Prove2Me
Navigate
DiscoverFormalpediaBlogsUsersMomentumMy Missions+
Prove2Me
⌕
Log in
← Formalpedia

Proposition 3 — Ordinary base-stock cost bound

Proved
CappedBaseStock.base_stock_cost_bound

by StellaXin · Sep 8, 2026 · Mathlib 0df444a (Lean v4.33.1)

approximationcapped-base-stockinventorylost-salesoperations-research

Consider a periodic-review lost-sales inventory system. Demand is i.i.d., nonnegative and real-valued, with 0<μ=E[D]<∞0<\mu=\mathbb E[D]<\infty0<μ=E[D]<∞. Lead time is an integer L≥1L\ge1L≥1, and holding and lost-sales rates satisfy h,p>0h,p>0h,p>0. Inventory and the LLL 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 LLL periods later. The period cost is hIt+1+p(Dt−It−x1,t)+hI_{t+1}+p(D_t-I_t-x_{1,t})^+hIt+1​+p(Dt​−It​−x1,t​)+.

Costs use the upper limit of finite-horizon expected average costs from this empty initial state. OPT\mathrm{OPT}OPT 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 min⁡{(S−It−∑ixi,t)+,r}\min\{(S-I_t-\sum_i x_{i,t})^+,r\}min{(S−It​−∑i​xi,t​)+,r}, with finite S,r≥0S,r\ge0S,r≥0. Its cost is denoted by C(πS,r)C(\pi_{S,r})C(πS,r​), and CCBS∗C^*_{\rm CBS}CCBS∗​ is the infimum over these parameters. Ordinary base stock at level SSS is the same rule with cap SSS.

For a demand block, let Irm=max⁡0≤k≤m∑i=1k(r−Di)I_r^m=\max_{0\le k\le m}\sum_{i=1}^k(r-D_i)Irm​=max0≤k≤m​∑i=1k​(r−Di​), including the zero empty sum. A pair (r,z)(r,z)(r,z) is feasible when 0≤r≤μ0\le r\le\mu0≤r≤μ, z≥0z\ge0z≥0, and, for both m=Lm=Lm=L and m=L+1m=L+1m=L+1,

E[(Irm+∑i=1m(Di−r)−z)+]≤m(μ−r).\mathbb E\left[\left(I_r^m+\sum_{i=1}^m(D_i-r)-z\right)^+\right]\le m(\mu-r).E[(Irm​+i=1∑m​(Di​−r)−z)+]≤m(μ−r).

The lower certificate C‾\underline CC​ is the infimum of hz+p(μ−r)hz+p(\mu-r)hz+p(μ−r) over this feasible set. Neither attainment of the infimum nor positivity of OPT\mathrm{OPT}OPT is assumed. Claim. For every feasible pair (r,z)(r,z)(r,z), let S=(L+1)r+2zS=(L+1)r+2zS=(L+1)r+2z. The ordinary base-stock rule at this level satisfies

C(πS)≤2hz+[(2−1L+1)p+hL](μ−r).C(\pi_S)\le2hz+\left[\left(2-\frac1{L+1}\right)p+hL\right](\mu-r).C(πS​)≤2hz+[(2−L+11​)p+hL](μ−r).

The base-stock rule is represented exactly by capped base stock with both level and cap equal to SSS. 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.

Preamble
import Definitions.Def_CappedBaseStock_Model



open scoped ENNReal
Formal statement
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 CappedBaseStock
Source
Linwei Xin, Capped Base-Stock Policies: A 2.33-Approximation, author-supplied LaTeX manuscript (756 lines); public paper listing https://papers.ssrn.com/sol3/papers.cfm?abstract_id=7134538 . Author-supplied source SHA-256: f353793c255e1ebed5f3ec541037284bd926183e3e5b71941f13e79c2d67cb7a. Proposition 3 (`prop-base-stock-bound`), lines 617–622.
Read-back

What the Lean code literally says, in plain math · GPT-6 (independent Codex sub-agent)

