1.What Is Route Optimization？
Route Optimization refers to the process of planning the most logical and efficient routes and visit sequences for vehicles or personnel across a set of delivery or service tasks, with the objective of minimizing overall costs or maximizing operational efficiency. Simply put, it is the optimization of delivery and service routes.
It is not merely about finding a navigation path between two points. Instead, it involves determining where to go first, where to go next, who should perform the task, and when it should be carried out. Route optimization typically must also satisfy multiple operational constraints simultaneously, such as vehicle capacity, delivery time windows (e.g., customers can only receive goods within specific time periods), driver working hour limits, mandatory return-to-depot requirements, cold chain delivery conditions, restricted or prohibited routes, and priority orders.
As the number of task locations increases, the number of possible route permutations grows exponentially. As a result, such problems are theoretically classified as classic combinatorial optimization challenges. Representative examples include the Traveling Salesman Problem (TSP) and the Vehicle Routing Problem (VRP).
In practice, the objectives of route optimization extend far beyond simply minimizing distance. They may include reducing fuel consumption, minimizing total travel time, lowering late-delivery penalties, minimizing the number of vehicles required, reducing waiting time, and even improving on-time performance and overall customer satisfaction.
In other words, route optimization is fundamentally about allocating limited resources—such as vehicles, personnel, time, and fuel—across a set of tasks and constraints to identify the decision方案 that best aligns with business objectives. It addresses global decision-making rather than local navigation: navigation helps you get from point to point, whereas route optimization takes responsibility for the entire operational plan.
2.What Is Workload Balancing？
Workload Balancing refers to the concept of ensuring that the work burden among different vehicles or personnel does not differ significantly. “Workload” is not limited to total mileage—it may also include total working hours, number of stops or tasks, service time, cargo weight, route complexity, customer difficulty, loading/unloading time, and regional traffic pressure, among other factors.For example, even if two personnel each cover 80 kilometers, the experience can be vastly different: one may drive smoothly on a highway, while the other navigates city streets with 30 stops, searching for parking at each location and carrying goods upstairs. In this case, the perceived workload is clearly not equivalent.
Workload balancing aims for fairness and stability, rather than making every individual’s work easy. Its goal is to avoid extreme imbalances—for example, one vehicle may not return to the depot until 6:00 PM, while another is already idle by 2:30 PM; or a driver may consistently handle the most demanding routes each week, leading to accumulated fatigue and ultimately resulting in sick leave or turnover.
Workload balancing can also be viewed as a form of management-level risk control, as excessive imbalance leads to numerous “non-quantifiable” costs, including complaints, coordination friction, internal conflicts, declining quality, increased accident risk, and workforce attrition.In practice, workload balancing does not usually aim to make everyone’s workload exactly equal. Instead, an acceptable range is established—for example, ensuring that each vehicle’s working hours fall between 7.5 and 8.5 hours, or that the number of stops per vehicle does not exceed a certain allowable difference.
3.Why Workload Balancing Matters in Route Optimization？
You might think that since the goal of route optimization is efficiency, it would make sense to assign the most tasks or the easiest, fastest routes to just a few vehicles. The problem, however, is that logistics scheduling is not a one-time mathematical problem—it is a system that operates daily.
When the focus is solely on minimizing distance or time, algorithms can easily produce results that are mathematically elegant but operationally harsh: a large number of tasks may be assigned to certain vehicles simply because they are closer to the depot or happen to form the shortest route, while other vehicles are left with fragmented tasks or even idle. In the short term, total mileage may decrease and fuel costs may be saved, but in the medium to long term, this approach incurs far greater hidden costs.First, exceeding driver working hours or causing fatigue driving directly increases safety risks and accident-related costs. Second, imbalance can lead to unstable service quality: overburdened vehicles are prone to delays, mistakes, and rushed service, resulting in lower customer satisfaction, more complaints, and even potential contract losses. Third, excessive imbalance can damage team morale, leading to higher staff turnover, and recruiting and training new personnel is often more costly than covering a few extra kilometers. Fourth, in scenarios with time windows or multiple constraints, imbalance can reduce overall feasibility and resilience, limiting the system’s ability to adapt to changes.
When a vehicle’s schedule is too tightly packed, even a small disruption—such as a last-minute order, traffic congestion, or a 15-minute delay at a single stop—can trigger a chain reaction, causing delays to cascade throughout the entire route. Conversely, when workloads are more evenly distributed and each vehicle has some buffer, the system is better able to absorb on-site uncertainties.Therefore, workload balancing is not an “optional courtesy”—it is a necessary condition for turning route optimization from a theoretical exercise into sustainable operations. Put more plainly: you can pursue the shortest routes, but you cannot pursue them to the extent of treating people as infinitely extendable components.
4.How Workload Balancing Impacts Route Optimization？
Once workload balancing is incorporated into route optimization, the most immediate impact is that the definition of the “optimal solution” changes.
Originally, you might focus solely on minimizing total distance or travel time. Once a workload balance objective is added, the problem becomes a multi-objective or penalty-based optimization. This increases the complexity of finding a solution and means that the result is no longer a single “shortest route.” Instead, it becomes a compromise solution: total distance may increase slightly, but working hour differences shrink, the risk of delays decreases, and operational stability improves.This kind of “trading a bit of efficiency for stability” is often more cost-effective in practice. A second impact is that routes and task assignments become more dispersed rather than concentrated. For example, all stops in a city center might originally be assigned to a single vehicle, forming a neat and dense route. With workload balancing, some of these stops are reassigned to another vehicle, meaning both vehicles enter the city. While total mileage may increase, the number of stops and service time for each vehicle becomes more balanced.
Conversely, introducing workload balancing makes the system easier for people to accept. No matter how strong the optimization is, if drivers feel they are consistently assigned the most demanding routes, resistance may arise—ranging from passive noncompliance to privately altering routes. In such cases, the “optimal solution” may never be followed in practice. Workload balancing enhances fairness and predictability, enabling route optimization to be effectively implemented and deliver sustainable benefits.