Optimal Contract Selection for Contract Manufacturing Organisations in the Pharmaceutical Industry Under Uncertainty

Abstract

This work presents an integrated planning and scheduling framework for the optimal contract selection problem of Contract Manufacturing Organisations (CMO) under uncertainty. Considering a multistage, multiproduct, batch facility of a secondary pharmaceutical industry, an aggregated MILP planning model is firstly proposed including material balances and allocation constraints. Utilizing a rolling horizon approach, the production targets are then provided to a precedence-based MILP scheduling model to define batch-sizing and sequencing decisions in detail. To model demand uncertainty, a scenario-based approach is proposed, considering the Value-at-Risk (VaR) and Conditional Value-at-Risk (CVaR) measures. Since large number of scenarios imposes significant challenge to computations, a scenario reduction framework is integrated to reduce the total solution time, when considering large-scale problem instances. The proposed methodology increases the profitability of CMOs by selecting the optimal contract combinations depending on their risk tolerance while considering the availability and optimal utilization of underlying production resources.