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This research proposes a multi-period, multi-product, multi-echelon closed-loop supply chain network design model under uncertainty.
We consider a one-period model under uncertainty.
Numerous researchers, such as Federgruen and Heching (1999), presented a combined pricing and inventory control model under uncertainty.
This is a one period model under uncertainty that borrows features from Ehrlich (1973) and Lochner and Moretti (2001).
Thus, the finally obtained linear model under uncertainty can be further converted to deterministic linear models, which can be solved by conventional algorithms.
This study endeavoured to present a development of a green supply chain network model under uncertainty of parameters such as customer demand as well as suppliers, production, and casting capacity.
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More precisely, it focuses on the parametric study of engineering models under uncertainty.
A standard result of life‐cycle models under uncertainty is that optimizing individuals equate the expected marginal utility of consumption across states of the world if insurance is available at actuarially fair rates.
A modeling and solution methodology as presented here can contribute to the efficient solution of network design models under uncertainty for reverse logistics.
In this paper an energy-based Bayesian wavelet method is presented for validation assessment of multivariate predictive models under uncertainty, using time-series data collected from a dynamic system.
A comparative study between the proposed hybrid solution approach and other solution approaches used to solve multi-objective models under uncertainty.
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