This paper presents two new procedures for ranking and selection (R&S) problems where the best system designs are selected from a set of competing ones based on multiple performance measures evaluated through stochastic simulation.
ANOVA analysis is performed to analyze the role of each factor on multiple performance characteristics and it was found that stirrer rotational speed and reaction time is playing a key role in affecting the mechanical property.
In this paper we demonstrate a method for determining the optimality of control algorithms based on multiple performance objectives.
In most countries, seismic codes are changing, reflecting a new design philosophy based on multiple performance levels.
The implications of the damage-based strength reduction factor (SRF), denoted as RD factor, on multiple performance targets are discussed.
The performance of ANN, SVM, and kNN in fault diagnosis is analyzed considering different scenarios, features, and based on multiple performance metrics.
The two flow configurations are contrasted based on multiple performance criteria, such as device temperature, power exchanged, conversions, and net hydrogen production by constructing operation maps.
Lab-scale thickener operation parameters including feed flowrate, solid percent, flocculant dosage and feedwell height were optimized based on multiple performance characteristics.
Oil agglomeration parameters including solid content, amount of oil, agitation time and agitation rate were optimized based on multiple performance characteristics.
The drilling parameters such as feed rate, spindle speed, drill material and wt.% of B4C particles were optimized based on multiple performance characteristics including thrust force, torque and surface roughness.
