Theorem 1 — Exact lead-time bound and the guarantee
ProvedCappedBaseStock.mainConsider 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. Let . Claim.
and, as consequences,
These are multiplicative inequalities, including possible zero optimal cost. The statement does not require a minimizing feasible pair or an attained best capped policy. Its only assumptions are the demand-law, positive-cost, and integer-lead-time assumptions of the model; it does not assume the lower bound, either policy comparison, or existence of an optimal stationary realization.
import Definitions.Def_CappedBaseStock_Model open scoped ENNReal
namespace CappedBaseStock
theorem main (P : DemandLaw) (c : Parameters) :
CbsOpt P c ≤ ENNReal.ofReal (kappa c.L) * lowerCertificate P c ∧
CbsOpt P c ≤ ENNReal.ofReal (kappa c.L) * OPT P c ∧
ENNReal.ofReal (kappa c.L) * OPT P c ≤ (7 / 3 : ℝ≥0∞) * OPT P c := 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 such that is integrable and its real mean is strictly positive, and every integer and real numbers and , the following three inequalities hold. To specify their quantities, let have the infinite product law and let be independent of that sequence with law equal to Lebesgue measure restricted to the closed interval . An admissible policy consists of a measurable map for each integer ; its order at time is , so the history at has no demand coordinates. All orders and state coordinates are finite nonnegative real numbers, with no boundedness or integrability condition on orders beyond measurability. Starting with and for , define , for , and , where . The period cost is , viewed in the extended nonnegative reals , and the cost of the policy is , with ranging over nonnegative integers and the integrals being nonnegative Lebesgue integrals, possibly infinite. Put over all such admissible policies. For any finite , let be the same average cost for the recursion with , and put ; zero values of either parameter are included. For a demand sequence , real numbers , and an integer , set , including the empty sum at , and , viewed in . A pair is feasible precisely when , , and for each of and , where each integral is a nonnegative Lebesgue integral and the finite nonnegative right side is embedded into . Put , again as an infimum in , and . The assertion is . All costs, infima, inequalities and products in this assertion use the extended nonnegative reals; the displayed finite nonnegative coefficients are embedded in that space, an empty infimum is , and . The infima defining , and assert no attainment by any pair of parameters or policy. The assumption includes and keeps the displayed denominators nonzero; demands may equal zero even though their mean is strictly positive.
Confirmed by the mission captain (proposal self-audit).