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Near Optimal Data Driven Approximation Schemes For Joint Pricing And Inventory Control Models
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Book Synopsis Near-optimal Data-driven Approximation Schemes for Joint Pricing and Inventory Control Models by : Hanzhang Qin (S. M.)
Download or read book Near-optimal Data-driven Approximation Schemes for Joint Pricing and Inventory Control Models written by Hanzhang Qin (S. M.) and published by . This book was released on 2018 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: The thesis studies the classical multi-period joint pricing and inventory control problem in a data-driven setting. In the problem, a retailer makes periodic decisions of the prices and inventory levels of an item that the retailer wishes to sell. The objective is to match the inventory level with a random demand that depends on the price in each period, while maximizing the expected profit over finite horizon. In reality, the demand functions or the distribution of the random noise are usually unavailable, whereas past demand data are relatively easy to collect. A novel data-driven nonparametric algorithm is proposed, which uses the past demand data to solve the joint pricing and inventory control problem, without assuming the parameters of the demand functions and the noise distributions are known. Explicit sample complexity bounds are given, on the number of data samples needed to guarantee a near-optimal profit. A simulation study suggests that the algorithm is efficient in practice.
Book Synopsis Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models by : Hanzhang Qin
Download or read book Data-Driven Approximation Schemes for Joint Pricing and Inventory Control Models written by Hanzhang Qin and published by . This book was released on 2019 with total page 45 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Research Handbook on Inventory Management by : Jing-Sheng J. Song
Download or read book Research Handbook on Inventory Management written by Jing-Sheng J. Song and published by Edward Elgar Publishing. This book was released on 2023-08-14 with total page 565 pages. Available in PDF, EPUB and Kindle. Book excerpt: This comprehensive Handbook provides an overview of state-of-the-art research on quantitative models for inventory management. Despite over half a century’s progress, inventory management remains a challenge, as evidenced by the recent Covid-19 pandemic. With an expanse of world-renowned inventory scholars from major international research universities, this Handbook explores key areas including mathematical modelling, the interplay of inventory decisions and other business decisions and the unique challenges posed to multiple industries.
Book Synopsis Sampling-Based Approximation Schemes for Capacitated Stochastic Inventory Control Models by : Wang Chi Cheung
Download or read book Sampling-Based Approximation Schemes for Capacitated Stochastic Inventory Control Models written by Wang Chi Cheung and published by . This book was released on 2017 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the classical multi-period capacitated stochastic inventory control problems in a data-driven setting. Instead of assuming full knowledge of the demand distributions, we assume that the demand distributions can only be accessed through drawing random samples. Such data-driven models are ubiquitous in practice, where the cumulative distribution functions of the underlying random demand are either unavailable or too complicated to work with. We apply the Sample Average Approximation (SAA) method to the capacitated inventory control problem and establish an upper bound on the number of samples needed for the SAA method to achieve a near-optimal expected cost, under any level of required accuracy and pre-specified confidence probability. The sample bound is polynomial in the number of time periods as well as the confidence and accuracy parameters. Moreover, the bound is independent of the underlying demand distributions. However, the SAA requires solving the SAA problem, which is #P-hard. Thus, motivated by the SAA analysis, we propose a randomized polynomial time approximation scheme which also uses polynomially many samples. Finally, we establish a lower bound on the number of samples required to solve this data-driven newsvendor problem to near-optimality.
Book Synopsis Computing Provably Near-optimal Policies for Stochastic Inventory Control Models by : Retsef Levi
Download or read book Computing Provably Near-optimal Policies for Stochastic Inventory Control Models written by Retsef Levi and published by . This book was released on 2005 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost-Sales and Censored Demand by : Boxiao Chen
Download or read book Nonparametric Learning Algorithms for Joint Pricing and Inventory Control with Lost-Sales and Censored Demand written by Boxiao Chen and published by . This book was released on 2020 with total page 44 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a joint pricing and inventory control problem in which the customer's response to selling price and the demand distribution are not known a priori. Unsatisfied demand is lost and unobserved, and the only available information for decision-making is the observed sales data (a.k.a. censored demand). Conventional approaches, such as stochastic approximation, online convex optimization, and continuum-armed bandit algorithms, cannot be employed since neither the realized values of the profit function nor its derivatives are known. A major challenge of this problem lies in that the estimated profit function constructed from observed sales data is multimodal in price. We develop a nonparametric spline approximation based learning algorithm. The algorithm separates the planning horizon into a disjoint exploration phase and an exploitation phase. During the exploration phase, the price space is discretized, and each price is offered an equal number of periods together with a pre-specified target inventory level. Based on the sales data collected on these prices, a spline approximation of the demand-price function is constructed, and then the corresponding surrogate optimization problem is solved on a sparse grid to obtain a pair of recommended price and target inventory level. During the exploitation phase, the algorithm implements the recommended strategies. We establish a (nearly) square-root regret rate, which (almost) matches the theoretical lower bound.
