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Dynamic optimization is usually used for developing optimal operation policies for new operational situations such as startup procedures, load changes, product changeovers.
This paper develops a game theory reinforcement learning (GT RL) method for determining the optimal operation policies in multi-operator multi-reservoir systems with respect to fairness and efficiency criteria.
Reservoir systems with multiple operators can benefit from coordination of operation policies.
As mobile multimedia services become more popular, operation policies must adapt to their demands.
In order to decrease the cost of operation, the startup period is to be shortened through optimizing startup operation policies.
Optimal operation policies in batch reactors are obtained using dynamic optimisation technique.
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The optimization problem is formulated as a multi-objective optimal control problem in order to investigate the impact of operational cost factors on optimal operation policy.
These conflicting objectives cause a dilemma for water managers when defining reservoir operation policy.
With these, an operation policy for the minimization of model parameter uncertainty is determined.
The optimal operation policy of flexible RO systems is studied in this work.
Failing to maintain relevant ESI, e.g. deletion by routine operation policy, would result in potential spoliation claims.
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