Robust inventory theory with perishable products

Marcio Costa Santos (), Agostinho Agra () and Michael Poss ()
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Marcio Costa Santos: Grupo de Estudos em Otimização e Aprendizado de Máquinas (NEMO) Universidade fereral do Ceará – Campus Russas
Agostinho Agra: University of Aveiro
Michael Poss: LIRMM, University of Montpellier, CNRS

Annals of Operations Research, 2020, vol. 289, issue 2, No 19, 473-494

Abstract: 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.

Keywords: Lot-sizing; Integer programming; Robust optimization; Row-and-column generation algorithms (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (5)

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DOI: 10.1007/s10479-019-03264-5

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