Robust Optimization for Lean Supply Chain design under disruptive risk

This paper aims at presenting the new 2-stage framework of Robust Optimization for Lean Supply Chain design under uncertainty by using the so-called Dual Lean Filter. First, we formulate one quantitative model of Fat Supply Chain in stable circumstance based on the six-performance drivers of Chopra and Meindl, (2013). Then, we propose one novel procedure called Forward Lean Filter in order to transform Fat Supply Chain into Lean Supply Chain. Afterwards, in second phase, we investigate the Lean Supply Chain model under disruptive risk, in which Reverse Lean Filter is introduced to prevent the Lean system from returning Fat form under threats. Both stages are optimized by one meta-heuristics namely priority-based Genetic Algorithm. All aforementioned processes are illustrated in one numerical supply chain under the risk of disruption its key distribution center.