Book Synopsis The Elements of Joint Learning and Optimization in Operations Management by : Xi Chen
Download or read book The Elements of Joint Learning and Optimization in Operations Management written by Xi Chen and published by Springer Nature. This book was released on 2022-09-20 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book examines recent developments in Operations Management, and focuses on four major application areas: dynamic pricing, assortment optimization, supply chain and inventory management, and healthcare operations. Data-driven optimization in which real-time input of data is being used to simultaneously learn the (true) underlying model of a system and optimize its performance, is becoming increasingly important in the last few years, especially with the rise of Big Data.
Book Synopsis Iterative Algorithms for a Joint Pricing and Inventory Control Problem with Nonlinear Demand Functions by : Anupam Mazumdar (S. M.)
Download or read book Iterative Algorithms for a Joint Pricing and Inventory Control Problem with Nonlinear Demand Functions written by Anupam Mazumdar (S. M.) and published by . This book was released on 2009 with total page 81 pages. Available in PDF, EPUB and Kindle. Book excerpt: Price management, production planning and inventory control are important determinants of a firm's profitability. The intense competition brought about by rapid innovation, lean manufacturing time and the internet revolution has compelled firms to adopt a dynamic strategy that involves complex interplay between pricing and production decisions. In this thesis we consider some of these problems and develop computationally efficient algorithms that aim to tackle and optimally solve these problems in a finite amount of time. In the first half of the thesis we consider the joint pricing and inventory control problem in a deterministic and multiperiod setting utilizing the popular log linear demand model. We develop four algorithms that aim to solve the resulting profit maximization problem in a finite amount of time. The developed algorithms are then tested in a variety of settings ranging from small to large instances of trial data. The second half of the thesis deals with setting prices effectively when the customer demand is assumed to follow the multinomial logit demand model, which is the most popular discrete choice demand model. The profit maximization problem (even in the absence of constraints) is non-convex and hard to solve. Despite this fact we develop algorithms that compute the optimal solution efficiently. We test the algorithms we develop in a wide variety of scenarios from small to large customer segment, with and without production/inventory constraints. The last part of the thesis develops solution methods for the joint pricing and inventory control problem when costs are linear and demand follows the multinomial logit model.
Book Synopsis Linear Programming-based Subgradient Algorithm for Joint Pricing and Inventory Control Problems by : Tingting Rao
Download or read book Linear Programming-based Subgradient Algorithm for Joint Pricing and Inventory Control Problems written by Tingting Rao and published by . This book was released on 2008 with total page 94 pages. Available in PDF, EPUB and Kindle. Book excerpt: It is important for companies to manage their revenues and -reduce their costs efficiently. These goals can be achieved through effective pricing and inventory control strategies. This thesis studies a joint multi-period pricing and inventory control problem for a make-to-stock manufacturing system. Multiple products are produced under shared production capacity over a finite time horizon. The demand for each product is a function of the prices and no back orders are allowed. Inventory and production costs are linear functions of the levels of inventory and production, respectively. In this thesis, we introduce an iterative gradient-based algorithm. A key idea is that given a demand realization, the cost minimization part of the problem becomes a linear transportation problem. Given this idea, if we knew the optimal demand, we could solve the production problem efficiently. At each iteration of the algorithm, given a demand vector we solve a linear transportation problem and use its dual variables in order to solve a quadratic optimization problem that optimizes the revenue part and generates a new pricing policy. We illustrate computationally that this algorithm obtains the optimal production and pricing policy over the finite time horizon efficiently. The computational experiments in this thesis use a wide range of simulated data. The results show that the algorithm we study in this thesis indeed computes the optimal solution for the joint pricing and inventory control problem and is efficient as compared to solving a reformulation of the problem directly using commercial software. The algorithm proposed in this thesis solves large scale problems and can handle a wide range of nonlinear demand functions.
