Eq. (17) — fixing the price at p earns pE[N − (N − n)⁺], N Poisson with mean λ(p)t
ProvedGVRPricing.FixedPrice.constant_policy_revenuefixed-pricep2o-batch-pfp2bp2o-gran-per-chapterp2o-plan-paperp2o-v1poisson-distributionrevenue-management
Let , , be an allowable rate with price , let be the stock and the horizon. The policy that charges the fixed price for the whole horizon (until the stock runs out) has expected revenue
where is a Poisson random variable with mean and .
The formula reduces the fixed-price heuristic to a computation with a Poisson distribution. It is the starting point of both case bounds in the proof of Theorem 3.
Formalization Note The left-hand side is the expected revenue of the constant-intensity policy built in the sales-process definition, not a formula. The Poisson law is Mathlib's poissonMeasure with rate , and the expectation is a lower Lebesgue integral of a nonnegative function.
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. (17)** (§3.3, Proof of Theorem 3, p. 1008). For an allowable rate `c ∈ Λ`, `c > 0`, the
expected revenue of fixing the price at `p = p(c)` for the whole horizon `[0, t]`, from stock
`n`, is `p E[N − (N − n)⁺]` with `N` a Poisson random variable of mean `c t`. -/
theorem constant_policy_revenue (M : Model) (c : ℝ) (hc : c ∈ M.Λ) (hc0 : 0 < c) (n : ℕ) (t : ℝ)
(ht : 0 ≤ t) :
(constPolicy M c).expectedRevenue n t =
ENNReal.ofReal (M.p c) *
∫⁻ k, ENNReal.ofReal ((k : ℝ) - max ((k : ℝ) - n) 0) ∂poissonMeasure (Real.toNNReal (c * t)) := 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. 1008 (PDF 10), §3.3, Proof of Theorem 3, eq. (17)
Human review
Confirmed by the mission captain (proposal self-audit).
Confirmed by the moderator at approval.