In this interview, An de Wispelaere, Chief Product Officer at PTV Logistics, shares her perspective on the real opportunities, advantages, limitations, and risks of applying AI to route optimization. She explains how PTV Logistics combines academic research with its own studies to advance innovation in solving the Vehicle Routing Problem (VRP) and continuously push the boundaries of what AI can deliver in logistics. Together, these insights paint a clear picture of how AI is evolving into practical, trustworthy decision support for the future of logistics.
Q: Will PTV Logistics use AI or reinforcement learning?
“Our fundamental objective is to continuously innovate and maintain a competitive edge. In pursuit of this goal, we are committed to leveraging all available methods and cutting-edge techniques, including AI or reinforcement learning. It should always be superior to other techniques that we use. Following the merger of PTV and Conundra, we are poised to enhance our innovation efforts and further outpace the competition.”
Q: What are the Pros of AI in VRP Context?
“AI can be used extensively in the Learn area. (Learn = using the execution data to gain insights on and improve the quality of our route optimization plan, by enriching the master data used to create the plan). For example, AI and Machine learning is used to analyze customer delivery times which might be time-dependent and even driver/resource/location related.
Geocoding: AI is a powerful tool for improving the accuracy and efficiency of geocoding, helping to enable more accurate and effective mapping and location-based services. Examples are:
- Address Parsing: AI can be used to parse the individual components of an address, such as the street name, city, state, and zip code. This parsing can help improve the accuracy of the geocoding process by ensuring that each component is correctly identified and matched to its corresponding geographic location.
- Natural Language Processing: AI can also be used to interpret natural language inputs, such as handwritten or spoken addresses. Natural language processing (NLP) can help identify and correct errors or ambiguities in the address, such as misspellings, missing or incorrect components, or unclear abbreviations.
Service Levels – Assessing service levels based on customer proximity to the depot is important. Delivery cost is obviously related to distance, but density in the delivery area also plays a crucial role. Accurately forecasting the entire delivery network of a particular day is necessary to make on-the-fly estimations or predictions of specific delivery costs for a customer and timeslot. AI techniques have a proven track record in forecasting and are highly relevant in this context.
Planning robustness: Road transport is prone to real-time changes, such as traffic congestion or sudden changes in demand. Machine Learning algorithms can learn from past data and predict future trends, which can help in adjusting the routes and schedules accordingly creating a more robust plan.
Resource Allocation: AI can be used to allocate resources such as vehicles and drivers efficiently. Machine learning algorithms can analyze historical data and predict demand, which can help in deciding the number of resources needed for a particular period.
Customer/Driver Satisfaction: AI can be used to improve customer/driver satisfaction by optimizing the delivery schedules and reducing the delivery time. This can be achieved by analyzing customer/driver behavior and preferences and incorporating them into the VRP algorithm.”
Q: What are the limits and risks of AI in VRP context?
“As businesses turn to artificial intelligence (AI) to optimize vehicle routing problems (VRP), it is important to carefully consider the limits and risks associated with AI implementation. We explore these factors and provide insights to help businesses navigate the potential drawbacks of using AI in VRP.
Limits:
- Data size needed: to learn valuable insights, enormous amounts of historical data is needed.
- Computational Resources: AI requires significant computational resources to perform its calculations, which can become a bottleneck for large VRP instances.
- Data quality: The quality of the data fed into AI models is crucial to their effectiveness. If the data is incomplete, incorrect, or inconsistent, it can lead to inaccurate solutions.
- Complexity of the problem: VRP can be a very complex optimization problem, especially when considering real-world constraints like traffic, weather, and vehicle capacity. The complexity of the problem can limit the effectiveness of AI models in solving it. We see AI approaches yielding satisfying results for simpler cases, these are promising for future evolutions.
- Trade-offs between solution quality and computation time: AI algorithms need to balance solution quality with computation time, especially in real-time applications. Sometimes, the AI algorithm may not be able to find the optimal solution within a reasonable time frame.
