A multiperiod optimization model is proposed for addressing the supply chain optimization in continuous flexible process networks. The main feature of this study is that detailed operational decisions are considered over a short time horizon ranging from 1 week to 1 month. Forgiven flexible process networks where dedicated and flexible processes coexist, we take into account the supply chain for sales, intermittent deliveries, production shortfalls, delivery delays, inventory profiles, and job changeovers. The proposed optimization model requires efficient solution strategies to reduce the computational expense. We describe a bilevel decomposition algorithm that involves a relaxed problem (RP) and a subproblem (SP) for the original supply chain problem. Decisions for purchasing raw materials are made in the RP in which the changeover constraints are relaxed, yielding an upper bound to the profit, In the SP, fixing the delivery predicted in RP, the supply chain optimization is performed with job changeovers, yielding a lower bound. As will be shown in the examples, the algorithm achieves significant reduction in CPU time for the larger problems.
