Bi-objective green vehicle routing problem with heterogeneous regular vehicles and occasional drivers joint delivery

The paper investigates a bi-objective green vehicle routing problem (GVRP) involving heterogeneous regular vehicles and occasional drivers joint delivery (GVRP-HVOV). This includes soft and hard time windows, earliness, and tardiness penalties. This study proposed for the first time to apply the crowdsourced mode to the road cargo transportation of trucks, and further expanded the scope of use of the crowdsourced mode. Considering the difference between the vehicle’s own attributes and road conditions between truck transportation and the last kilometer delivery, a new crowdsourced mode setting and vehicle path setting were established for truck transportation. A bi-objective programming model is established to minimize total transportation cost as the first objective and reduce total greenhouse gas emissions as the second objective, aiming to strike a balance between transportation cost and carbon emissions, minimizing both. To address this problem, an improved non-dominated sorting genetic algorithm II (NSGA II) is proposed to enhance the quality of the generated population. Additionally, the algorithm’s performance is evaluated using different scale test data to demonstrate its superiority. Finally, instance tests from Walmart show that joint delivery with occasional drivers effectively reduces total costs and greenhouse gas emissions. The study provides useful recommendations for actual logistics companies in planning vehicle types and applying new models. Additionally, sensitivity analysis on the available number of occasional drivers and their delivery range offers a more comprehensive analysis of the impact of joint delivery with occasional drivers on total costs and carbon dioxide emissions in the delivery process.