How planners and logistics leaders plan many depots, many vehicle types, and many rules as one network.
Most days, the plan gets built, and it is usually a workable one. It is rarely the lowest cost plan, because nobody can compare every way of splitting orders across several depots, vehicle types, and delivery windows by hand. Every extra depot, rule, or vehicle type multiplies the options.
The right route optimization software for complex logistics networks closes that gap. This guide explains what makes a network complex, how optimization handles it, and what to look for when you choose software.
Size is only part of it. A network with ten vehicles can be complex, and one with a thousand identical vehicles leaving a single depot can be simple. Complexity grows with the number of things that have to be decided together. It usually shows up in one or more of these ways:
If three or more of these sound like your operation, a basic route planner is likely to leave money on the table.
Also related: Transportation planning software for freight and delivery networks
A basic route planner treats each vehicle as its own puzzle. It orders the stops well enough. What it cannot see is how a depot choice, a vehicle choice, and a time window affect each other across the whole network. Those interactions are where the cost hides: extra kilometers, half empty vehicles, and customers served from the wrong depot.
Cost pressure makes that hidden cost harder to ignore. The American Transportation Research Institute reports that the average cost to operate a truck in the US reached $2.336 per mile in 2025, up 3.4 percent on the year and the highest per mile cost in the history of its report (ATRI, 2026 Analysis of the Operational Costs of Trucking, July 15, 2026). At that level, a few avoidable kilometers on every route add up quickly.
Three signs your network has outgrown manual planning:
Tell us about your depots, fleet, and rules. We will show you what network wide optimization looks like on your own data.
The method is the same whether you run four depots or forty. What changes is how many rules the software has to hold at once.
Complex networks need answers at three levels, and a decision at one level changes the next.
Most vendor pages say they handle multiple depots and many constraints. Few explain how. These are the questions that separate a tool that copes from one that fits.
Complex networks need answers at every level, from where depots should sit to which vehicle takes which stops today. PTV Logistics brings strategic, tactical, and operational planning together in one optimization solution. You can work with it as an application, as a conversational assistant, or through APIs, depending on how your teams plan.
With PTV OptiFlow, the planner loads the orders and available vehicles, chooses the cost drivers that matter, whether time, distance, capacity, or workload, and runs the optimization. The plan reflects depots, vehicle types, and the rules that limit what is feasible, and time dependent traffic models keep the routes realistic on the road. PTV OptiFlow is available as a SaaS application, through PTV Mira, or as an API engine through PTV Developer.
PTV Mira is a logistics intelligence agent you can talk to. She turns planning and analysis questions into real optimizations, compares scenario alternatives, and explains the results, so a question such as what happens to cost and service if a depot closes gets an answer in minutes instead of hours or days. PTV Mira supports your decisions, and you approve every action. She is built on more than 40 years of PTV Logistics routing and optimization expertise.
PTV Developer is a modular API platform with 17 individual APIs across six areas: route optimization, truck routing, geocoding, maps, map matching, and supporting services. You can mix and match them, so optimization can sit inside the TMS, ERP, or WMS you already run. It scales from 10 to more than 10,000 daily routes and is SAP TM ready.
ID Logistics
ID Logistics has centralized its planning. Two planners are now sufficient to plan more than 400 routes with 5,000 to 6,000 stops every evening.
Albert Heijn
Albert Heijn’s extensive and complex distribution network consists of 1,100 Albert Heijn stores, 600 Gall & Gall shops, 530 Etos locations and 7 home shop centers.
Rosedor Penja
With the help of PTV OptiFlow, Rosedor Penja has transformed its logistics operations into a strategic growth driver. The cooperative based in Dordogne now optimizes delivery routes, refines its depot network, and supports commercial expansion—all while reducing operating costs.
Share your depots, fleet, and delivery rules, and we will show you what a single plan looks like on your data.
Route optimization software for complex logistics networks plans routes, stops, and vehicles across many depots, vehicle types, customers, and rules at the same time, so the whole network runs at the lowest total cost instead of each route being planned on its own. In plain words, it decides which depot serves which customer, which vehicle takes which stops, and in what order, while respecting every rule you set.
The vehicle routing problem is the challenge of finding the best set of routes for a fleet of vehicles serving a set of customers. Route optimization software solves it automatically. Real networks add rules such as capacities, time windows, and driver hours, which is why they are called complex or rich routing problems.
Read more about it here: Leveraging AI for Route Optimization: Pros, Limits, and Risks
A multi depot vehicle routing problem is a routing problem where vehicles start from more than one depot and the software must decide which depot serves which customer as well as the route order. Treating depot choice as part of the optimization usually works better than assigning customers to depots first and routing afterwards.
Consider it when you operate from two or more depots but still plan as if you had one. Common signs are planners manually reassigning customers between depots to fix daily capacity, spreadsheets sitting alongside your TMS, cost per stop rising despite healthy volume, time windows that differ sharply by region, or new depots and depot closures on the horizon. Model the options with your real orders before you decide, because the right answer depends on where your demand is and how it changes through the week.
Read more about this in detail here: Single depot vs multi depot planning: Challenges, tradeoffs and solutions
The depots that give the lowest total cost across the whole network, not simply the closest ones. The answer depends on distance, travel time at the time of day, depot capacity, vehicle availability, and service windows. An optimizer weighs those together, so a customer closest to one depot may still be better served from another.
They plan the depots, vehicles, and stops as one problem and let software compare the options. In practice that means loading all orders and vehicles together, defining what cost means for the business, and letting the optimizer assign customers to depots and sequence the routes in one run. Planners then review the result and handle exceptions.
Treat each time window as a constraint the plan must respect, then optimize cost around it. The software checks whether a vehicle can reach each stop inside its window using realistic travel times, so a route that looks fast at 3am is not counted as feasible in 8am traffic. Narrow windows reduce your options, which is why they are worth planning across the whole network and not one route at a time. PTV OptiFlow works this way: it weighs distance, time windows, and available capacity together and uses time dependent traffic models, so the plan holds up on the road.
PTV Mira is a logistics intelligence agent that turns planning and analysis questions into real optimizations, compares scenario alternatives, and explains the results. You can ask questions such as what happens to cost and service if a depot closes. She supports decisions and does not make them, so users approve every action.
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