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The presence of disturbances can easily cause constraint violations in the transient.
In the testing, performance is measured by overall travel costs and the probability of constraint violations.
The controller design prevents constraint violations and ensures convexity while regulating the state vector to origin.
The parameter Γ i controls the trade-off between the probabilities of constraint violations.
It can be claimed that the maximisation of fairness and minimisation of constraint violations are independent.
In this paper we focus on the latter goal as we want to avoid constraint violations as much as possible.
A robust control invariant set is used to ensure that the rounding errors do not lead to constraint violations.
It is more valuable to allow for constraint violations, i.e., fuzzy constraints which in turn may lead to cheaper diets.
In this latter scenario, some constraint violations must be accepted if convergence to the optimum is desired.
The latter includes the operation costs for workstations as well as penalty costs generated by the cycle time constraint violations.
In this feasible situation, the constraint violations of COPs with zero are equivalent to be one of the unconstrained optimization problems because constraint violations of every individual are zero.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com