Results 1  10
of
42
Newsvendor Networks: Inventory Management and Capacity Investment with Discretionary Activities
, 2002
"... We introduce a class of models, called newsvendor networks, that allow for multiple products and multiple processing and storage points and investigate how their singleperiod properties extend to dynamic settings. Such models provide a parsimonious framework to study various problems of stochastic ..."
Abstract

Cited by 43 (7 self)
 Add to MetaCart
We introduce a class of models, called newsvendor networks, that allow for multiple products and multiple processing and storage points and investigate how their singleperiod properties extend to dynamic settings. Such models provide a parsimonious framework to study various problems of stochastic capacity investment and inventory management, including assembly, commonality, distribution, flexibility, substitution and transshipment. Newsvendor networks are stochastic models with recourse that are characterized by linear revenue and cost structures and a linear inputoutput transformation. While capacity and inventory decisions are locked in before uncertainty is resolved, some managerial discretion remains via expost inputoutput activity decisions. Expost decisions involve both the choice of activities and their levels and can result in subtle benefits. This discretion in choice is captured through alternate or "nonbasic" activities that can redeploy inputs and resources to best respond to resolved uncertain events. Nonbasic activities are never used in a deterministic environment; their value stems from discretionary flexibility to meet stochastic demand deviations from the operating point. The optimal capacity and inventory decisions balance overages with underages. Continuing the classic newsvendor analogy, the optimal balancing conditions can be interpreted as
A Capacitated ProductionInventory Model with Periodic Demand
 Operations Research
, 1996
"... For a single product, singlestage capacitated productioninventory model with stochastic, periodic (cyclic) demand, we find the optimal policy and characterize some of its properties. We study the finitehorizon, the discounted infinitehorizon and the infinitehorizon average cases. A simulation b ..."
Abstract

Cited by 38 (7 self)
 Add to MetaCart
For a single product, singlestage capacitated productioninventory model with stochastic, periodic (cyclic) demand, we find the optimal policy and characterize some of its properties. We study the finitehorizon, the discounted infinitehorizon and the infinitehorizon average cases. A simulation based optimization method is provided to compute the optimal parameters. Based on a numerical study, several insights into the model are also provided.
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...
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.
The Value of Information Sharing in a TwoStage Supply Chain with Production Capacity Constraints
 Naval Research Logistics
, 2003
"... We study the value of information sharing in a twostage supply chain with a single manufacturer and a single retailer in infinite time horizon, where the manufacturer has finite production capacity and the retailer faces independent demand. The manufacturer receives demand information even during ..."
Abstract

Cited by 12 (1 self)
 Add to MetaCart
We study the value of information sharing in a twostage supply chain with a single manufacturer and a single retailer in infinite time horizon, where the manufacturer has finite production capacity and the retailer faces independent demand. The manufacturer receives demand information even during periods of time in which the retailer does not order. Allowing for time varying cost functions, our objective is to characterize the impact of information sharing on the manufacturer’s cost and service level. We develop a new approach to characterize the induced Markov chain under cyclic orderupto policy. Taking advantage of the model’s special cost structure, we show that a cyclic orderupto policy is optimal for the manufacturer under the average cost criterion. Using extensive computational analysis, we quantify the impact of information sharing on the manufacturer’s performance in infinite time horizon under both i.i.d demand and independent but nonstationary demand.
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
(Show Context)
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
Tactical Planning Models for Supply Chain Management
 Elvesier Publishers, 2003. OR/MS Handbook on Supply Chain Management: Design, Coordination and Operation
, 2004
"... ..."
An Asymptotic Analysis of Inventory Planning with Censored Demand
, 2006
"... We study stochastic inventory planning with lost sales, where contrary to classical inventory theory, the knowledge of the demand distribution is not available a priori. While the manager observes the sales quantities in each period, lost sales are unobservable, i.e., demand data is censored. The de ..."
Abstract

Cited by 6 (1 self)
 Add to MetaCart
We study stochastic inventory planning with lost sales, where contrary to classical inventory theory, the knowledge of the demand distribution is not available a priori. While the manager observes the sales quantities in each period, lost sales are unobservable, i.e., demand data is censored. The decision in each period depends 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/ T). Computational results show that our policies perform well.