Tandem Queues with Planned Inventories

Lee, Yong-Joo; Zipkin, Paul

doi:10.1287/opre.40.5.936

Cited by 90 publications

(58 citation statements)

References 25 publications

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“…This amount is called the basestock level of each stage. In other words, the basestock level of a production stage determines the maximum planned inventory of the outputs of the stage (Lee and Zipkin 1992). To operate a Base-stock, it is necessary to transmit demand information to all production stages as demand occurs.…”

Section: Base-stock Control Systemmentioning

confidence: 99%

Production Systems

Khojasteh¹

2016

Management for Professionals

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Section: Base-stock Control Systemmentioning

confidence: 99%

Production Systems

Khojasteh¹

2016

Management for Professionals

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“…Compared to the single-stage system, the evaluation for the two-stage system considering form postponement is more difficult. We use an approximation scheme developed by Lee and Zipkin (1992) …”

Section: The Single-stage Systemmentioning

confidence: 99%

Analysis of form postponement based on optimal positioning of the differentiation point and stocking decisions

Wong

Wikner

Naim

2008

International Journal of Production Research

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To cite this version:Hartanto Wong, Joakim Wikner, M M Naim. Analysis of form postponement based on optimal positioning of the differentiation point and stocking decisions. Abstract: In this paper we analyse the use of form postponement based on the positioning of differentiation point and stocking policy. Six classes of manufacturing configurations are identified based on the choice of whether or not form postponement is employed and the decision regarding the stocking policy for the final product configurations as well as for the generic component. Analytical evaluation methods based on queuing models are used to assess operational measures for each class of configuration and solution algorithms are developed to determine the optimal positioning of differentiation point and the optimal stocking levels. This allows us to compare the relative merits of all manufacturing configurations based on their respective best performances. The results of numerical experiment show how different operational parameters may influence the choice of optimal configuration, the preference of early or late postponement, and the relative cost savings obtained from employing form postponement.

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“…Systems of this type are widely used in industry to represent situations in which parts arrive at a service area, obtain the service they require, and then move on to the next service area or leave the system. Flow line manufacturing systems have been widely studied in operations research as serial, series or tandem queueing systems [see references [20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35][36].…”

Section: Definition and Assumptions For A Manufacturing Flow Linementioning

confidence: 99%

An aggregation procedure for simulating manufacturing flow line models

Savory

Mackulak

1997

Computers & Operations Research

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In developing a discrete-event simulation model, most of the actual features of a system under study are ignored and an abstraction is developed. If done correctly, this idealization provides a useful approximation of the real system. Aggregation is one of the available techniques for abstracting a system. Potential benefits for developing an aggregate simulation model include a reduced run length, a less complex model, and decreased demand for simulation resources. This paper presents a quantitative method for creating an aggregate discrete-event simulation model of a flow line manufacturing system. Computational experiments indicate that the average part cycle time through a flow line can be approximated with minimal error. ABSTRACTWe develop a formal method for specifying an aggregate discrete-event simulation model of a production flow line manufacturing system. The methodology operates by aggregating production stations or resources of a flow line. Determining the specifications for representing the aggregated resources in a simulation model is the focus of our presentation. We test the methodology for a set of flow lines with exponentially distributed arrival and service times.Comparisons between analytical and simulation results indicate the aggregation approach is quite accurate for estimating average part cycle time.

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Tandem Queues with Planned Inventories

Cited by 90 publications

References 25 publications

Production Systems

Production Systems

Analysis of form postponement based on optimal positioning of the differentiation point and stocking decisions

An aggregation procedure for simulating manufacturing flow line models

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