Impulse Control with Discontinuous Setup Costs: Discounted Cost Criterion

TL;DR

This paper investigates optimal inventory control policies under discontinuous setup costs, revealing that generalized policies outperform traditional $(s,S)$-type policies in certain cases, and develops a new lower bound theorem for such problems.

Contribution

It introduces a generalized $(s, ext{S}(x))$ policy framework for discontinuous setup costs and proves its optimality in a broad setting, extending previous results.

Findings

$(s,S)$ policies are not always optimal under discontinuous costs.

Generalized $(s, ext{S}(x))$ policies can outperform traditional policies.

A new lower bound theorem is established for complex inventory optimization.

Abstract

This paper studies a continuous-review backlogged inventory model considered by Helmes et al. (2015) but with discontinuous quantity-dependent setup cost for each order. In particular, the setup cost is characterized by a two-step function and a higher cost would be charged once the order quantity exceeds a threshold $Q$. Unlike the optimality of $(s,S)$-type policy obtained by Helmes et al. (2015) for continuous setup cost with the discounted cost criterion, we find that, in our model, although some $(s,S)$-type policy is indeed optimal in some cases, the $(s,S)$-type policy can not always be optimal. In particular, we show that there exist cases in which an $(s,S)$ policy is optimal for some initial levels but it is strictly worse than a generalized $(s,\{S(x):x\leq s\})$ policy for the other initial levels. Under $(s,\{S(x):x\leq s\})$ policy, it orders nothing for $x>s$ and orders up to level $S(x)$ for $x\leq s$, where $S(x)$ is a non-constant function of $x$. We further prove the optimality of such $(s,\{S(x):x\leq s\})$ policy in a large subset of admissible policies for those initial levels. Moreover, the optimality is obtained through establishing a more general lower bound theorem which will also be applicable in solving some other optimization problems by the common lower bound approach.