Abstract
A De Novo programming approach for a robust closed-loop supply chain network design under uncertainty: An M/M/1 queueing model
Highlights
In recent years, closed-loop supply chain networks design has widely attracted researchers’ attention due to the advantages of jointly managing the reverse and forward supply chains
This paper provides a framework to study the uncertain behavior of the parameters in a Closed-loop supply chain (CLSC) model accompanied with a queueing system and the De Novo programming approach
To respond the need for capacity determination, this paper has proposed a De Novo-based closed-loop supply chain model, which considers the effects of establishing a queueing system in each recovery center
Summary
In recent years, closed-loop supply chain networks design has widely attracted researchers’ attention due to the advantages of jointly managing the reverse and forward supply chains. In reverse flow of the CLSCs the quantity of returned products are considerably uncertain, so in spite of cost increment due to applying HWRP, the hard worst case robust optimization will be the best approach to handle the uncertainty of parameters (Pishvaee et al, 2011). According to the above-mentioned descriptions, the contribution of this research is twofold; first, we tackle the capacity determination of the recovery centers in the reverse flow of a CLSC by use of a De Novo programming while considering the effects of a queueing system in these centers. We use a hard worst case robust optimization to handle the uncertainty of parameters and integrate it with an interactive fuzzy programming approach to cope with the robustness of the bi-objective CLSC model.
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