Book Synopsis Data-driven Optimization with Behavioral Considerations by : Rim Hariss
Download or read book Data-driven Optimization with Behavioral Considerations written by Rim Hariss and published by . This book was released on 2019 with total page 241 pages. Available in PDF, EPUB and Kindle. Book excerpt: This thesis aims to introduce descriptive and predictive models that guide more informed pricing strategies in practice, drawing from interdisciplinary work of current OM, behavioral theories and recent machine learning advances. In chapter 2, we integrate a consumer purchase experiment and an analytical model to investigate how consumers’ price-based quality perception, expected markdown, and a product’s availability information influence a retailer’s markdown pricing strategy. We subsequently develop a consumer model that incorporates consumers’ price-based quality perception observed from the experimental data and consumers’ potential loss aversion. We embed this consumer model into the retailer’s markdown optimization and examine the impact of these behavioral factors on the retailer’s optimal strategy. In chapter 3, we study a retailer’s optimal promotion strategy when demand is affected by different classes of customers’ status in the rewards program and their heterogeneous redemption behavior. We formulate the retailer’s problem as a dynamic program and prove a unique optimal threshold discounting policy. We also propose an approximation algorithm of the optimal price as a convex combination of the optimal prices for each class separately. Using data from a fast food chain, we assess the performance of the algorithm and the optimal pricing compared to current practice. In chapter 4, we are concerned with accurately estimating price sensitivity for listed tickets in the secondary market. In the presence of endogeneity, binary outcomes and non-linear interactions between ticket features, we introduce a novel loss function which can be solved using several off-the-shelf machine learning methods. On a wide range of synthetic data sets, we show that our approach beats state-of-the-art machine learning and causal inference approaches for estimating treatment effects in the classification setting. In chapter 5, we consider an optimization problem with a random forest objective function and general polyhedral constraints. We formulate this problem using Mixed Integer Optimization techniques and show it can be solved to optimality efficiently using Pareto-optimal Benders cuts. We prove analytical guarantees for a random forest approximation that consists of only a subset of trees. We also propose heuristics inspired by cross-validation and assess their performance on two real-world case
Book Synopsis Joint Pricing and Inventory Control with a Markovian Demand Model by : Rui Yin
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.
Book Synopsis Assortment and Inventory Optimization by : Mohammed Ali Aouad
Download or read book Assortment and Inventory Optimization written by Mohammed Ali Aouad and published by . This book was released on 2017 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Finding optimal product offerings is a fundamental operational issue in modern retailing, exemplified by the development of recommendation systems and decision support tools. The challenge is that designing an accurate predictive choice model generally comes at the detriment of efficient algorithms, which can prescribe near-optimal decisions. This thesis attempts to resolve this disconnect in the context of assortment and inventory optimization, through theoretical and empirical investigation. First, we tightly characterize the complexity of general nonparametric assortment optimization problems. We reveal connections to maximum independent set and combinatorial pricing problems, allowing to derive strong inapproximability bounds. We devise simple algorithms that achieve essentially best-possible factors with respect to the price ratio, size of customers' consideration sets, etc. Second, we develop a novel tractable approach to choice modeling, in the vein of nonparametric models, by leveraging documented assumptions on the customers' consider-then-choose behavior. We show that the assortment optimization problem can be cast as a dynamic program, that exploits the properties of a bi-partite graph representation to perform a state space collapse. Surprisingly, this exact algorithm is provably and practically efficient under common consider-then-choose assumptions. On the estimation front, we show that a critical step of standard nonparametric estimation methods (rank aggregation) can be solved in polynomial time in settings of interest, contrary to general nonparametric models. Predictive experiments on a large purchase panel dataset show significant improvements against common benchmarks. Third, we turn our attention to joint assortment optimization and inventory management problems under dynamic customer choice substitution. Prior to our work, little was known about these optimization models, which are intractable using modern discrete optimization solvers. Using probabilistic analysis, we unravel hidden structural properties, such as weak notions of submodularity. Building on these findings, we develop efficient and yet conceptually-simple approximation algorithms for common parametric and nonparametric choice models. Among notable results, we provide best-possible approximations under general nonparametric choice models (up to lower-order terms), and develop the first constant-factor approximation under the popular Multinomial Logit model. In synthetic experiments vis-a-vis existing heuristics, our approach is an order of magnitude faster in several cases and increases revenue by 6% to 16%.
