Green Vehicle Routing for Mixed-Fleet Cold-Chain Distribution under Time- and Location-Dependent Traffic Congestion
This study proposes a mixed-fleet cold-chain vehicle routing model that integrates time- and location-dependent traffic congestion into a nonlinear energy-consumption framework and solves it using an improved adaptive large neighborhood search algorithm to minimize total distribution costs while demonstrating the operational and economic advantages of mixed fleets under dynamic congestion conditions.
Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer
In the bustling arteries of modern cities, the movement of goods is a constant, high-stakes ballet of timing and temperature. For perishable items like fresh produce, dairy, and medicine, the journey from a warehouse to a customer's door is not just about distance; it is a race against time and heat. If a delivery truck sits too long in traffic, the refrigeration unit must work harder, draining energy and raising the risk that the cargo will spoil. This delicate balance becomes even more complex as cities introduce electric trucks to reduce carbon emissions. While electric vehicles offer a cleaner path, they carry a different set of constraints: limited battery life and the need for charging. Logistics managers now face a difficult puzzle: how to mix these two types of vehicles, navigate the unpredictable rhythms of city traffic, and keep food fresh without spending a fortune. The challenge is compounded by the fact that traffic does not behave the same way everywhere or at every hour; a road might be clear at noon but gridlocked at 5 PM, and a specific neighborhood might be a bottleneck while the next block flows freely.
Researchers at Northeast Forestry University in China have tackled this intricate problem by developing a new way to plan delivery routes for a mixed fleet of electric and fuel-powered refrigerated trucks. They recognized that traditional planning methods often treat traffic as a static average or look at time and location separately, missing the reality that congestion is a shifting, three-dimensional phenomenon. To capture this, the team created a detailed model that breaks down every road segment into smaller pieces, calculating how long a truck takes to travel each piece based on the exact time of day and the specific congestion level of that zone. This approach allowed them to build a comprehensive cost calculator. This calculator does not just count miles; it accounts for the electricity or fuel burned by the engine, the extra energy needed to keep the cargo cold, the financial loss if food spoils due to delays, the penalties for arriving too early or too late, and the cost of carbon emissions. By feeding this complex data into a sophisticated search algorithm, the researchers could simulate millions of possible route combinations to find the most efficient path for a fleet of mixed vehicles.
The study focused on a specific scenario where a distribution center sends out trucks to serve many customers within strict time windows. The researchers tested their model using various city layouts and traffic patterns, ranging from small neighborhoods to large, sprawling districts. They found that the most effective strategy was rarely to use only electric trucks or only fuel-powered ones. Instead, a mixed fleet consistently proved to be the most economical and flexible solution. In their simulations, combining the two vehicle types allowed the system to adapt to the specific demands of the day. For instance, in areas with dense, clustered customers and shorter routes, electric trucks could thrive, leveraging their lower operating costs. However, when routes became long, dispersed, or hit severe traffic jams that threatened to drain a battery or miss a delivery window, the fuel-powered trucks provided the necessary range and reliability. The results showed that this hybrid approach reduced total distribution costs significantly compared to using a single type of vehicle, with the mixed fleet performing best across almost all tested scenarios.
The research also highlighted just how sensitive these delivery systems are to traffic conditions. The team ran simulations where they increased the duration of rush hour and the size of congested zones. They observed that as traffic worsened, the costs for spoilage, refrigeration, and time-window penalties rose sharply. Slower speeds meant trucks spent more time on the road, which not only consumed more energy but also increased the time that perishable goods were exposed to potential temperature fluctuations. In response to these harsher conditions, the algorithm naturally shifted its strategy, deploying more fuel-powered vehicles to ensure that deliveries were completed on time, even if it meant a slight increase in fuel consumption. This dynamic adjustment demonstrated that a rigid fleet cannot cope with the fluid nature of city traffic; the ability to switch between vehicle types based on real-time conditions is crucial for maintaining efficiency.
Furthermore, the researchers compared their new algorithm against several existing methods, including standard genetic algorithms and other search techniques. Their new method, which combines a large neighborhood search with a simulated annealing process, consistently found better solutions. It was particularly effective at escaping local traps—situations where a route looks good but is far from the best possible outcome—by occasionally accepting slightly worse routes to explore new possibilities. In tests involving up to one hundred customers, the new algorithm found the lowest cost routes in every instance, outperforming the other methods by a noticeable margin. The study suggests that for logistics companies aiming to go green without sacrificing reliability, the answer lies not in a single technology but in a smart, adaptive mix of technologies. By understanding the specific, shifting nature of traffic and matching it with the right blend of electric and fuel-powered vehicles, cities can move toward a future where cold-chain distribution is both environmentally friendly and economically viable.
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