Browsing by Author "Krishnamurthy, A."

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Open Access
Contracts for biopharmaceutical manufacturing based on production cost and capabilities
(Taylor & Francis, 2023-06-09) Limon, Yasemin; Martagan, T.; Krishnamurthy, A.
trategic collaborations are critical to the success of efforts aimed at discovering new drugs and therapies in the biopharmaceutical industry. These collaborations aim to leverage domain expertise, asymmetries in production costs and/or capabilities to improve efficiency. Despite increasing collaborations, the biopharmaceutical industry lacks a structured guideline for choosing contracts. We present contract models with effort-based formulations that capture the key characteristics of biopharmaceutical operations and analyse incentive mechanisms such as fixed payment, revenue-sharing, risk-sharing, and cost-sharing in biopharmaceutical collaborations. We show that traditional incentive schemes do not achieve supply chain coordination, although they are commonly used in the industry. We introduce a new contract model called fee-for-effort-and-output contract that encourages the parties to exert higher efforts by offering discounts on their operating costs, and show that this contract achieves coordination with an appropriate selection of contract parameters. We also investigate the efficiency of noncoordinating contracts with traditional incentive schemes and identify the capability and cost structures under which they achieve the highest efficiency possible, to both determine the next-best alternatives and explain their popularity in practice.
Open Access
Dynamic resource scheduling of biomanufacturing projects
(Elsevier, 2020-05) Limon, Yasemin; Krishnamurthy, A.
We consider the scheduling problem for biomanufacturing projects that involve multiple tasks and “no-wait” constraints between some of these tasks. The aim is to create schedules that ensure timely delivery of products while enabling schedule revisions to accommodate additional constraints realized during the execution of these projects. We formulate the problem as a mixed-integer linear programming model with the objective of minimizing total tardiness, and propose a dynamic scheduling approach that solves a series of modified mixed-integer linear programming models to revise and improve the schedule. We conduct numerical studies to investigate the performance of this approach and compare its performance to a traditional proactive scheduling approach. In collaboration with biomanufacturing companies, we create a scheduling tool and validate the approach with implementation in an industry setting.