The route planning and delivery management software is becoming a cornerstone for any delivery operations. There are a number of solutions on the market – from free routing planning software that will be good for one driver operations to plan a dozen of stops to powerhouses that help to plan hundreds or thousands of deliveries from multiple locations. We are going to discuss what requirements may companies of different sizes and in different verticals have and what they should consider in choosing a route planning software for their operations.
What is route planning and why it is hard?
There is a misconception that route planning software helps find the best route between 2 or more locations on the map. People usually imagine google navigation or other navigation software that helps you to decide which route to take. How strange may it sound, the route planning for last-mile operations generally has little to do with maps or navigation. It is rather a complex process of deciding the number of vehicles needed to implement the job, the number of orders in each vehicle, and the sequence of stops so that total mileage or driving time will be the minimum. This is becoming more complex when stops have tight delivery windows and variables service times. Juggling between fitting everything into time constraints, keeping costs down and drivers happy seems an impossible task.
Now, lets see what is a route planning and how does that work.
The most widespread way of modeling multi-stop delivery operations is by solving the so-called Vehicle Routing Problem (VRP’s). In the majority of cases, the VRP goal is to optimize driven mileage, which, in formal terms, means getting the minimum possible mileage for the given plan. Combinatorial optimization is the mathematical apparatus to solve VRPs. There are two major ways to solve this problem – using exact methods or metaheuristics (approximate solutions). The discussion of combinatorial optimization is a topic for another post, so we won’t go deep into it now. What we can say is that the exact methods are limited to 100 nodes (deliveries), hence if one wants to get a plan within a reasonable time the use of some kind of metaheuristic is absolutely necessary. A consequence of this process is that no solution can get an “optimal” result but rather should try to get as close to optimum as possible. For example, if the theoretical minimum for 1000 deliveries is 10000 miles with 40 trucks, different algorithms will certainly get more miles than that (could be both 11000 and 14000). In fact, as no one knows what the theoretical optimum is, the planner can’t realistically assess the quality of the solution through a comparative process. In our experience, manual planners get somewhere between 60-80% to optimal. So, what is the best way to assess how good the solution is? We must compare the firm’s historical data (if that exists) or run it in parallel with manual planning to see the difference in optimization rates. As an industry-standard, good routing solutions should give results close to 95-97% to optimal.
The difference between paid and free route planning software as well as between different paid solutions will be sometime substantial differences both in (a) optimization quality and (b) application usability. Some of the distinctive characteristics of a “good” application are described below. Companies may want to look for route planning software based on the importance of the criteria described below.
The main problem of VRP is time complexity or, in other words, the fact that the amount of time needed to solve the problem grows exponentially when the number of deliveries or nodes increases or when the algorithm incorporates more constraints. To give you an idea of the problem encountered when scaling up nodes/deliveries consider that solving a Capacitated Vehicle Routing Problem with Time Windows (CVRPTW) for 100 locations takes about 10 minutes using current commercial optimization solvers but takes a whopping 8 hours for 300 locations. Why, because solving for optimization goals for the first set of nodes takes tens of millions of permutations and for the latter – hundreds of millions.
Below are a few more examples of variables dramatically adding to the numbers of permutations and thus increasing the time for a potential optimal solution.
Time complexity rises when an optimization algorithm tries to incorporate more real-life data whose absence would massively degrade the usability of the solution. A primary example is that many firms have a variety of trucks and equipment. An average firm can use 4-8 types of trucks with different capacities. The majority of free and even paid solutions either don’t include the capacity constraint in calculations or are using just one capacity constraint.
Delivery windows are another variable that adds complexity to the problem. The planner should make sure that capacity is maximally leveraged while ensuring ETA’s are within delivery windows. While the majority of paid solutions will factor in delivery windows they are absent from free route planning software.
Service requirements are different for different clients. Dwell or loading/unloading time can vary for each of these 400 deliveries and the optimization algorithm should be able to incorporate it into calculations. We didn’t see a free route planning software that includes variable service time.
There are orders which should be prioritized over the others. Thus, the optimization algorithm should be able to keep strict delivery requirements for the high-priority deliveries while still assuring the best optimization goals.
Electronic Logging Device (ELD)
ELD’s are mandatory nowadays and Hours of Service (HOS) requirements should be considered from the planning phase. This should go as a constraint variable into the algorithms as well. Needless to say that the majority of free route planning software or free versions of paid route planning software don’t have this feature.
Along with main constraints they could be other requirements that are hardly found in the majority of free and some of paid route planning software.