Book Synopsis Asymptotic Optimality of Constant-Order Policies in Joint Pricing and Inventory Control Models by : Xin Chen
Download or read book Asymptotic Optimality of Constant-Order Policies in Joint Pricing and Inventory Control Models written by Xin Chen and published by . This book was released on 2019 with total page 43 pages. Available in PDF, EPUB and Kindle. Book excerpt: We consider a traditional joint pricing and inventory control problem with lead times, which has been extensively studied in the literature but is notoriously difficult to solve due to the complex structure of the optimal policy. In this work, rather than analyzing the optimal policy, we propose a class of so-called constant-order dynamic pricing policies, which are quite different from base-stock heuristics, the primary focus in the existing literature. Under such a policy, a constant-order amount of new inventory is ordered every period and a pricing decision is made based on the on-hand inventory. The policy is independent of the lead time and does not suffer from the curse of dimensionality. We prove that the best constant-order dynamic pricing policy is asymptotically optimal as the lead time grows large, which is exactly the setting in which the problem becomes computationally intractable due to the curse of dimensionality. As a main methodological contribution, we implement the so-called vanishing discount factor approach and establish the convergence to a long-run average random yield inventory model with zero lead time and ordering capacities by its discounted counterpart as the discount factor goes to one, non-trivially extending the previous results in Federgruen and Yang (2014) that analyze a similar model but without capacity constraints.
Author :Institute for Operations Research and the Management Sciences. National Meeting Publisher : ISBN 13 : Total Pages :644 pages Book Rating :4.E/5 ( download)
Book Synopsis INFORMS Annual Meeting by : Institute for Operations Research and the Management Sciences. National Meeting
Download or read book INFORMS Annual Meeting written by Institute for Operations Research and the Management Sciences. National Meeting and published by . This book was released on 2009 with total page 644 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Joint Price and Inventory Optimization Under Minimax Regret by : Chengzhang Li
Download or read book Joint Price and Inventory Optimization Under Minimax Regret written by Chengzhang Li and published by . This book was released on 2020 with total page 51 pages. Available in PDF, EPUB and Kindle. Book excerpt: We study the problem of jointly optimizing the price and order quantity for a perishable product, also known as the pricing-newsvendor problem. We consider the case with demand ambiguity where the demand is a function of the price and an uncertain factor, of which only the support information is known. We employ the minimax regret decision criterion to minimize the worst-case regret, which is defined as the difference between the optimal profit that could be obtained with perfect information and the realized profit using the decision made with ambiguous information. First, we characterize the optimal pricing and ordering decisions under the minimax regret criterion and compare their properties with those in the classical models that seek to maximize the expected profit. Specifically, we explore the impact of inventory risk by comparing the optimal price and the risk-free price, and study comparative statics with respect to the degree of demand ambiguity and the unit ordering cost. Second, we compare the minimax regret approach with two other approaches that are commonly used under demand ambiguity, namely the max-min robust approach and the regression-based data-driven approach. In the demand ambiguity setting, we show that the minimax regret approach avoids the high degree of conservativeness that is often incurred in the max-min approach. In the data-driven setting, we show via a numerical study that the minimax regret approach outperforms the classical regression-based approach when data is scarce, when the demand has high volatility, or when the demand model is misspecified.
Book Synopsis Approximation Methods for Supply-chain Problems by : Van Anh Truong
Download or read book Approximation Methods for Supply-chain Problems written by Van Anh Truong and published by . This book was released on 2007 with total page 262 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Optimal Procurement and Inventory Control in Volatile Commodity Markets by : Christian Mandl
Download or read book Optimal Procurement and Inventory Control in Volatile Commodity Markets written by Christian Mandl and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
