Hybrid Fleets Cut Logistics Costs and Emissions, Study Finds
A groundbreaking study from the University of Shanghai for Science & Technology reveals that logistics companies can significantly reduce both operational costs and carbon emissions by adopting a mixed fleet strategy—blending traditional internal combustion engine vehicles (ICEVs) with electric vehicles (EVs). The research, led by Qiu Yingying and Professor Gan Hongcheng, demonstrates that a strategically optimized hybrid fleet can lower total logistics costs by approximately 12% compared to a conventional all-diesel fleet, while simultaneously achieving substantial reductions in energy consumption and greenhouse gas output.
Published in the Journal of Chongqing Technology and Business University (Natural Science Edition), the study addresses a critical challenge in modern urban logistics: the environmental impact of last-mile delivery. While diesel-powered trucks have long dominated the sector, their contribution to air pollution and climate change has become increasingly untenable. At the same time, a full transition to electric fleets presents its own set of logistical hurdles, including limited driving range, lengthy charging times, and higher upfront vehicle costs. The researchers argue that a hybrid approach offers a pragmatic and economically viable middle ground.
The core of the study lies in a newly developed optimization model for vehicle routing problems (VRP) under low-carbon constraints. Unlike previous research that often focused solely on either ICEVs or EVs, this work explicitly models a mixed fleet, incorporating the distinct operational characteristics of both vehicle types. The model accounts for a comprehensive set of variables, including fixed vehicle costs, per-kilometer travel expenses, fuel and electricity consumption, carbon emission costs, and the constraints of customer time windows and vehicle load capacities.
The optimization framework is designed to minimize the total cost of a delivery operation, which includes the cost of vehicle deployment, the cost of fuel or electricity, and the financial cost associated with carbon emissions. By assigning a monetary value to CO₂ emissions, the model incentivizes solutions that are not only cheaper to operate but also more environmentally sustainable. This integration of environmental cost into the economic calculus of logistics planning is a key innovation, reflecting a shift towards true cost accounting in supply chain management.
To solve this complex optimization problem, the researchers employed a sophisticated algorithmic approach. They utilized a Particle Swarm Optimization (PSO) method, known for its efficiency in navigating large solution spaces, and enhanced it with the Metropolis criterion from simulated annealing. This hybrid algorithm is designed to avoid getting trapped in suboptimal local solutions, a common pitfall in such complex combinatorial problems. This computational robustness allowed the team to conduct a detailed comparative analysis between three fleet configurations: an all-diesel fleet, an all-electric fleet, and a mixed fleet of five diesel and five electric vehicles.
The results of the computational experiments were compelling. The all-diesel fleet, while requiring the fewest vehicles (four), incurred the highest total cost at nearly 17,000 units of currency. This high cost was driven by significant fuel consumption and the associated carbon emission fees. In contrast, the all-electric fleet, which required seven vehicles to cover the same set of delivery points, achieved the lowest total cost. This outcome underscores the fundamental economic advantage of electricity as a fuel source, which is cheaper and produces zero tailpipe emissions.
However, the mixed fleet emerged as the most balanced and practical solution. With a total cost of approximately 14,939 units, it represented a 12% reduction compared to the diesel fleet. While its cost was slightly higher than the all-electric option, it required three fewer vehicles and covered a significantly shorter total distance—783 kilometers versus over 1,080 kilometers for the all-electric fleet. This reduced mileage is a direct consequence of the range limitations of the EVs, which necessitated more frequent detours to charging stations, increasing the overall distance traveled.
The study’s findings highlight a crucial trade-off. An all-electric fleet minimizes energy and emission costs but can lead to higher operational complexity and longer delivery routes due to the need for charging infrastructure. A mixed fleet, on the other hand, leverages the long-range capability of diesel vehicles for longer hauls while using EVs for shorter, urban routes where their environmental benefits are most pronounced. This strategic allocation of vehicle types to specific tasks is what enables the hybrid model to achieve its cost and emission savings.
The research team conducted a detailed sensitivity analysis to explore how changes in EV technology could further improve the performance of a mixed fleet. They examined the impact of increasing the maximum load capacity of the electric vehicles. As the load capacity increased by 20%, 40%, and up to 100%, the overall delivery cost, energy cost, and carbon emissions all decreased. This is because a higher load capacity allows a single vehicle to serve more customers on a single route, reducing the total number of trips and the associated mileage.
