Approximate Analysis of Decentralized, Multi-Stage, Pull-Type Production/Inventory Systems

“…Although it is possible to use a more accurate representation of the processing time distributions of the pseudoprocessors in the analysis such an approach would enlarge the state space to a level where tractability is lost even in a single-product case, as shown in Gurgur and Altiok (2004). For instance, the distribution of V j (i) in subsystem (Q N (i) .…”

Section: Approximation Algorithmmentioning

confidence: 99%

“…However, when material transport is periodic, and consequently the batch size is greater than one, kanban systems differ from a tandem queue and the analyses of such systems becomes a lot more involved. Gurgur and Altiok (2004) have used a two-node decomposition method to analyze singleproduct, multi-stage pull systems with a two-card control policy. The present paper extends this scenario to the case with multiple products.…”

Section: Introductionmentioning

confidence: 99%

“…This enables us to split the original system into several single-product, multi-stage systems (one for each product) by treating the delay period as an additional phase a processor has to go through before being assigned to a product type. The analysis of each single-product system originates from the decomposition approach discussed in Gurgur and Altiok (2004), and involves the creation of subsystems for all the storage activities and phase-type modeling of the remaining system behavior. However, due to interactions among the products in the evaluation of delay parameters, the characterization of subsequent subsystems is different from that of Gurgur and Altiok (2004).…”

Section: Introductionmentioning

confidence: 99%

“…The analysis of each single-product system originates from the decomposition approach discussed in Gurgur and Altiok (2004), and involves the creation of subsystems for all the storage activities and phase-type modeling of the remaining system behavior. However, due to interactions among the products in the evaluation of delay parameters, the characterization of subsequent subsystems is different from that of Gurgur and Altiok (2004). A subsystem of a particular product interacts with subsystems of all the other products in the evaluation of delay parameters.…”

Section: Introductionmentioning

confidence: 99%

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Decentralized multi-product multi-stage systems with backorders

Gurgur

Altıok

2008

IIE Transactions

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A multi-stage production/inventory system with decentralized two-card kanban control policies producing multiple product types is considered. The system involves one-at-a-time processing at each stage, finite target levels in the buffers and batch transfers between production stages. The demand process for each product is assumed to be Poisson and excess demand is backordered. Products have a priority structure and the processor at each stage is shared according to a switching rule. Our objective is to obtain steady-state performance measures such as the average inventory and service levels for each product type. We propose an approximation algorithm based on: (i) characterization of the delay by a product type before receiving the processor's attention at each stage; and (ii) creation of subsystems for all the storage activity and phase-type modeling of the remaining system's behavior. Numerical examples are presented to show the accuracy of the method and its potential use in system design.

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Analysis of decentralized multi‐product pull systems with lost sales

Gurgur

Altıok

2007

Naval Research Logistics

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Abstract:We study a pull-type, flexible, multi-product, and multi-stage production/inventory system with decentralized two-card kanban control policies. Each stage involves a processor and two buffers with finite target levels. Production stages, arranged in series, can process several product types one at a time. Transportation of semi-finished parts from one stage to another is performed in fixed lot sizes. The exact analysis is mathematically intractable even for smaller systems. We present a robust approximation algorithm to model two-card kanban systems with batch transfers under arbitrary complexity. The algorithm uses phase-type modeling to find effective processing times and busy period analysis to identify delays among product types in resource contention. Our algorithm reduces the effort required for estimating performance measures by a considerable margin and resolves the state-space explosion problem of analytical approaches.Using this analytical tool, we present new findings for a better understanding of some tactical and operational issues. We show that flow of material in small procurement sizes smoothes flow of information within the system, but also necessitates more frequent shipments between stages, raising the risk of late delivery. Balancing the risk of information delays vis-à-vis shipment delays is critical for the success of two-card kanban systems. Although product variety causes time wasted in setup operations, it also facilitates relatively short production cycles enabling processors to switch from one product type to another more rapidly. The latter point is crucial especially in high-demand environments. Increasing production line size prevents quick response to customer demand, but it may improve system performance if the vendor lead-time is long or subject to high variation. Finally, variability in transportation and processing times causes the most damage if it arises at stages closer to the customer.

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Approximate Analysis of Decentralized, Multi-Stage, Pull-Type Production/Inventory Systems

Cited by 11 publications

References 23 publications

Approximate evaluation of continuous review (R,Q) policies in two-echelon inventory systems with stochastic transportation

Approximate evaluation of continuous review (R,Q) policies in two-echelon inventory systems with stochastic transportation

Decentralized multi-product multi-stage systems with backorders

Analysis of decentralized multi‐product pull systems with lost sales

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