A Multi-Stage Stochastic Programming Model for the Multi-Echelon Multi-Period Reverse Logistics Problem

Reverse logistics planning plays a crucial role in supply chain management. Stochasticity different parameters along with time horizon can be challenge solving reverse problems. This paper proposes multi-stage, multi-period lot sizing decisions under uncertainties. The main uncertain factors are return and demand quantities, quality. Moment matching method was adopted to generate discrete set of scenarios represent the original continuous distribution stochastic parameters. Fast forward selection algorithm employed select most representative facilitate computational tractability. A case study conducted optimal solution recursive problem obtained by extensive form. Sensitivity analysis implemented on elements solution. Results sow that (RP) outperforms from expected values (EEV).