Eq. (15) — the augmented functional J_u(n,t,μ) dominates J_u(n,t) for μ ≥ 0
ProvedGVRPricing.FixedPrice.augmented_ge_revenuelagrangian-relaxationp2o-batch-pfp2bp2o-gran-per-chapterp2o-plan-paperp2o-v1revenue-management
Let be an admissible pricing policy, an initial stock, a horizon and a multiplier. Then the expected revenue is finite and
The term prices the stock constraint (13). Combined with Lemma 1, this inequality bounds every policy's revenue by the Lagrangian dual function of the deterministic problem.
Formalization Note is the real number ; finiteness of the two expectations is asserted in Lemma 1. Here the statement asserts that before comparing real values.
Preamble
import Mathlib import Definitions.Def_GVRPricing_FixedPrice_Model import Definitions.Def_GVRPricing_FixedPrice_SalesProcess open MeasureTheory ProbabilityTheory Set open scoped ENNReal
Formal statement
namespace GVRPricing.FixedPrice
/-- **Eq. (15)** (§3.2.1, p. 1007): for every admissible policy `u`, stock `n`, horizon `t ≥ 0`
and multiplier `μ ≥ 0`, the expected revenue `J_u(n, t)` is finite and bounded by the augmented
functional `J_u(n, t, μ) = E_u[∫_0^t (r(λ_s) − μ λ_s) ds] + n μ`. -/
theorem augmented_ge_revenue (M : Model) (u : Policy M) (n : ℕ) (t μ : ℝ) (ht : 0 ≤ t)
(hμ : 0 ≤ μ) :
u.expectedRevenue n t ≠ ∞ ∧ (u.expectedRevenue n t).toReal ≤ u.augmentedValue n t μ := by sorry
end GVRPricing.FixedPrice
Source
Gallego, van Ryzin, Optimal Dynamic Pricing of Inventories with Stochastic Demand over Finite Horizons, Management Science 40(8) (1994), p. 1007 (PDF 9), §3.2.1, Proof of Theorem 2, eq. (15)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.