Production Planning Under Demand and Endogenous Supply Uncertainty

Mike Hewitt () and Giovanni Pantuso ()
Additional contact information
Mike Hewitt: Department of Information Systems and Supply Chain Management, Quinlan School of Business, Loyola University, Chicago, Illinois 60611
Giovanni Pantuso: Department of Mathematical Sciences, University of Copenhagen, 2100 Copenhagen, Denmark

INFORMS Journal on Computing, 2025, vol. 37, issue 4, 831-855

Abstract: 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.

Keywords: production planning; stochastic programming; endogenous uncertainty; Benders decomposition (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://dx.doi.org/10.1287/ijoc.2023.0067 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:orijoc:v:37:y:2025:i:4:p:831-855

Access Statistics for this article

More articles in INFORMS Journal on Computing from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().