separated case, we show that the regret of our algorithm is O(T^{1/(m+1)}) when the number of price changes is limited by m >= 1, and is O( log T) when limited by beta log T for some positive constant beta>0; while for a more general case, the regret is O(T^{1/2}) when the underlying demand is bounded and O(T^{1/2} log T) when the underlying demand is unbounded. We further prove that our algorithm for each case is the best possible in the sense that its regret rate matches with the theoretical lower bound.

Download or read book Joint Pricing and Inventory Control with a Markovian Demand Model written by Rui Yin and published by . This book was released on 2007 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider the joint pricing and inventory control problem for a single product with a finite horizon and periodic review. The demand distribution in each period is determined by an exogenous Markov chain. Pricing and ordering decisions are made at the beginning of each period and all shortages are backlogged. The surplus costs as well as fixed and variable costs are state dependent. We show the existence of an optimal (s, S, p)-type feedback policy for the additive demand model. We extend the model to the case of emergency orders and also incorporate capacity and service level constraints. We compute the optimal policy for a class of Markovian demand and illustrate the benefits of dynamic pricing over fixed pricing strategies through numerical examples. The results indicate that it is more beneficial to implement the dynamic pricing strategy in a Markovian demand environment with a high fixed ordering cost or with high demand uncertainty.

Download or read book Dynamic Pricing and Inventory Management in the Presence of Online Reviews written by Nan Yang and published by . This book was released on 2018 with total page 38 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the joint pricing and inventory management problem in the presence of online customer reviews. Customers who purchase the product may post reviews that would influence future customers' purchasing behaviors. Under the common practice of customer-generated reviews on e-commerce platforms, rigorous investigation of their operational implications offers valuable insights and guidance for both the research community and practitioners. We develop a stochastic joint pricing and inventory management model to characterize the optimal policy in the presence of online reviews. We show that a rating-dependent base-stock/list-price policy is optimal. Interestingly, the inventory dynamics of the firm do not influence the optimal policy as long as the initial inventory is below the initial base-stock level. Hence, we can reduce the dynamic program that characterizes the optimal policy to one with a single-dimensional state-space (the aggregate net rating). The presence of online reviews gives rise to the trade-off between generating current profits and inducing future demands, thus having several important implications upon the firm's operations decisions. First, online reviews drive the firm to deliver a better service and attract more customers to post a review. Hence, the safety-stock and base-stock levels are higher in the presence of online reviews. Second, the evolution of the aggregate net rating process follows a mean-reverting pattern: When the current rating is low (resp. high), it has an increasing (resp. decreasing) trend in expectation. Third, although myopic profit optimization leads to significant optimality losses in the presence of online reviews, balancing the current profits and near-future demands suffices to exploit the network effect induced by online reviews. We propose a dynamic look-ahead heuristic policy that well leverages this idea and achieves small optimality gaps which decay exponentially in the length of the look-ahead time-window.