Jackson Network Models of Manufacturing Systems

Buzacott, John A.; Shanthikumar, J. George; Yao, David D.

doi:10.1007/978-1-4612-2670-3_1

Cited by 167 publications

(166 citation statements)

References 27 publications

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“…Therefore, the service curve guaranteed by WFC, to be derived in the next section, is also guaranteed by the MB system. Definition 2.1 ( [2]) Manufacturing blocking (production blocking) Consider a serial system of servers, where each server always serves a packet as long as there is a packet available for processing, and it is not "blocked". It is termed blocked if a packet with completed service cannot proceed to the downstream buffer because that buffer is full.…”

Section: Dominance Of Mb By Wfcmentioning

confidence: 99%

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Analysis of manufacturing blocking systems with Network Calculus

Bose

Jiang

Liu

et al. 2006

Performance Evaluation

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In this paper, manufacturing blocking (MB) system is studied from the Network Calculus perspective. By dominating MB by a window flow controller (WFC), we obtain the service curve of the system for both instantaneous and non-instantaneous cases. The explicit expression of the system service curve further leads to the optimal allocation of the buffer sizes in order to guarantee ideal system service curve (i.e. the service curve for the system with unlimited buffers between servers). This allocation is more efficient than that based on the guarantees of individual service curve. In addition, the simulation of NetCal systems is developed based on the duality between arrival curve and strict service curve. The concept of Workload Regulation is introduced to enforce a service curve of the rate-latency form. This construction provides the service regulator in the same way as the leaky bucket enforces the arrival curve. Simulation experiments are conducted to show the tightness of the theoretical bounds obtained using Network Calculus.

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Section: Dominance Of Mb By Wfcmentioning

confidence: 99%

“…From definition, it is well-known ( [2], p. 185, (5.39)) that the following recursive formula holds for manufacturing blocking system.…”

Section: Dominance Of Mb By Wfcmentioning

confidence: 99%

Analysis of manufacturing blocking systems with Network Calculus

Bose

Jiang

Liu

et al. 2006

Performance Evaluation

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“…It is well known that the demand variability leads to a decrease in customer demand satisfaction (Buzacott and Shanthikumar, 1993;Hopp and Spearman, 1996;Altiok, 1997;Zipkin, 2001 To investigate the properties of DTP for various levels of customer demand, we use the notion of the load factor (LF). This notion has been introduced by Jacobs and Meerkov (1995) as the ratio of the average demand to the average production volume during the shipping period, i.e.,…”

Section: Dtp Degradation As a Function Of Demand Variabilitymentioning

confidence: 99%

“…Production/inventory systems have been considered in numerous publications for over two decades (see monographs by Buzacott and Shanthikumar (1993), Hopp and Spearman (1996), Altiok (1997) and Zipkin (2001), and articles by Gavish and Graves (1981), Federgruen and Zipkin (1986), Altiok (1989), Srinivasan and Lee (1991), Ciarallo, Akella, and Morton (1994), Zijm and Vanhoutum (1994), Glasserman and Tayur (1996), Rubio and Wein (1996), Wang and Gerchak (1996), Moinzadeh and Aggarwal (1997), Erkip (1997, 1999), Gullu (1998), Liberopoulos and Dallery (2002)). The demand is almost always assumed to be random.…”

Section: Introductionmentioning

confidence: 99%

Random Demand Satisfaction in Unreliable Production–Inventory–Customer Systems

Enginarlar

Meerkov

2004

Annals of Operations Research

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Abstract.A method for calculating the probability of customer demand satisfaction in productioninventory-customer systems with Markovian machines, finite finished goods buffers, and random demand is developed. Using this method, the degradation of this probability as a function of demand variability is quantified. In addition, it is shown by examples that the probability of customer demand satisfaction depends primarily on the coefficient of variation, rather than on the complete distribution, of the demand.

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“…In this last case, server 1 is said to be 'blocked'. Transfers from node 1 to node 2 are instantaneous (see [1,19]). The above type of blocking is referred to as 'manufacturing blocking'.…”

Section: Manufacturing Blockingmentioning

confidence: 99%

Spectral Expansion Solutions for Markov-Modulated Queues

Mitrani¹

2002

Performance Evaluation of Complex Systems: Techniques and Tools

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Jackson Network Models of Manufacturing Systems

Cited by 167 publications

References 27 publications

Analysis of manufacturing blocking systems with Network Calculus

Analysis of manufacturing blocking systems with Network Calculus

Random Demand Satisfaction in Unreliable Production–Inventory–Customer Systems

Spectral Expansion Solutions for Markov-Modulated Queues

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