Reverse Logistics Optimization Based on GI/G/m

Over the past few years, public awareness of environmental aspects has dramatically increased. Since the disposition of end-of-life products has caused tremendous attention, Reverse logistics system planning shall become vital as the take-back rate increases and the service area expands in the future. This study combines GI/G/m queuing model and adaptive genetic algorithm to optimize the capacity of the recovery facilities, the infrastructure design and the reverse network flow. The proposed model attempts to minimize the total cost, which consists of collection cost, transportation cost, fixed cost for facilities, variable cost, disposal cost, as well as the sale revenue of reclaimed materials and revenue of selling two-hands products.