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We model the hierarchical and decentralized nature of product transitions using a mixed-integer bilevel program with two followers, a manufacturing unit and an engineering unit. The leader, corporate management, seeks to maximize revenue over a finite planning horizon. The manufacturing unit uses factory capacity to satisfy the demand for current products. The demand for new products, however, cannot be fulfilled until the engineering unit completes their development, which, in turn, requires factory capacity for prototype fabrication. We model this interdependency between the engineering and manufacturing units as a generalized Nash equilibrium game at the lower level of the proposed bilevel model. We present a reformulation where the interdependency between the followers is resolved through the leader’s coordination, and we derive a solution method based on constraint and column generation. Our computational experiments show that the proposed approach can solve realistic instances to optimality in a reasonable time. We provide managerial insights into how the allocation of decision authority between corporate leadership and functional units affects the objective function performance. This paper presents the first exact solution algorithm to mixed-integer bilevel programs with interdependent followers, providing a flexible framework to study decentralized, hierarchical decision-making problems. 

The highly competitive nature of semiconductor manufacturing requires firms to constantly introduce new products with improved features and cost. Product divisions, which are responsible for product specification and demand forecasting, must collaborate with manufacturing and product engineering groups to develop new products and bring them into high-volume production for sale. We present a centralized optimization model for resource allocation across the different units. Computational experiments indicate that the model captures the interactions between agents in a logically consistent manner, providing a basis for decentralized approaches and stochastic formulations.