Towards adaptive and digital manufacturing

Monostori, László; Csáji, Balázs Cs.; Kádár, Botond; Pfeiffer, András; Ilie‐Zudor, Elisabeth; Kemény, Zs.; Szathmári, Marcell Ferenc

doi:10.1016/j.arcontrol.2010.02.007

Cited by 35 publications

(13 citation statements)

References 21 publications

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“…In this way, processing and testing times, reject rates and machine availability are given as stochastic values, and the parameters of the distribution functions are adjusted by a function that process the latest MES log data. Accordingly, the direct link between the simulation model and the physical system can be always maintained, resulting in reliable results without any direct user interaction (Monostori et al 2010). …”

Section: Robust Production Planning Methods For the Final Assembly Linmentioning

confidence: 99%

“…A production plan is called robust if it results in an acceptable level of the selected performance indicators even if unpredictable disruptions occur during the execution of the plan. Efficient ways of taking uncertainties into account, and to achieve more robust solutions are either applying stochastic models (Sahinidis 2004;Naeem et al 2013) (e.g., by estimating the underlying stochastic processes), or using adaptive and cooperative approaches which allows prompt responses to changes and disturbances (Monostori et al 2010). As deterministic models usually fail to provide executable plans due to the existence of uncertain and stochastic parameters (e.g.…”

Section: Robust Production Planningmentioning

confidence: 99%

See 1 more Smart Citation

Robust production planning and control for multi-stage systems with flexible final assembly lines

Gyulai

Pfeiffer

Monostori

2016

International Journal of Production Research

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Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator-task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

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Section: Robust Production Planning Methods For the Final Assembly Linmentioning

confidence: 99%

Section: Robust Production Planningmentioning

confidence: 99%

Robust production planning and control for multi-stage systems with flexible final assembly lines

Gyulai

Pfeiffer

Monostori

2016

International Journal of Production Research

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“…These decisions can be optimized using methods from operations research and it can be shown that many resource allocation problems can be formulated as special Markov decision processes [11]. Markov decision problems can be solved by dynamic programming, which solves complex problems by breaking them down into simpler sub problems.…”

Section: Figure 2 Objectives In Production Logisticsmentioning

confidence: 99%

A Review of Current Challenges in Production Planning

Szármes

2015

Acta Tech Jaur

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For manufacturing companies growing customer expectations, shortening product life cycles and increasing product variant numbers mean great challenges. Overcoming these is a major task for production planning, which is responsible for coordinating all business processes directly related to manufacturing. This paper presents important activities of production planning giving an integrative overview, and presents the digital factory concept with special attention to problems and their possible solutions. It concludes with challenges still waiting to be resolved and future development trends.

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“…Off-line simulations can be used for solution validation, parameter sensitivity analysis and evaluation of robustness and performance, usually on the control design process (Jernigan et al, 1997;Smith, 2003). Conversely, online simulations are suited for anticipating deviations and prospectively analyzing multiple scenarios and strategies, before a decision is made (Pfeiffer et al, 2008;Monostori et al, 2010). In their study about the industrial applications of agent technology, Mařík and Lažanský, (2007) …”

Section: Approaches Based On Simulationmentioning

confidence: 99%