Optimizing and automating route planning

Within the Digital Factory, CAPE Groep offers a challenging and inspiring environment for scientific research into digital transformation and innovation. We translate strategic issues into agile solutions for various organizations. CAPE Groep works together with universities and knowledge institutions, such as the University of Twente, TKI Dinalog and NWO, continuously on innovative projects. In this way we bring science and business together and arrive at solutions that make a difference. Topics in which we offer assignments are within the areas of digital transformation, CI/CD, blockchain, BI and big data, machine learning, IoT and innovation.

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Research: 'Optimizing and automating route planning through optimization and simulation'
Industrial Engineering & Management, University of Groningen

In April 2021 I was given the opportunity to complete my study Industrial Engineering and Management at the University of Groningen through a graduation research project at CAPE Groep. What appealed to me at CAPE is the open culture and the opportunity to develop yourself on both a personal and technical level. At CAPE you are immediately included in the company and everyone is always ready to help you when needed. Here is a brief summary of my thesis.


In the logistics sector, an efficient and timely service is essential to be able to compete in the international market. Logistics companies are therefore constantly looking for new ways to optimize their transport process and thereby reduce the total transport costs. One way to reduce overall transportation costs is to optimize route planning. An optimized route planning can ensure:

  • Reduction of the total kilometers driven;
  • reduction of empty kilometers, and
  • better utilization of the available trucks.

In my thesis I investigated how technologies such as optimization and simulation can contribute to the optimization and automation of route planning.


Making route plans is a very complex process due to the many aspects that must be taken into account. Examples of this are:

  • The products must be delivered within a certain agreed time (window times);
  • There is only a limited availability of the trucks;
  • The planners ensure that the Driving Hours Act is not exceeded; that means that the drivers are only available to a limited extent;
  • The amount of orders varies greatly from day to day, which means that the planners are faced with a new challenge every day.

The literature often mentions simplified solutions where it is not clear how they will perform in the “real world”.


In my research I built a simulation-based optimization (SBO) framework to minimize the outbound journeys from one of the depots of a large transport company. The SBO consists of an optimizer made in python and a discrete event simulator (DES) made in Anylogic. The optimizer contains a combination of algorithms that can calculate the optimal route planning in a short time. The DES is used to mimic the real world so that the routes suggested by the optimizer are validated. To mimic the real world, the DES contains multiple stochastic variables such as travel and service times. Furthermore, the simulation model measures several KPIs so that the outcome is validated on several levels.


The SBO has been tested and validated on the outbound logistics of Farm Trans, a full-service logistics service provider specialized in conditioned transport based in the Benelux. The result is a significant improvement in several logistics KPIs, such as a reduction in the kilometers driven and the number of outbound trips. This improvement in the KPIs is reason enough to further develop this prototype and thus strive for cost savings.