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These participants can be allocated in different ways to the available resources, which leads to different configuration alternatives.
Different supply chain configuration alternatives are provided for the case study region considering various weights for objective functions representing relative importance of each objective.
All configuration alternatives were simulated rigorously using Aspen Plus were based on the energy requirements, total annual costs (TAC), and total carbon dioxide emissions (TCE).
To measure and analyze the performance effects (e.g., work in process, lead time) of the supply chain configuration alternatives depicted, a simulation environment is developed.
However, this algorithm requires an infrastructure with capabilities for: monitoring context changes, activating and assembling (at runtime) components that implement specific features, and gathering suitable metrics for system properties, so as to assess various configuration alternatives.
In this study models were developed based on population balances which included the effects of redistribution of material of different composition classes and particle sizes with four configuration alternatives of grinding and classification circuits (grinding, grinding-classification, classification-grinding, classification-grinding-classification).
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Usually, each configuration alternative will perform differently with respect to the execution and communication cost of the overall system.
Corresponding supply chain performance measures in terms of total cost and GHG emissions are proposed and discussed for each configuration alternative.
Process models are critical to our technology development efforts; enabling informed comparisons and decisions about process configuration, alternative operations, and selection of optimal recycle & waste minimization options through the development and life cycle of a process.
In order to achieve a straightforward interpretation of experimental data and avoid complex reconstruction algorithms inherent to fluence-based multiple source-detector configurations, alternative experimental techniques can be explored.
To address this type of problem we have applied a genetic algorithm (GA) to generate multiple network configurations (policy alternatives) for evaluation by human decision-makers.
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