Introduces Λ-lr-QAOA and piecewise-ramp QAOA that promote penalty schedules to variational parameters and use a feasibility-driven loss on budget-constrained MWIS satellite planning instances.
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A multi-objective probabilistic forecast combination framework is introduced that generates Pareto-optimal combinations balancing forecast accuracy and inventory decision performance, outperforming single-objective methods on retail and spare parts data.
An additive model separates ranking information from scale variation in noisy non-reciprocal pairwise comparisons and derives noise estimators plus ranking-region probabilities.
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Feasibility-driven QAOA with penalty scheduling
Introduces Λ-lr-QAOA and piecewise-ramp QAOA that promote penalty schedules to variational parameters and use a feasibility-driven loss on budget-constrained MWIS satellite planning instances.