Risks:
- Overreliance on AI: An overreliance on AI to solve complex optimization problems like VRP can lead to complacency and reduced innovation in traditional problem-solving methods.
- Black Box Problem: AI models can be difficult to interpret, which makes it challenging to understand why certain routes are recommended. This lack of transparency can make it difficult to identify and correct errors or biases in the algorithm.
- Overreliance on Historical Data: AI models are typically trained on historical data, which can be problematic when conditions change.
Overall, it is important to carefully consider these risks and limits when implementing AI in route optimization to ensure that the benefits outweigh the potential drawbacks.”
AI only works when teams can actually use it.
The real challenge isn’t having powerful algorithms — it’s turning data, constraints, and daily disruptions into clear, confident decisions. PTV Mira bridges this gap by transforming everyday logistics questions into real optimization scenarios with transparent, explainable results, helping teams act faster and with greater confidence.
Q: How does PTV Logistics Addresses Tasks that Competitors Claim to Solve with AI?
“With AI constantly changing the landscape of the business world, a few of our competitors declare to have achieved mastery in incorporating AI into their solutions. We examine how our company addresses this challenge and endeavors to attain superior results.
- Take for example the statement that one should learn from the manually applied changes to the planned routes by the dispatcher, considering “not everything is known in master data and never will be”.
- In this particular case, we can utilize AI to automatically improve, enhance, or correct the master data, instead of using AI to manipulate the VRP solution. There are two primary reasons behind this approach.
Firstly, our VRP algorithm is specifically designed to generate the best possible solution based on a dataset that is assumed to be complete and accurate. Therefore, tweaking or manipulating the solution may not necessarily lead to improvement since the algorithm already assumes the data is reliable.
Secondly, the “black box” argument emphasizes the importance of transparency in the decision-making process. By understanding why certain routes are chosen, based on an underlying cost model, planners can make informed decisions. Modifying the solution could result in a more expensive plan, providing valuable insights into the algorithm’s decision-making rationale.”
Q: What sets our approach apart, and how does it surpass others?
“We continuously enhance our VRP-solving algorithm through academic research and our own studies. We aim to provide cutting-edge outcomes that exceed industry standards, which we achieve by subjecting our modifications to rigorous benchmarks and comparing them with our competitors. We are proud to say that our algorithm currently delivers exceptional results, and we will continue to innovate and use the right mix of algorithms. Thus, if our algorithm yields an unexpected result, we are inclined to believe that the issue lies with the input data rather than the output. AI could be utilized as a learning tool to study the adjustments made by humans in the past and use that knowledge to ask better questions about the algorithm in the future. This could include improving the input data by suggesting changes or identifying gaps in the algorithm’s capabilities.”
As AI becomes more accessible and practical in everyday logistics work, many teams also ask how these principles translate into real tools. This leads to a natural question:
Q: How does PTV Logistics’ latest innovation, PTV Mira, help logistics teams put AI into practice for route optimization?
“PTV Mira is our interactive logistics intelligence agent that turns complex routing and network questions into clear answers through simple natural‑language conversation. Instead of relying on dashboards, spreadsheets, or manual analysis, planners and decision-makers can ask PTV Mira questions such as:
“What happens if volume increases next Monday?”
“Why is this order unplanned?”, or
“Which depot location performs best and why?”
She translates these questions into optimization scenarios, runs real simulations via PTV Developer APIs, and compares alternatives across cost, service, and CO₂ emissions.
Built on PTV Logistics’ routing and optimization engines — strengthened by more than 40 years of domain expertise — PTV Mira does not just retrieve data. She executes real optimizations, generates scenario comparisons, explains outcomes transparently, and highlights trade-offs. This makes AI practical and trustworthy in day-to-day route planning and strategic decision-making.
PTV Mira is designed for enterprise reality: no autonomous decisions, full transparency of assumptions, and user approval for every action. She reduces manual analysis, accelerates decision-making from hours to minutes, and empowers teams to understand root causes, explore scenarios, and improve both operational and strategic KPIs.”