Proposition 3 — Ordinary base-stock cost bound
ProvedCappedBaseStock.base_stock_cost_boundConsider a periodic-review lost-sales inventory system. Demand is i.i.d., nonnegative and real-valued, with . Lead time is an integer , and holding and lost-sales rates satisfy . Inventory and the pipeline coordinates initially equal zero. The first pipeline component arrives, the order is placed before current demand is observed, demand is served up to available inventory, and the remaining pipeline shifts; today's order arrives periods later. The period cost is .
Costs use the upper limit of finite-horizon expected average costs from this empty initial state. is the infimum over all measurable, possibly time-dependent history policies in the canonical demand-path model, with independent uniform private randomization. Infinite expected costs remain infinite. A capped base-stock rule orders , with finite . Its cost is denoted by , and is the infimum over these parameters. Ordinary base stock at level is the same rule with cap .
For a demand block, let , including the zero empty sum. A pair is feasible when , , and, for both and ,
The lower certificate is the infimum of over this feasible set. Neither attainment of the infimum nor positivity of is assumed. Claim. For every feasible pair , let . The ordinary base-stock rule at this level satisfies
The base-stock rule is represented exactly by capped base stock with both level and cap equal to . Its cost is the long-run expected average from the empty initial state. In particular, the source's stationary expected-loss inequalities are not presumed to hold at each transient period.
import Definitions.Def_CappedBaseStock_Model open scoped ENNReal
namespace CappedBaseStock
theorem base_stock_cost_bound (P : DemandLaw) (c : Parameters) (r z : ℝ)
(feasible : CertificateFeasible P c r z) :
baseStockCost P c (Real.toNNReal (((c.L : ℝ) + 1) * r + 2 * z)) ≤
ENNReal.ofReal (2 * c.h * z +
((2 - 1 / ((c.L : ℝ) + 1)) * c.p + c.h * (c.L : ℝ)) * (mean P - r)) := by sorry
end CappedBaseStockRead-back
What the Lean code literally says, in plain math · GPT-6 (independent Codex sub-agent)
For every probability measure on the nonnegative real numbers whose identity function is integrable and whose real mean is strictly positive, every integer , every pair of real numbers and , and all real numbers satisfying the feasibility conditions below, the stated bound holds for a particular policy cost. For a nonnegative real demand sequence and integer , let , including the empty sum at , and let , where . The conditions are , , and for each of and . These are nonnegative Lebesgue integrals under the infinite independent product demand law and inequalities in the extended nonnegative reals , with each positive part and finite nonnegative right side embedded into that space. Set , a finite nonnegative real; the hypotheses make . Let have law and independently let have law equal to Lebesgue measure restricted to . Starting from and for all , define , , for , and for every integer . The recursion ignores , but costs are integrated over the stated joint law. With embedded in , define , where ranges over nonnegative integers and each integral is a nonnegative Lebesgue integral. The assertion is , with the finite right side embedded into ; this right side is already nonnegative under the hypotheses. All state, loss and order subtractions are truncated at zero as displayed, while the sums in and the expression inside use ordinary real subtraction. The average cost is defined in the extended nonnegative reals, allowing , and divides by the positive finite number . This is a bound for the explicitly specified level and equal order cap ; it does not take an infimum or assert an optimum is attained. The cases , zero demand values, , , and are included whenever feasible, and makes the displayed denominator nonzero.
Confirmed by the mission captain (proposal self-audit).