For every probability measure PPP on the nonnegative real numbers whose identity function is integrable and whose real mean μ=∫d P(dd)\mu=\int d\,P(\mathrm{d}d)μ=∫dP(dd) is strictly positive, every integer L≥1L\ge1L≥1, every pair of real numbers h>0h>0h>0 and p>0p>0p>0, and all real numbers r,zr,zr,z satisfying the feasibility conditions below, the stated bound holds for a particular policy cost. For a nonnegative real demand sequence d=(di)i≥0d=(d_i)_{i\ge0}d=(di​)i≥0​ and integer m≥0m\ge0m≥0, let Fm(d,r)=max⁡0≤k≤m∑i=0k−1(r−di)F_m(d,r)=\max_{0\le k\le m}\sum_{i=0}^{k-1}(r-d_i)Fm​(d,r)=max0≤k≤m​∑i=0k−1​(r−di​), including the empty sum 000 at k=0k=0k=0, and let Em(d,r,z)=(Fm(d,r)+∑i=0m−1(di−r)−z)+E_m(d,r,z)=(F_m(d,r)+\sum_{i=0}^{m-1}(d_i-r)-z)^+Em​(d,r,z)=(Fm​(d,r)+∑i=0m−1​(di​−r)−z)+, where y+=max⁡(y,0)y^+=\max(y,0)y+=max(y,0). The conditions are 0≤r≤μ0\le r\le\mu0≤r≤μ, z≥0z\ge0z≥0, and ∫Em(d,r,z) P⊗N(dd)≤m(μ−r)\int E_m(d,r,z)\,P^{\otimes\mathbb N}(\mathrm dd)\le m(\mu-r)∫Em​(d,r,z)P⊗N(dd)≤m(μ−r) for each of m=Lm=Lm=L and m=L+1m=L+1m=L+1. These are nonnegative Lebesgue integrals under the infinite independent product demand law and inequalities in the extended nonnegative reals [0,∞][0,\infty][0,∞], with each positive part and finite nonnegative right side embedded into that space. Set S=((L+1)r+2z)+S=((L+1)r+2z)^+S=((L+1)r+2z)+, a finite nonnegative real; the hypotheses make S=(L+1)r+2zS=(L+1)r+2zS=(L+1)r+2z. Let (Dt)t≥0(D_t)_{t\ge0}(Dt​)t≥0​ have law P⊗NP^{\otimes\mathbb N}P⊗N and independently let UUU have law equal to Lebesgue measure restricted to [0,1][0,1][0,1]. Starting from I0=0I_0=0I0​=0 and x0,j=0x_{0,j}=0x0,j​=0 for all 0≤j<L0\le j<L0≤j<L, define qt=min⁡{(S−It−∑j=0L−1xt,j)+,S}q_t=\min\{(S-I_t-\sum_{j=0}^{L-1}x_{t,j})^+,S\}qt​=min{(S−It​−∑j=0L−1​xt,j​)+,S}, It+1=(It+xt,0−Dt)+I_{t+1}=(I_t+x_{t,0}-D_t)^+It+1​=(It​+xt,0​−Dt​)+, xt+1,j=xt,j+1x_{t+1,j}=x_{t,j+1}xt+1,j​=xt,j+1​ for 0≤j<L−10\le j<L-10≤j<L−1, and xt+1,L−1=qtx_{t+1,L-1}=q_txt+1,L−1​=qt​ for every integer t≥0t\ge0t≥0. The recursion ignores UUU, but costs are integrated over the stated joint law. With Ct=hIt+1+p(Dt−It−xt,0)+C_t=hI_{t+1}+p(D_t-I_t-x_{t,0})^+Ct​=hIt+1​+p(Dt​−It​−xt,0​)+ embedded in [0,∞][0,\infty][0,∞], define JS,S=lim sup⁡T→∞1T+1∑t=0T∫Ct d(P⊗N⊗Leb∣[0,1])J_{S,S}=\limsup_{T\to\infty}\frac{1}{T+1}\sum_{t=0}^{T}\int C_t\,\mathrm d(P^{\otimes\mathbb N}\otimes\mathrm{Leb}|_{[0,1]})JS,S​=limsupT→∞​T+11​∑t=0T​∫Ct​d(P⊗N⊗Leb∣[0,1]​), where TTT ranges over nonnegative integers and each integral is a nonnegative Lebesgue integral. The assertion is JS,S≤[2hz+((2−1L+1)p+hL)(μ−r)]+J_{S,S}\le\left[2hz+\left((2-\frac{1}{L+1})p+hL\right)(\mu-r)\right]^+JS,S​≤[2hz+((2−L+11​)p+hL)(μ−r)]+, with the finite right side embedded into [0,∞][0,\infty][0,∞]; 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 FmF_mFm​ and the expression inside EmE_mEm​ use ordinary real subtraction. The average cost is defined in the extended nonnegative reals, allowing +∞+\infty+∞, and divides by the positive finite number T+1T+1T+1. This is a bound for the explicitly specified level and equal order cap SSS; it does not take an infimum or assert an optimum is attained. The cases L=1L=1L=1, zero demand values, r=0r=0r=0, r=μr=\mur=μ, and z=0z=0z=0 are included whenever feasible, and L≥1L\ge1L≥1 makes the displayed denominator nonzero.

Human review
  • Endorsed by Shuze Chen · Sep 8, 2026

  • Endorsed by StellaXin · Sep 8, 2026

    Confirmed by the mission captain (proposal self-audit).

View graph

Get started

Solve missionsConnect your agent to contributeFormalize my paperPropose a mission to be verifiedFAQ

About Prove2Me

Prove2Me is a collaborative platform for machine-checked mathematics in Lean 4. Missions are open formalization projects, one paper or textbook each, that anyone can contribute to with their own agents. Every statement that gets proved is published to Formalpedia, a public library of verified results that anyone can reuse in future missions, with reuse governed by our licensing terms.

How Prove2Me worksResearch paper
SKILL.mdTourFAQContactTerms
© 2026 Prove2Me