Scheduling semiconductor lines using a fluid network model

Connors, Daniel P.; Feigin, G.; Yao, David D.

doi:10.1109/70.282534

Cited by 68 publications

(23 citation statements)

References 12 publications

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“…Connors et al [27] presented a novel method for "what's next" scheduling of semiconductor manufacturing lines based on a deterministic fluid network model, capable of generating dynamic schedules. Kumar and Kumar [28] provided an introduction to the application of queuing network models to the design and analysis of reentrant flow lines in semiconductor wafer fabs.…”

Section: Queuing Network Modelmentioning

confidence: 99%

Job shop scheduling techniques in semiconductor manufacturing

Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sivakumar

2004

Int J Adv Manuf Technol

124

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This paper presents a brief review on job shop scheduling techniques in semiconductor manufacturing. The manufacturing environment in a semiconductor industry is considered a highly complex job shop, involving multiple types of work centers, large and changing varieties of products, sequencedependent setup times, reentrant process flow, etc., in a dynamic scheduling environment. Due to the stubborn nature of the deterministic job shop scheduling problem itself, many of the solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem-specific strategies. In this paper, we seek to provide a brief overview of the problem, the techniques used and the researchers involved in solving this problem.

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Section: Queuing Network Modelmentioning

confidence: 99%

Job shop scheduling techniques in semiconductor manufacturing

Gupta‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬

Sivakumar

2004

Int J Adv Manuf Technol

124

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“…For some relevant recent work see Chen and Yao (1993), Connors, Feigin and Yao (1994), Avram, Bertsimas and Ricard (1995), Dai (1995), Chen and Meyn (1999) , Meyn (2001), Meyn (2003) and Chen, Pandit and Meyn (2003).…”

Section: The Multi-class Fluid Network Optimization Problemmentioning

confidence: 99%

Near optimal control of queueing networks over a finite time horizon

Nazarathy

Weiss

2008

Ann Oper Res

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We propose a method for the control of multi-class queueing networks over a finite time horizon. We approximate the multi-class queueing network by a fluid network and formulate a fluid optimization problem which we solve as a separated continuous linear program. The optimal fluid solution partitions the time horizon to intervals in which constant fluid flow rates are maintained. We then use a policy by which the queueing network tracks the fluid solution. To that end we model the deviations between the queuing and the fluid network in each of the intervals by a multi-class queueing network with some infinite virtual queues. We then keep these deviations stable by an adaptation of a maximum pressure policy. We show that this method is asymptotically optimal when the number of items that is processed and the processing speed increase. We illustrate these results through a simple example of a three stage re-entrant line.

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“…F urthermore, we observe that job instances executing the last stage of their route can never deadlock the system, since their unloading from the system is always a feasible step. Hence, they can be ignored during the evaluation of the admissibility of a resource allocation state, and therefore, they are omitted during the de nition of the content o f RC i t and LC i t. 8 It is easy to see that job stages JT 11 Although not used in the previous example, a similar remark regarding the in-signi cance of last job stages in deadlock a voidance applies to the implementation of RUN DAP.…”

Section: The Resource Ordering Ro Policymentioning

confidence: 98%

Structural Control of Large-Scale Flexibly Automated Manufacturing Systems

Reveliotis¹,

Lawley²,

Ferreira³

2000

Computer-Aided Design, Engineering, and Manufacturing

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Current strategic and technological trends in discrete-part manufacturing require extensive and exible automation of the underlying production systems. However, even though a great deal of work has been done to facilitate manufacturing automation at the hardware component level, currently there is no adequately developed control methodology for these environments. In particular, it has recently been realized by the manufacturing community that any successful attempt towards the extensive automation of these environments requires the establishment o f logically correct and robust behavior from the underlying system. The resulting line of research is complementary to the most traditional performance-oriented control, and it has come to be known as the structural control of the contemporary exibly automated production environment. In this chapter, we initially discuss the role of structural control in the broader context of realtime control of exible manufacturing systems FMS, and in continuation, we present the major results of our research program on manufacturing system deadlock, currently the predominant structural control problem in the manufacturing research literature. In the concluding section, we also indicate how these results should be integrated in contemporary FMS controllers, and highlight directions for future research in the FMS structural control area.

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Scheduling semiconductor lines using a fluid network model

Cited by 68 publications

References 12 publications

Job shop scheduling techniques in semiconductor manufacturing

Job shop scheduling techniques in semiconductor manufacturing

Near optimal control of queueing networks over a finite time horizon

Structural Control of Large-Scale Flexibly Automated Manufacturing Systems

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