Results 1  10
of
24
Management: Research Overview and Prospects
 Transportation Science
"... This survey reviews the fortyyear history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The surv ..."
Abstract

Cited by 150 (5 self)
 Add to MetaCart
This survey reviews the fortyyear history of research on transportation revenue management (also known as yield management). We cover developments in forecasting, overbooking, seat inventory control, and pricing, as they relate to revenue management, and suggest future research directions. The survey includes a glossary of revenue management terminology and a bibliography of over 190 references. In the forty years since the first publication on overbooking control, passenger reservations systems have evolved from low level inventory control processes to major strategic information systems. Today, airlines and other transportation companies view revenue management systems and related information technologies as critical determinants of future success. Indeed, expectations of revenue gains that are possible with expanded revenue management capabilities are now driving the acquisition
The Censored Newsvendor and the Optimal Acquisition of Information
 Operations Research
, 1998
"... This paper investigates the effect of demand censoring on the optimal policy in newsvendor inventory models with general parametric demand distributions and unknown parameter values. We show that the newsvendor problem with observable lost sales reduces to a sequence of singleperiod problems while ..."
Abstract

Cited by 33 (3 self)
 Add to MetaCart
This paper investigates the effect of demand censoring on the optimal policy in newsvendor inventory models with general parametric demand distributions and unknown parameter values. We show that the newsvendor problem with observable lost sales reduces to a sequence of singleperiod problems while the newsvendor problem with unobservable lost sales requires a dynamic analysis. Using a Bayesian Markov decision process approach we show that the optimal inventory level in the presence of partially observable demand is higher than when demand is completely observed. We explore the economic rationality for this observation and illustrate it with numerical examples. Key words: Inventory, Bayesian Markov decision processes, lost sales, demand estimation, censoring Inspite of the extensive research on inventory models, there still remain some practical issues that have not received due consideration. One of them is demand estimation and its effect on optimal policies. Most results in stocha...
Inventory Record Inaccuracy: An Empirical Analysis
, 2004
"... This study explores the systematic variation in inventory record inaccuracy (IRI) observed both within and across stores. Traditional inventory models, with a few exceptions, do not account for the existence of IRI and those that do treat record inaccuracy as random. Examining nearly 370,000 invento ..."
Abstract

Cited by 32 (2 self)
 Add to MetaCart
(Show Context)
This study explores the systematic variation in inventory record inaccuracy (IRI) observed both within and across stores. Traditional inventory models, with a few exceptions, do not account for the existence of IRI and those that do treat record inaccuracy as random. Examining nearly 370,000 inventory records from 37 stores of one retailer, we found 65 % to be inaccurate. That is, the recorded inventory quantity of an item fails to match the quantity found in the store. We identify factors associated with this inaccuracy that are stock keeping unit (SKU) and storespecific. SKUspecific factors such as item cost, selling quantity, and method of distribution account for the observed variation in IRI within stores. Storespecific factors such as the density and variety of inventory observed at each store account for the variation in IRI across stores. 1
Provably nearoptimal samplingbased policies for stochastic inventory control models
 Proceedings, 38th Annual ACM Symposium on Theory of Computing
, 2006
"... In this paper, we consider two fundamental inventory models, the singleperiod newsvendor problem and its multiperiod extension, but under the assumption that the explicit demand distributions are not known and that the only information available is a set of independent samples drawn from the true ..."
Abstract

