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The optimization problem is solved for two different performance objectives: maximization of net system power and maximization of system exergetic efficiency.
In addition to maximization of system availability, the decision maker also oftenly requires that the cost of the system is minimized simultaneously.
This study develops a new bilevel optimization problem based on goals at two different levels: minimization of water demands at the lower level and maximization of system benefit at the upper level.
The multi objective approach using genetic algorithm is applied considering two pairs of proposed objectives: (1) maximization of output power and minimization of the electricity cost and (2) maximization of system electrical efficiency and minimization of the electricity cost.
In most of the real world design or decision making problems involving reliability optimization, there are simultaneous optimization of multiple objectives such as the maximization of system reliability and the minimization of system cost, weight and volume.
Essentially the reliability optimization problems are dealing with the fine trade-off between two contradictory requirements, the maximization of system's reliability and the minimization of system's cost.
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Performance centering is sought through concurrent maximization of system-level flexibility based on behavioral models and implementation-level performance margins based on performance models.
Then, the throughput maximization of secondary system under the interference constraint of primary system and transmission power constraints is formulated as a constrained optimization problem.
Several papers have been published on ORCs systems, most of them dealing with the maximization of overall system performance and selection of the working fluid.
In this paper we show that the sizes (weights) of heat and fluid flow systems that function on board vehicles such as aircraft can be derived from the maximization of overall (system level) performance.
The Pareto optimization theory is used to tackle the conflicting objectives of simultaneous maximization of the system response and the input entropy rate.
More suggestions(14)
maximization of sector
maximization of electricity
maximization of essence
maximization of pleasure
maximization of productivity
maximization of profit
maximization of revenue
maximization of share
maximization of network
maximization of growth
maximization of utility
maximization of group
maximization of course
maximization of distribution
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