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A basic factor in answering all these questions is the expected behavior of production and distribution costs.
Factory planning and scheduling topics include flow planning, bottleneck characterization, buffer and batch-size tactics, seasonal planning, and dynamic behavior of production systems.
We illustrate how an efficient methodology called frequency domain experimentation can be used to gain better insight into the behavior of production systems.
The spectrum of potential contributions of control theory to understanding the dynamic behavior of production systems in the presence of turbulence is broad.
One means of adapting to variation in demand is making capacity flexible so that work in progress (WIP) can be regulated; however, this can significantly influence the dynamic behavior of production networks in which there is high local autonomy.
The behavior of production rate, when producing from a dual-porosity system at constant bottom-hole pressure, is different from that in a single-porosity system.
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The fundamental question in this research motivated us to understand the logical behavior of the production planner in the decision-making process through daily production scheduling.
On the other hand, the uncertainty affecting the production problem must be considered due to the presence of operations executed by human operators, the high level of customization entailing the need to cope with new products and processes and the behavior of the production environment.
Researchers often complain that the behavior of database production rules is diffcult to reason about and understand, due in part to the lack of formal declarative semantics.
At the first case study, an offshore petroleum well with gas lift artificial elevation is simulated – to determine the behavior of petroleum production as a function of the injected gas flow rate for different reservoir pressures and different wellbore diameters – and optimized to determine the maximum production considering different productivity indexes.
Three-dimensional response plot shown in Fig. 2a describes the behavior of cellulase production, main effect, interaction effect, and squared effect (nonlinear) of MgSO4 and pH at different concentrations.
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practice of production
performance of production
way of production
functioning of production
behavior of supply
role of production
behavior of extraction
reaction of production
activity of production
pattern of production
response of production
practices of production
behavior of generating
behavior of manufacturing
characteristics of production
acts of production
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