Authors:
Carlos E. Testuri
1
;
Héctor Cancela
1
and
Víctor M. Albornoz
2
Affiliations:
1
Depto. de Investigación Operativa, Instituto de Computación, Facultad de Ingeniería, Universidad de la República, J. Herrera y Reissig 565, 11300 Montevideo and Uruguay
;
2
Depto. de Industrias, Campus Santiago Vitacura, Universidad Técnica Federico Santa María, Avda. Santa María 6400, Vitacura, Santiago and Chile
Keyword(s):
Stochastic Lot-sizing, Multi-stage Stochastic Integer Programming, Valid Inequalities.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Inventory Theory
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Resource Allocation
;
Stochastic Optimization
;
Supply Chain Management
;
Symbolic Systems
Abstract:
The problem addresses the expected cost minimization of meeting the uncertain demand of a product during a discrete time planning horizon. The product is supplied by selecting fixed quantity shipments that have lead times. Due to the uncertainty of demand, corrective actions, such as shipment cancellations and postponements, must be taken with associated costs and delays. The problem is modeled as an extension of the discrete lot-sizing problem with different capacities and uncertain demand, which belongs to the N P-hard class. To improve the resolution of the problem by tightening its formulation, valid inequalities based on the (,̀S) inequalities approach are used. Given that the inequalities are highly dominated for most experimental instances, a scheme is established to determine undominated ones. Computational experiments are performed on the resolution of the model and variants that include subsets of undominated and representative valid inequalities for instances of several in
formation structures of uncertainty. The experimental results allow to conclude that the inclusion of undominated and representative derived (,̀S) valid inequalities enable a more efficient resolution of the model.
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