Producción CyT
Capítulo de Libro
Autoría
L.J. Zeballos
;
C.A. Méndez
;
A.P. Barbosa-Povoa
;
A. Novais
Fecha
2014
Editorial y Lugar de Edición
University of Calabria
Libro
Proceedings of the international Workshop on Innovation for Logistics
(pp. 188-195)
University of Calabria
University of Calabria
ISBN
978-953-7738-29-7
Resumen
Información suministrada por el agente en
SIGEVA
In this paper, a two-stage stochastic programming approach is used for analyzing the behavior of Closed-Loop Supply Chains (CLSCs) with uncertainty in supply, customer demands and return rates when the design and planning problems are considered. In addition, as part of the planning problem, the approach takes into account the capacity constraints on production, distribution and storage, as well as operational and environmental costs. The approach considers, by means of scenarios, the simultane...
In this paper, a two-stage stochastic programming approach is used for analyzing the behavior of Closed-Loop Supply Chains (CLSCs) with uncertainty in supply, customer demands and return rates when the design and planning problems are considered. In addition, as part of the planning problem, the approach takes into account the capacity constraints on production, distribution and storage, as well as operational and environmental costs. The approach considers, by means of scenarios, the simultaneous integration of the three uncertainty sources. Since in stochastic approaches another important aspect to be considered is the selection of the objective function, this paper includes three performance measures that search the revenue maximization. Two objective functions regard risk criteria with the objective of achieving more robust solutions. The effectiveness of the proposed approach is established by means of an example considering different supply, demand and return rates states. Thus, employing the stochastic programming approach, novel insights related to the behavior of CLSCs and the flow of returned products are derived.
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Palabras Clave
Supply ChainOptimizationScenario