Social, economic and green optimization of the distribution process of e-commerce platforms

Abstract

During the last ten years, online shopping has continuously increased while embedding growing sustainability concerns regarding environment and, especially, drivers working conditions. Therefore, this paper presents a multi-objective simulated annealing (MOSA) developed to deal with a goods distribution problem characterized by social, economic and green sustainability aspects. This contribution compares three scenarios. The first one is distinguished by diesel vehicles and it neglects the load inside them. The second scenario considers the variation of the vehicle load along its route. Finally, the third scenario employs electric vehicles instead of diesel ones. The developed MOSA is implemented in real-world instances and results show that the load-based scenario performs similar to the one which ignores it, but it is more realistic since just 30% of the route is traveled with no load inside. In addition, the load-based scenario is more reliable since the metabolic energy consumption of the drivers depends also on this feature. Regarding this social aspect, the proposed contribution shows that the solution of the Pareto frontier which optimizes this aspect provides routes more balanced among drivers in terms of metabolic energy consumption, considering the personal characteristic of each operator. Furthermore, this paper indicates that the electric vehicles are more efficient, economically and environmentally, than diesel ones just in small areas.