Cited by 14 (2 self)
 Add to MetaCart
(Show Context)
In this paper, we consider two fundamental inventory models, the singleperiod newsvendor problem and its multiperiod extension, but under the assumption that the explicit demand distributions are not known and that the only information available is a set of independent samples drawn from the true distributions. Under the assumption that the demand distributions are given explicitly, these models are wellstudied and relatively straightforward to solve. However, in most reallife scenarios, the true demand distributions are not available or they are too complex to work with. Thus, a samplingdriven algorithmic framework is very attractive, both in practice and in theory. We shall describe how to compute samplingbased policies, that is, policies that are computed based only on observed samples of the demands without any access to, or assumptions on, the true demand distributions. Moreover, we establish bounds on the number of samples required to guarantee that with high probability, the expected cost of the samplingbased policies is arbitrarily close (i.e., with arbitrarily small relative error) compared to the expected cost of the optimal policies which have full access to the demand distributions. The bounds that we develop are general, easy to compute and do not depend at all on the specific demand distributions.
Adaptive datadriven inventory control policies based on KaplanMeier estimator
, 2009
"... Using the wellknown productlimit form of the KaplanMeier estimator from statistics, we propose a new class of nonparametric adaptive datadriven policies for stochastic inventory control problems. We focus on the distributionfree newsvendor model with censored demands. The assumption is that the ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
Using the wellknown productlimit form of the KaplanMeier estimator from statistics, we propose a new class of nonparametric adaptive datadriven policies for stochastic inventory control problems. We focus on the distributionfree newsvendor model with censored demands. The assumption is that the demand distribution is not known and there is only sales data available. We study the theoretical performance of the new policies and show that for discrete demand distributions they converge almost surely to the set of optimal solutions. Computational experiments suggest that the new policies converge for general demand distributions, not necessarily discreet, and demonstrate that they are significantly more robust than previously known policies. As a byproduct of the theoretical analysis, we obtain new results on the asymptotic consistency of the KaplanMeier estimator for discrete random variables that extend existing work in statistics. To the best of our knowledge, this is the first application of the KaplanMeier estimator within an adaptive optimization algorithm, in particular, the first application to stochastic inventory control models. We believe that this work will lead to additional applications in other domains.
Inventory Control with Unobservable Lost Sales and Bayesian Updates. Working Paper
, 2005
"... We study a finitehorizon lostsales inventory model. The demand distribution is unknown and is dynamically updated based on the previous sales data in a Bayesian fashion. We derive a samplepath representation of the first order optimality condition, which characterizes the key tradeoff of the prob ..."
Abstract

Cited by 10 (0 self)
 Add to MetaCart
We study a finitehorizon lostsales inventory model. The demand distribution is unknown and is dynamically updated based on the previous sales data in a Bayesian fashion. We derive a samplepath representation of the first order optimality condition, which characterizes the key tradeoff of the problem. The expression allows us to see why the computation of the optimal policy is difficult and why the myopic solution is not a bound on the optimal solution. It enables us to develop simpler solution bounds and approximations. It also helps us to develop cost bounds as well as cost error bounds of the approximations. Numerical examples indicate that our approximations are most effective for products with short lifecycle. Otherwise, the myopic policy may be a reasonable choice. 1
Bounds and heuristics for optimal Bayesian inventory control with unobserved lost sales
, 2009
"... In most retail environments, when inventory runs out, the unmet demand is lost and not observed. The sales data are effectively censored by the inventory level. Factoring this censored data effect into demand estimation and inventory control decision makes the problem difficult to solve. In this pap ..."
Abstract

