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- Author: Nathan P Sherman
- Publisher: BiblioScholar
- ISBN-10: 1249594677
- ISBN-13: 9781249594673
- Format: 18.9 x 24.6 x 0.5 cm, minkšti viršeliai
- Language: English
- SAVE -10% with code: EXTRA
A Stochastic Model for Joint Reception, Staging, Onward Movement, and Integration (e-book) (used book) | bookbook.eu
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Description
A stochastic model for the performance evaluation of a key phase in the deployment process, namely Joint Reception, Staging, Onward Movement, and Integration (JRSOI) is presented. The process is modeled as an open, multi-class tandem queueing network wherein personnel and various classes of cargo are modeled as the flow entities and the stages of the process constitute individual queueing stations. Singleand multiple-class models at both low and high resolutions are presented. No analytical, stochastic model of this process currently exists in the literature or in practice. The model provides a quick look at key aggregate performance measures such as system throughput and closure, and can be used to expediently identify problems occurring during JRSOI and the impact they have on the process. This information can substantially aid decision makers in regulating process flow. The queueing network model developed here can easily be expanded and adapted to any potential area of conflict. Numerical comparisons with Monte-Carlo simulation demonstrate that the model provides a viable, novel approach to the problem.
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- Author: Nathan P Sherman
- Publisher: BiblioScholar
- ISBN-10: 1249594677
- ISBN-13: 9781249594673
- Format: 18.9 x 24.6 x 0.5 cm, minkšti viršeliai
- Language: English English
A stochastic model for the performance evaluation of a key phase in the deployment process, namely Joint Reception, Staging, Onward Movement, and Integration (JRSOI) is presented. The process is modeled as an open, multi-class tandem queueing network wherein personnel and various classes of cargo are modeled as the flow entities and the stages of the process constitute individual queueing stations. Singleand multiple-class models at both low and high resolutions are presented. No analytical, stochastic model of this process currently exists in the literature or in practice. The model provides a quick look at key aggregate performance measures such as system throughput and closure, and can be used to expediently identify problems occurring during JRSOI and the impact they have on the process. This information can substantially aid decision makers in regulating process flow. The queueing network model developed here can easily be expanded and adapted to any potential area of conflict. Numerical comparisons with Monte-Carlo simulation demonstrate that the model provides a viable, novel approach to the problem.
Reviews