Interestingly, the rate of cost reduction began to plateau as the load capacity continued to increase. The researchers attribute this to the fact that other constraints, such as customer time windows and the finite driving range of the EVs, eventually become the limiting factors. This suggests that simply building larger EVs is not a linear path to infinite savings. There is an optimal point where the benefits of increased capacity are balanced against the diminishing returns imposed by other logistical constraints and the potential for higher maintenance costs associated with heavier vehicles.
The second major area of sensitivity analysis focused on the charging rate of the EVs. Increasing the charging rate from the baseline had a pronounced positive effect. Faster charging means less time spent at a charging station, which directly translates to more time available for making deliveries. This reduction in idle time allows the fleet to better adhere to customer time windows and serve more customers within a given operating period.
As the charging rate improved, the total delivery cost, carbon emissions, and energy costs all declined. However, similar to the load capacity findings, the benefits of faster charging eventually reach a point of diminishing returns. Once the charging time is short enough that it no longer becomes a bottleneck in the delivery schedule, further increases in charging speed do not lead to significant improvements in the overall route optimization. The delivery paths and total costs stabilize, indicating that other factors, such as traffic patterns and customer locations, become the primary determinants of efficiency.
These sensitivity analyses provide actionable insights for logistics managers. They suggest that investing in EVs with higher load capacities and ensuring access to high-speed charging infrastructure can yield substantial operational benefits. For a company planning to transition to a greener fleet, these findings offer a clear roadmap: prioritize vehicles with sufficient payload and partner with charging network providers that offer rapid charging solutions.
The study also implicitly addresses a common concern among fleet operators: the fear that adopting EVs will compromise service reliability. By demonstrating that a mixed fleet can outperform a traditional diesel fleet in both cost and environmental impact, the research alleviates this concern. It shows that a transition to electrification does not have to be an all-or-nothing proposition. A gradual, phased approach, starting with a hybrid model, can deliver immediate benefits while allowing companies to build the necessary infrastructure and expertise for a future with a higher proportion of electric vehicles.
The implications of this research extend beyond individual companies. For policymakers, it provides a strong economic argument for supporting the adoption of electric and hybrid commercial vehicles. Subsidies for EV purchases, investments in public charging infrastructure, and the implementation of carbon pricing mechanisms all become more justified when the data shows a clear path to cost savings and emission reductions. The study’s model, which incorporates a cost for carbon, demonstrates how such policies can directly influence corporate decision-making, steering investment towards more sustainable technologies.
The authors also emphasize the importance of collaboration. They note that achieving a truly sustainable logistics sector will require a concerted effort from governments, businesses, academic institutions, and non-governmental organizations. Sharing best practices, pooling resources for research and development, and creating industry standards for data and charging can accelerate the transition. For logistics companies, actively engaging in these partnerships can provide access to new knowledge and help shape a regulatory environment that supports innovation.
From a broader environmental perspective, the 12% reduction in total costs achieved by the mixed fleet represents more than just a financial saving. It translates into a significant reduction in fossil fuel consumption and a corresponding decrease in CO₂ emissions. In the context of global efforts to combat climate change, such as those highlighted by the International Energy Agency, every percentage point of emission reduction counts. This research shows that the logistics sector, often seen as a major contributor to urban pollution, can be a key part of the solution.
The study’s methodology is also noteworthy for its realism. By incorporating practical constraints like partial charging (where a vehicle does not need to charge to 100% before resuming its route) and fixed customer time windows, the model reflects the complexities of real-world operations. This grounding in practical logistics makes the findings far more applicable to actual fleet managers than theoretical models that ignore such details.
In conclusion, the research by Qiu Yingying and Gan Hongcheng presents a compelling case for the adoption of mixed electric and diesel fleets in urban logistics. It moves beyond the simplistic dichotomy of “diesel versus electric” and instead offers a nuanced, data-driven strategy for optimizing fleet operations in a low-carbon economy. The 12% cost reduction is a powerful incentive for businesses, while the significant drop in carbon emissions aligns with global environmental goals. The sensitivity analyses further refine this strategy, showing that improvements in EV load capacity and charging speed are key levers for enhancing efficiency.
This work stands as a significant contribution to the field of green logistics. It provides a robust theoretical foundation and practical guidance for companies seeking to reduce their environmental footprint without sacrificing profitability. As the world continues to grapple with the dual challenges of economic growth and environmental sustainability, studies like this one illuminate a clear and achievable path forward for the transportation and logistics industry.
Qiu Yingying, Gan Hongcheng, University of Shanghai for Science & Technology, Journal of Chongqing Technology and Business University (Natural Science Edition), doi:10.16055/j.issn.1672-058X.2024.0006.015