We study the problem of determining how much finished goods inventory to source from different capacitated facilities in order to maximize profits resulting from sales of such inventory. We consider a problem wherein there is uncertainty in demand for finished goods inventory and production yields at facilities. Further, we consider that uncertainty in production yields is endogenous, as it depends on both the facilities where a product is produced and the volumes produced at those facilities. We model the problem as a two stage stochastic program and propose an exact, Benders-based algorithm for solving instances of the problem. We prove the correctness of the algorithm and with an extensive computational study demonstrate that it outperforms known benchmarks. Finally, we establish the value in modeling uncertainty in both demands and production yields. History: Accepted by Andrea Lodi, Area Editor for Design & Analysis of Algorithms–Discrete. Supplemental Material: Software that implements the algorithms found in this paper, as well as the instances used in the computational study, can be found at Hewitt and Pantuso (2024) . The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0067 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0067 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

This paper presents a solution scheme for a class of multistage stochastic programs (possibly mixed-integer at all stages) in which a hierarchy of decisions emerges. A special structure, common to many strategic problems affected by uncertainty, allows decomposing the problem into a master problem and many independent linear programming subproblems, facilitating the isolation and reduction of the complicating mixed-integer component of the problem. Specialized (possibly heuristic) procedures can be used for solving the master problem while subproblems can be efficiently solved to optimality. We adapt and test the decomposition scheme for a case of the maritime fleet renewal problem, whose real life instances cannot be solved by means of commercial off-the-shelf solvers.