Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/29855
Title: Robust inventory theory with perishable products
Author: Santos, Marcio Costa
Agra, Agostinho
Poss, Michael
Keywords: Lot-sizing
Integer programming
Robust optimization
Row-and-column generation algorithms
Issue Date: Jun-2020
Publisher: Springer
Abstract: We consider a robust inventory problem where products are perishable with a given shelf life and demands are assumed uncertain and can take any value in a given polytope. Interestingly, considering uncertain demands leads to part of the production being spoiled, a phenomenon that does not appear in the deterministic context. Based on a deterministic model we propose a robust model where the production decisions are first-stage variables and the inventory levels and the spoiled production are recourse variables that can be adjusted to the demand scenario following a FIFO policy. To handle the non-anticipativity constraints related to the FIFO policy, we propose a non-linear reformulation for the robust problem, which is then linearized using classical techniques. We propose a row-and-column generation algorithm to solve the reformulated model to optimality using a decomposition algorithm. Computational tests show that the decomposition approach can solve a set of instances representing different practical situations within reasonable amount of time. Moreover, the robust solutions obtained ensure low losses of production when the worst-case scenarios are materialized.
Peer review: yes
URI: http://hdl.handle.net/10773/29855
DOI: 10.1007/s10479-019-03264-5
ISSN: 0254-5330
Publisher Version: https://link.springer.com/article/10.1007%2Fs10479-019-03264-5
Appears in Collections:CIDMA - Artigos
DMat - Artigos
OGTCG - Artigos

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