This grant provides funding for developing optimization models and algorithms for analyzing the primary manufacturing operational stage of the pharmaceutical supply chain, which is responsible for the production of active pharmaceutical ingredients. The designed approach will determine optimal assignment and sequencing of pharmaceutical products to various processing bays of a primary manufacturing facility in order to meet customer requirements at minimal production costs (which include contributions from both the setups required and inventory incurred to achieve a quick turn-around), given a set of equipments with specified capacities as well as a product-mix and demand rate for each product. A decomposition-based methodology will be developed for determining (near-) optimal batching, assignment, and sequencing decisions. Certain important special cases that arise in practice will also be studied. The models and solution methodologies developed will be tested on real-life problem instances encountered by the Boehringer Ingelheim Chemical plant, located in Petersburg, Virginia, which is devoted to the production of active pharmaceutical ingredients and intermediaries for the pharmaceutical industry.
If successful, the results of this research will lead to increasing the responsiveness to market demand by reducing cycle time delays in practice, while curtailing manufacturing costs. The models to be investigated include as substructures the high multiplicity asymmetric travelling salesman problem and the classic job shop scheduling problem. The polyhedral analysis based on the proposed research effort will also serve to enhance the independent solution of these problems, which are important in their own right. In addition, the proposed research will contribute toward providing insights and computational tools for analyzing other batch production facilities, such as in semiconductor manufacturing.
