A bi-level multi-follower optimization model for R&D project portfolio: an application to a pharmaceutical holding company - Rennes School of Business Access content directly
Journal Articles Annals of Operations Research Year : 2023

A bi-level multi-follower optimization model for R&D project portfolio: an application to a pharmaceutical holding company

Abstract

The need for a study of project portfolio optimization in pharmaceutical R&D has become all the more urgent with the outbreak of COVID-19. This study examines a new model for optimizing R&D project portfolios under a decentralized decision-making structure in a pharmaceutical holding company. Specifically, two levels of decision makers hierarchically decide on budget allocation and project portfolio selection-scheduling to maximize their profit, and we formulate the problem as a bi-level multi-follower mixed-integer optimization model. At the upper level, the investment company has complete knowledge of the subsidiaries' response, acts first, and decides on the best budget allocation. At the lower level, each subsidiary responds to the allocated budget and decides on its portfolio scheduling. Since the lower level represents several mixed-integer programming problems, solving the resulting bi-level model is challenging. Therefore, we propose an efficient hybrid solution approach based on parametric optimization and convert the bi-level model into a single-level mixed-integer model. To validate it, we solve a case and discuss the optimal strategy of each actor. The experimental results show that the planned project portfolio for each subsidiary of the holding company is drastically affected by the allocated budget and its decisions.

Dates and versions

hal-04059561 , version 1 (05-04-2023)

Identifiers

Cite

Faraz Salehi, S. Mohammad J. Mirzapour Al-E-Hashem, S. Mohammad Moattar Husseini, S. Hassan Ghodsypour. A bi-level multi-follower optimization model for R&D project portfolio: an application to a pharmaceutical holding company. Annals of Operations Research, 2023, 323 (1-2), pp.331-360. ⟨10.1007/s10479-022-05052-0⟩. ⟨hal-04059561⟩
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