Maximizing the Long-run Average Expected Profit of a Periodic-Review Assemble-to-Order System

This study considers an assemble-to-order system with periodic-reviews and a general bill of materials. The objective is to maximize the long-run average expected profit through policies including product price and component replenishment and allocation. An upper bound on the objective for all feasible policies is established through a two-stage stochastic programme (SP) which inspires the construction of a myopic policy. Particularly, the first-stage SP solution specifies the price and replenishment decisions, while the allocation decision resembles the second-stage SP recourse solution. A lower bound on the optimal policy is also provided. Numerical experiment results demonstrate that both the upper bound and the proposed policy are effective. This study brings new perspectives to supply chain management of assemble-to-order systems and suggests ways to improve profitability.