Optimal discrete-flow control of a single-stage failure-prone manufacturing system

Mourani, Iyad; Hennequin, Sophie; Xie, Xiaolan

doi:10.1109/cdc.2003.1272506

Cited by 3 publications

(4 citation statements)

References 13 publications

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“…In these cases, where production is halted plant-wide, primary effort is invested in resolving or re-setting the fault trigger in a minimum time, and then in re-establishing production rates (Farrell, Berger, & Appleby, 1993). Recent advances in system level control of supply chains and equipment buffers have suggested that ''hedging point'' strategies (implemented in the design and control of material handling ''input and output'' subsystems, typically) are an optimum way to accommodate temporary disruptions to production (Bullock & Hendrickson, 1993;Mourani, Hannequin, & Xie, 2003).…”

Section: Consequences Of Loss Of Dependability-system Requirementsmentioning

confidence: 99%

Improving automation software dependability: A role for formal methods?

Johnson

2007

Control Engineering Practice

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Section: Consequences Of Loss Of Dependability-system Requirementsmentioning

confidence: 99%

Improving automation software dependability: A role for formal methods?

Johnson

2007

Control Engineering Practice

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“…From hypothesis 3 and equations (11) and 12, the hedging point policy given by equation 10is optimal [16].…”

Section: Hypothesismentioning

confidence: 99%

“…In this policy, a nonnegative production surplus of parttypes should be maintained at times of excess capacity in order to hedge against future capacity shortages caused by machine failures. For the case of failure-prone manufacturing system, this policy has been shown to be optimal (see for examples [9], [10] and [11]). The hedging point policy has been adapted in [12] to take into account the pollutant emissions during the production.…”

Section: Introductionmentioning

confidence: 99%

A fuzzy hedging point policy for sustainable manufacturing system

Hennequin

Restrepo

2015

2015 International Conference on Industrial Engineering and Systems Management (IESM)

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“…Two types of models are considered in the literature: continuous flow models and discrete flow models. Discrete flow models are often considered more realistic for discrete manufacturing but the discrete processing of parts makes the performance analysis difficult especially when simulation is used (Mourani, andal., 1983, and, Song andSun, 2001 ). Continuous flow models (see Hu, and al., 1994, Sharifnia, 1988, Glasserman, 1995, Hu, 1995, Perkins and Srikant, 1998, Veatch and Caramanis, 1999, and Xie, 1989, offer a good approximation of material flows and makes the performance analysis more efficient without the need to track each individual part.…”

Section: Introductionmentioning

confidence: 99%