SKU level data
An average distributor operates with 1000’s SKUs and a large distributor with 100’s of thousands. Each SKU has different volume and weight characteristics which should be taken into account when planning deliveries. Not all applications incorporate product-specific accurate data into calculations or use capacity as a constraint at all. This results in a lot of capacity violations and too many routes becoming obsolete when it comes to filling and dispatch. There is also a highly variable human factor in play because many companies don’t have all the needed information in their Warehouse Management Systems (WMS). As a result, algorithms should be able to work with incomplete data. Needless to say, the software should be able to seamlessly integrate with a firm’s order and warehouse management systems or other Enterprise Resource Planning (ERP) modules.
As the routing problem is complex and requires lots of data and computational resources most of the routing applications available on the market should be installed on-premise. Conversely, it is becoming imperative that people who are not in the field (and even people who are in the field) should have access to multi-user plug and work opportunities. This creates a high-demand situation requiring a novel solution. The routing solution should offer robust multi-user functionality. At the same time, it should be able to process billions of combinatorial permutations in seconds. An advanced enterprise-grade cloud-native architecture is needed, otherwise, the firm will be creating additional problems rather than solving old ones.
Imagine what happens when there are not one but 10’s of planners in the firm who plan data for multiple warehouses and distribution centers. An optimization solution requires multiple installations in different warehouses resulting in massive investments and further support cost. Even without that, the majority of solution applications in the market are limited to planning 500 simultaneous deliveries and are only suitable for small firms. One can plan routes in batches but that will substantially degrade the quality of optimization for the total plan. Growing and competitive firms should have the opportunity to run and compare multiple instances simultaneously, plan tens of thousands of deliveries, and have other collaborative functionality which is only possible with the enterprise-grade cloud-native architecture.
There are a number of additional features that should be embedded in any forward-looking solution such as a) data interoperability b) creation of dispatch documentation c) monitoring dashboard e) functional analytics f) dynamic planning, etc. We will return to these topics in other blog posts.
Current Solutions in the Market
There are different solutions in the market which help to plan loads and dispatch them. We will list some of them and group them following the descriptions copied from their websites.
Mapping applications offer route optimization, which is limited to getting the best sequence for the given data points. The number of points is limited to 10-20 points. Apparently, maps don’t do VRP optimization for multi-stop deliveries. Maps won’t provide any product and shipment management functional either. Routing functionality would be a good addition for personal or occasional usage.
Free routing software
Free applications are one step ahead of current maps routing functions. They can be used by small firms, but it is not clear if these applications use any kind of mathematical optimization. They apparently miss the majority of the ideal functionality previously touched on.
Paid routing software
All paid solutions state that they have some optimization algorithms under the hood. Routific and OptimoRoute have straightforward pricing while Route4Me has a relatively low base price but additionally charges for different features. Workwave has several products for different industries. All these solutions incorporate delivery windows in the planning and build multi-stop loads.
Other consideration in choosing a route planning software
First, one should look deeper into what has been optimized and how good it actually is? As we have noted above, results can be different (sometimes substantially) depending on the types of algorithms and optimization methodology used. The best way to compare is either to test the same dataset using different applications or compare results with the optimal values for that dataset.
Second, does software optimize the delivery plan for the day or just updates sequences on fixed routes? Running CVRPTW is hard and requires substantial resources and appropriate algorithms, so many applications just help you to build a route plan once, without taking into consideration specific order details and location time windows. You would assign orders manually afterward by figuring out sequences. Later is a much simpler approach but it substantially reduces optimality. I.e., Less® Platform runs CVRPTW for every group of orders from the scratch, thus assuring the most optimal route plan for that specific order list.
Third, data requirements for foodservice wholesale distributors and cleaning services, for example, can be substantially different. It is not the same to run the Capacitated VRP optimization for the several thousand different SKUs and simple stops routing. Wholesale distribution, trucking, and logistics management companies need a comprehensive product and freight management function. These industries also combine inbound and outbound operations, can have simultaneous pick-ups and deliveries, and offer multi-depot operations.
Forth, the capacity of the platform for handling the number of deliveries should be appropriate to the size of the company. Due to the time complexity of the problem, larger companies should pick an application (more than 300-500 deliveries a day), which is capable to work with large data sets without compromising the solution quality.
Transportation, distribution, and logistics management firms operate in a highly competitive and increasingly uncertain environment where margins are tiny and even a small disadvantage will keep the firm out of the highest levels of competition. Delivery route planning optimization sometimes lags behind other, seemingly more important operations when it comes to decision making. Crucially, this is where your short-term profitability and long-term efficiency rests. The cost of choosing a poor solution will be hundreds and millions of dollars. We have seen many cases where suboptimal planning and poor execution (which are highly interconnected) sink both growing and established firms.
Less® Platform can turn your data into loads within 10 minutes and compare it with your historical results! You can also test all the necessary features for your distribution model and consult with our experts on your challenges.
Vardan Markosyan is the CEO at Less® Platform
MBA from the University of Chicago Booth School of Business
PhD in Economics from the Institute of Economy of NAS RA
He spent decades of research and consultancy on business process optimization and system design