2006
DOI: 10.1002/nav.20147
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The stochastic joint replenishment problem: A new policy, analysis, and insights

Abstract: Abstract:In this study, we propose a new parsimonious policy for the stochastic joint replenishment problem in a singlelocation, N-item setting. The replenishment decisions are based on both group reorder point-group order quantity and the time since the last decision epoch. We derive the expressions for the key operating characteristics of the inventory system for both unit and compound Poisson demands. In a comprehensive numerical study, we compare the performance of the proposed policy with that of existing… Show more

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Cited by 27 publications
(13 citation statements)
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“…Numerical tests on the problems used by Viswanathan (1997) and Pantumsinchai (1992) indicate the Aðm; MÞ outperforms P ðm; MÞ and ðA; MÞ policies. Ozkaya et al (2006) proposed a new control policy which combines features of both periodic and continuous review policies into an effective more computationally attractive policy. The policy, denoted ðA; M; T Þ, is to monitor inventory positions continuously and when the aggregate demand since last replenishment reaches A units or the time elapsed since last replenishment reaches T, all products are replenished up to M i .…”
Section: The Jrp Under Stochastic Demand (Sjrp)mentioning
confidence: 99%
“…Numerical tests on the problems used by Viswanathan (1997) and Pantumsinchai (1992) indicate the Aðm; MÞ outperforms P ðm; MÞ and ðA; MÞ policies. Ozkaya et al (2006) proposed a new control policy which combines features of both periodic and continuous review policies into an effective more computationally attractive policy. The policy, denoted ðA; M; T Þ, is to monitor inventory positions continuously and when the aggregate demand since last replenishment reaches A units or the time elapsed since last replenishment reaches T, all products are replenished up to M i .…”
Section: The Jrp Under Stochastic Demand (Sjrp)mentioning
confidence: 99%
“…For characterization of the distribution function FWs(τ) of the random delay Ws at the warehouse, we refer to Ozkaya et al. () who provide the delay distributions at the upper echelon for various stochastic joint replenishment policies. When the retailer employs the ( r , Q ) policy and the warehouse order‐up‐to level is Sw, it is given by FWs(τ)={center0centeritalicτ<0center11FEfalse(Lwitalicτ,normalΔ×Q,italicλfalse)center0italicτLwcenter11centeritalicτLw , where FE(x,k,λ) denotes the distribution function of an Erlang random variable with shape and scale parameters k and λ and with density f ( x , k , λ ).…”
Section: Coordinated Logistics: Two‐echelon Supply Chainmentioning
confidence: 99%
“…Related works include Renberg and Planche () who first proposed the ( Q , S ) policy, Pantumsinchai (), who presented an exact analysis of this policy under Poisson demands, and Cheung and Lee (), Nielsen and Larsen (), and Ozkaya et al. (, ).…”
Section: Introductionmentioning
confidence: 99%
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“…With regard to coordinated ordering decision, most literatures applied joint replenishment problem (JRP) to OWNR due to the similarity of cost functions and solution procedures [7,8]. JRP is originally developed for the multi-product inventory problem with the replenishment coordination of a group of items jointly ordered from the same supplier.…”
Section: Introductionmentioning
confidence: 99%