Intralogistics: Efficient Material Flows

In the field of intralogistics, we focus on two key areas:

• Simulation-based analysis, planning, and optimization of material flow and intralogistics systems
• The use of artificial intelligence, particularly machine learning, to optimize logistics processes

By combining simulation and AI, new control concepts can be explored and strategies tested before they are implemented in real-world facilities.

In the field of simulation for research and industrial projects as well as in teaching, the IFT uses the simulation software Plant Simulation and AnyLogic.

In the field of material flow system simulation, we focus on the simulation-based planning, analysis, and optimization of intralogistics systems.

The following simulation programs are used:

• Plant Simulation:
  • Wide range of predefined building blocks
  • Intuitive modeling capabilities
  • Simple, easy-to-understand programming language (SimTalk)
  • 3D visualization for realistic representations
    ⇒ Easy visualization of common intralogistics processes
• AnyLogic
  • Support for various modeling paradigms (multi-method modeling)
  • Combination of event-driven, agent-based, and system dynamics modeling
  • Java-based and therefore highly flexible when interfacing with hardware or other software systems
    ⇒ Analysis and modeling of complex processes, e.g., in the field of automated guided vehicle systems

The different strengths of the two programs allow users to select the most appropriate simulation program for their specific application.

In the HaProLoK project, for example, the Plant Simulation software was used to analyze various control variables and response variables of the production logistics system. These variables influence both internal processes and external factors, and thus significantly impact the performance of a manufacturing company.

As in many areas of research, the question of the appropriate use of artificial intelligence (AI) algorithms has also become a focal point in simulation. The combination of simulation and AI makes sense because simulation models can generate data that the algorithms use to learn.

Are you interested in a simulation-based analysis of your material flow process?

In addition to opportunities for research collaborations, we offer services in the following areas:

• Market overview of material flow simulators
• Simulation studies
• Workshop on material flow simulation

Our staff has several years of experience in developing simulation models. If you are interested, please contact Ms. Aya Ounissi to schedule a consultative meeting.

The Institute for Mechanical Handling and Logistics investigates how modern artificial intelligence methods can be applied in logistics research. A particular focus is on deep reinforcement learning (DRL). The research explores ways in which this methodology can improve the control of material flow.

The work is based on the following approaches:

• Simulation (e.g., in Plant Simulation, AnyLogic, Python) as a data source for training RL algorithms and for developing policies or control strategies.
• Testing strategies in virtual models before adapting real systems.

This allows, for example, the evaluation of how different control variables or actuators behave in warehouse logistics or material flow before they are tested in a real facility.

As part of the ALPHA project, an autonomous block storage system for pallets is currently being developed. Using DRL-based storage strategies, the aims are not only to make operations more efficient, but also to reduce the carbon footprint and optimize energy consumption. 

In doing so, the IFT demonstrates how reinforcement learning and simulation can work together to operate complex logistics systems in a flexible, resource-efficient, and effective manner. New control concepts are also being researched that would be difficult to implement using traditional methods alone.

Teaching

We offer two courses for students as part of the “Planung und Simulation in der Logistik” module: “Simulation und Visualisierung in der Intralogistik” and “Planung logistischer Systeme,” which cover the fundamentals of these respective fields. Student research projects are regularly assigned across all subject areas. If you are interested, please contact Ms. Aya Ounissi.