Cited by 7 (2 self)
 Add to MetaCart
(Show Context)
In most retail environments, when inventory runs out, the unmet demand is lost and not observed. The sales data are effectively censored by the inventory level. Factoring this censored data effect into demand estimation and inventory control decision makes the problem difficult to solve. In this paper, we focus on developing bounds and heuristics for this problem. Specifically, we consider a finitehorizon inventory control problem for a nonperishable product with unobserved lost sales and a demand distribution having an unknown parameter. The parameter is estimated sequentially by the Bayesian updating method. We first derive a set of solution upper bounds that work for all prior and demand distributions. For a fairly general monotone likelihoodratio distribution family, we derive relaxed but easily computable lower and upper bounds along an arbitrary sample path. We then propose two heuristics. The first heuristic is derived from the solution bound results. Computing this heuristic solution only requires the evaluation of the objective function in the observed lostsales case. The second heuristic is based on the approximation of the firstorder condition. We combine the firstorder derivatives of the simpler observed lostsales and perishableinventory models to obtain the approximation. For the latter case, we obtain a recursive formula that simplifies the computation. Finally, we conduct an extensive numerical study to evaluate and compare the bounds and heuristics. The numerical results indicate that both heuristics perform very well. They outperform the myopicpolicies by a wide margin.
Retail Inventory Management When Records are Inaccurate
, 2005
"... Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. While investments in preventative and corrective measures can be effective remedies, gains can also be achieved through inventory management tools that account for record errors. In this ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
Inventory record inaccuracy is a significant problem for retailers using automated inventory management systems. While investments in preventative and corrective measures can be effective remedies, gains can also be achieved through inventory management tools that account for record errors. In this paper, we consider intelligent inventory management tools that account for record errors using a Bayesian inventory record. We assume that excess demands are lost and unobserved, in which case sales data reveal information about physical inventory positions. We show that a probability distribution on inventory levels is a sufficient summary of past sales and replenishment observations, and that this probability distribution can be efficiently updated as observations are accumulated. We also demonstrate the use of this distribution as the basis for practical replenishment and inventory audit policies, and illustrate how the needed parameters can be estimated using data from a large national retailer. Our replenishment policies avoid the problem of “freezing, ” in which a physical inventory position persists at zero while the corresponding record is positive. In addition, simulation studies show that our replenishment policies recoup much of the cost of inventory record inaccuracy, and that our audit policies significantly outperform the popular “zerobalance walk ” audit policy. 1.
A NonParametric Asymptotic Analysis of Inventory Planning with Censored Demand
"... We study stochastic inventory planning with lost sales and instantaneous replenishment, where contrary to the classical inventory theory, the knowledge of the demand distribution is not available. Furthermore, we observe only the sales quantity in each period, and lost sales are unobservable, that i ..."
Abstract

Cited by 4 (1 self)
 Add to MetaCart
(Show Context)
We study stochastic inventory planning with lost sales and instantaneous replenishment, where contrary to the classical inventory theory, the knowledge of the demand distribution is not available. Furthermore, we observe only the sales quantity in each period, and lost sales are unobservable, that is, demand data are censored. The manager must make an ordering decision in each period based only on historical sales data. Excess inventory is either perishable or carried over to the next period. In this setting, we propose nonparametric adaptive policies that generate ordering decisions over time. We show that the Tperiod average expected cost of our policy differs from the benchmark newsvendor cost – the minimum expected cost that would have incurred if the manager had known the underlying demand distribution – by at most O(1/
An Adaptive Algorithm for Finding the Optimal BaseStock Policy in Lost Sales Inventory Systems with Censored Demand
"... We consider a periodicreview singlelocation singleproduct inventory system with lost sales and positive replenishment lead times. It is well known that the optimal policy does not possess a simple structure. Motivated by recent results showing that basestock policies perform well in these syste ..."
Abstract

Cited by 2 (0 self)
 Add to MetaCart
(Show Context)
We consider a periodicreview singlelocation singleproduct inventory system with lost sales and positive replenishment lead times. It is well known that the optimal policy does not possess a simple structure. Motivated by recent results showing that basestock policies perform well in these systems, we study the problem of finding the best basestock policy in such a system. In contrast to the classical inventory literature, we assume that the manager does not know the demand distribution a priori, but must make the replenishment decision in each period based only on the past sales (censored demand) data. We develop a nonparametric adaptive algorithm that generates a sequence of orderupto levels whose Tperiod running average of the inventory holding and lost sales penalty cost converges to the cost of the optimal basestock policy at the rate of O(1/T 1/3). Our analysis is based on recent advances in stochastic online convex optimization and on the uniform ergodicity of Markov chains associated with basesstock policies.