An integrated sustainable model of mobile facility location-routing scheduling under uncertainty considering environmental parameters

In this study, a mobile facility location-routing scheduling is presented in an innovative way. The aim of this model is for minimizing the total costs and carbon dioxide emissions. Optimizing the objectives of this model simultaneously following the location of main warehouses, the establishment of intermediate warehouses (mobile facility) and vehicle routing showed that with the reduction of carbon dioxide emissions, the costs of using vehicles and routing will increase. Also, with the increase in the uncertainty rate in demand parameters and transportation costs, in addition to the increase in total costs, the quantity of carbon dioxide emissions has also been incremented. In this article, by introducing a new multi-objective hybrid algorithm called MOWPSO, it was observed that the operators of this algorithm have a high ability to search the solution space and achieve effective solutions with the greatest expansion. This method is more efficient than other advanced algorithms and can produce efficient solutions in a period of time close to MOPSO and MOWOA algorithms. So that MOWPSO has obtained the highest mean efficient solution (NPF). While the average time to solve sample problems by this method was 205.76 seconds. The MOPSO algorithm has the lowest average solution time (Time) and the MOWOA algorithm has the lowest metric distance (SM).