The planning of a manufacturing system: a dynamic model

Bedini, R.; Toni, P.

doi:10.1080/00207548008919660

Cited by 4 publications

(3 citation statements)

References 1 publication

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“…Albright and Collins (1977) developed a Bayesian approach to the optimal control of continuous industrial processes. Bedini and Toni (1980) developed a dynamic model for the planning of a manufacturing system. The maximum principle has been used to formulate the problem and to obtain a solution.…”

Section: Editorial Supply Chain Dynamics Control and Disruption Manamentioning

confidence: 99%

Supply chain dynamics, control and disruption management

Ivanov

Mason

Hartl

2016

International Journal of Production Research

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Section: Editorial Supply Chain Dynamics Control and Disruption Manamentioning

confidence: 99%

Supply chain dynamics, control and disruption management

Ivanov

Mason

Hartl

2016

International Journal of Production Research

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“…Most of the published work concerns analytical modelling of the behaviour of production systems (e.g. Bedini et al 1979, Legasto et al 1980. Simulation of production systems by using modal analysis focused on market dynamics has also been proposed (Talavage 1980).…”

Section: Introductionmentioning

confidence: 99%

An approach to the dynamic modelling of manufacturing systems

Chryssolouris¹,

Anifantis²,

Karagiannis³

1998

International Journal of Production Research

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Because of their complexity manufacturing systems are di cult to model. However, modelling is very often required in order to study the behaviour of the system. In this paper an approach is described, where an analogy is drawn between the behaviour of a manufacturing and a mechanical system. Manufacturing systems have to respond to a dynamic demand, namely, a demand that changes over time. Flexibility of a manufacturing system can be thought of as the ability and the rapidness with which the system responds to the dynamic demand. This resembles the behaviour of a mechanical system under the excitation of a force that changes over time. The paper attempts to establish a modelling method based on this analogy and uses this method in the study of a real industrial system.

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Section: Introductionmentioning

confidence: 99%

Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications

Dolgui

Ivanov

Sethi

et al. 2018

International Journal of Production Research

235

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This paper presents a survey on the applications of optimal control to scheduling in production, supply chain and Industry 4.0 systems with a focus on the deterministic maximum principle. The first objective is to derive major contributions, application areas, limitations, as well as research and application recommendations for the future research. The second objective is to explain control engineering models in terms of industrial engineering and production management. To achieve these objectives, optimal control models, qualitative methods of performance analysis and computational methods for optimal control are considered. We provide a brief historic overview and clarify major mathematical fundamentals whereby the control engineering terms are brought into correspondence with industrial engineering and management. The survey allows the grouping of models with only terminal constraints with application to master production scheduling, models with hybrid terminal-logical constraints with applications to short term job and flow shop scheduling, and hybrid structural-terminal-logical constraints with applications to customised assembly systems such as Industry 4.0. Computational algorithms in state, control and adjoint variable spaces are discussed.

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The planning of a manufacturing system: a dynamic model

Cited by 4 publications

References 1 publication

Supply chain dynamics, control and disruption management

Supply chain dynamics, control and disruption management

An approach to the dynamic modelling of manufacturing systems

Scheduling in production, supply chain and Industry 4.0 systems by optimal control: fundamentals, state-of-the-art and applications

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