Stochastic Dynamic Lot-sizing with Supplier-Driven Substitution and Service Level Constraints

TL;DR

This paper develops a stochastic lot-sizing model with product substitution and service level constraints, proposing a chance-constrained policy that improves reliability and reduces costs in uncertain demand scenarios.

Contribution

It introduces a novel two-stage chance-constrained approach for multi-product lot-sizing with substitution, enhancing service levels and cost efficiency.

Findings

Substitution reduces costs by 7% to 25%.

Chance-constrained policy achieves higher service levels.

Limited substitution levels still provide significant benefits.

Abstract

We consider a multi-stage stochastic lot-sizing problem with service level constraints and supplier-driven product substitution. A firm has multiple products and it has the option to meet demand from substitutable products at a cost. Considering the uncertainty in future demands, the firm wishes to make ordering decisions in every period such that the probability that all demands can be met in the next period meets or exceeds a minimum service level. We propose a rolling-horizon policy in which a two-stage joint chance-constrained stochastic program is solved to make decisions in each time period. We demonstrate how to effectively solve this formulation. In addition, we propose two policies based on deterministic approximations. We demonstrate that the proposed chance-constraint policy can achieve the service levels more reliably and at a lower cost. We also explore the value of product substitution in this model, demonstrating that the substitution option allows achieving service levels while reducing costs by 7% to 25% in our experiments, and that the majority of the benefit can be obtained with limited levels of substitution allowed.