(<i>s, S</i>) Optimality in Joint Inventory-Pricing Control: An Alternate Approach

Huh, Woonghee Tim; Janakiraman, Ganesh

doi:10.1287/opre.1070.0462

Cited by 98 publications

(62 citation statements)

References 13 publications

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“…In spirit, our analysis of this model is similar to the one offered in Veinott (1966). In a recent notable work, Huh and Janakiraman (2005) extend the analysis in Veinott (1966) to the context of joint inventory-pricing control.…”

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confidence: 87%

Periodic review inventory management with contingent use of two freight modes with fixed costs

Jain

Groenevelt

Rudi

2011

Naval Research Logistics

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We study a stochastic inventory model of a firm, that sources the product from a make-to-order manufacturer, and can ship orders by a combination of two freight modes. The two freight modes differ in lead-times, and each has a fixed and a quantity proportional cost for each use. The ordering decisions are made periodically; however, the inventory holding and back-order penalty costs are incurred continuously in time. The decision of how to allocate units between the two freight modes utilizes information about demand during the completion of manufacturing. We derive the optimal freight mode allocation policy and show that the optimal ordering policy is not an (s, S) policy in general. We provide bounds for the optimal policy and perform a stationary analysis of the model assuming an (s, S) policy. We show that the best (s, S) policy achieves time average probability of being in-stock equal to the ratio of penalty cost rate and the sum of penalty cost rate and holding cost rate. We carry-out extensive numerical investigations of the properties of the optimal ordering policy and its benefits over the single freight policy, and show that the performance of best (s, S) policy is comparable to the optimal policy in most cases.

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confidence: 87%

Periodic review inventory management with contingent use of two freight modes with fixed costs

Jain

Groenevelt

Rudi

2011

Naval Research Logistics

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“…Therefore, it is possible that an (s, S, p) policy is not optimal in the lost sales case. In the joint pricing and inventory control literature, several researchers have considered the independent additive demand model with lost sales in a finite horizon system, such as Chen, Ray and Song (2003) and Huh and Janakiraman (2005). They prove the optimality of an (s, S, p) policy under the assumptions of stationary parameters and a salvage value that is equal to the unit ordering cost.…”

Section: Theorem 4 the Dynamic Programming Equationsmentioning

confidence: 99%

“…For a finite horizon system, Chen, Ray and Song (2003) and Huh and Janakiraman (2005) have proved the optimality of an (s, S, p) policy under assumptions of stationary parameters and a salvage value that is equal to the unit purchasing cost. In the area of continuous review models, Feng and Chen (2003) assume demand follows a Poisson process with price-sensitive intensities, while Chen, Wu and Yao (2004) model the demand process as a Brownian motion with a drift rate that is a function of price.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, in terms of techniques to prove the optimality of an (s, S, p)-type policy, Chen and Simchi-Levi (2004a), and Chen, Ray and Song (2003) use induction and the dynamic programming formulations, which are similar to that in Scarf (1960) in the classic stochastic inventory control problem. Huh and Janakiraman (2005) propose an alternative approach for the optimality proof, which is based on the method used in Veinott (1966). For a review of other work in the pricing and inventory literature, the reader is referred to Petruzzi and Dada (1999), Elmaghraby and Keskinocak (2003) and Chan et al (2004).…”

Section: Introductionmentioning

confidence: 99%

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Joint pricing and inventory control with a Markovian demand model

Yin

Rajaram

2007

European Journal of Operational Research

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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.

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“…For instance, Huh and Janakiraman (2005) propose an elegant framework based on sample path analysis to retrieve and extend most of the existing results for multi-period dynamic pricing and inventory problems (their approach cannot be applied to our model because in our setting the pricing decision directly affects the budget constraint of the following period).…”

Section: Introductionmentioning

confidence: 99%

Resource and Revenue Management in Nonprofit Operations

Véricourt

Lobo

2009

Operations Research

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Nonprofit firms sometimes engage in for-profit activities for the purpose of generating revenue to subsidize their mission activities. The organization is then confronted with a consumption vs. investment tradeoff, where investment corresponds to providing capacity for revenue customers, and consumption corresponds to serving mission customers. Exemplary of this approach are the Aravind Eye Hospitals in India, where profitable paying hospitals are used to subsidize care at free hospitals. We model this problem as a multi-period stochastic dynamic program. In each period, the organization must decide how much of the current assets should be invested in revenue-customer service capacity, and at what price the service should be sold. We provide sufficient conditions under which the optimal capacity and pricing decisions are of threshold type. Similar results are derived when the selling price is fixed but the banking of assets from one period to the next is allowed. We compare the performance of the optimal threshold policy with heuristics that may be more appealing to managers of nonprofit organizations, and assess the value of banking and of dynamic pricing through numerical experiments. Terms of use: Documents in

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(s, S) Optimality in Joint Inventory-Pricing Control: An Alternate Approach

Cited by 98 publications

References 13 publications

Periodic review inventory management with contingent use of two freight modes with fixed costs

Periodic review inventory management with contingent use of two freight modes with fixed costs

Joint pricing and inventory control with a Markovian demand model

Resource and Revenue Management in Nonprofit Operations

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