Industrial Systems Engineering


We focus on the development of methods, techniques and tools to support the design and operational management of production systems. The activities concentrate around a number of research topics:

  • Mathematical modeling and optimization of industrial systems
    Development of multi-criteria evaluation and optimization methods to support the design, planning and control of manufacturing systems. The main focus lies on stochastic modelling approaches to obtain robust solutions, taking into account the inherent uncertainty and variability of today’s assembly environments.
  • Validation of flexible automation concepts and operator support systems
    Experimental validation of flexible automation and operator support systems. We focus on the development of standardized models for digitized assembly information based on industrial standards to allow for the (automated) generation of personalized and contextualized work instructions and other support tools.
  • Virtual models to support operational decisions and virtual commissioning
    Development of simulation models to validate design choices and test what-if scenarios. An important research topic is the use of virtual models to perform virtual commissioning of industrial control systems/strategies and automatically generate test scenarios.

Next to this we have activities related to logistics, supply chain management, material handling and warehousing. 



Augmented workers in a manufacturing cell 

operatorinfo.pngThe objective of this project is to develop in-depth understanding of methodologies and tools that allow an effective transition from human to human-robot collaborative (HRC) assembly. Our tasks relate to the task distribution and workplace configuratoin. To this end an ontology based on the ISA95 standard has been developed. This ontology allows to collect and provide context aware information to the worker in a manufacturing cell. E.g. information captured from an experienced worker will be stored automatically in this ontology as task instructions. These task instructions will then be provided to the worker, the level of detail will depend on the context e.g. the skill level, the experience, the lot size, .... 

SEM model - State Enabled Manufacturing model

State Enabled Manufacturing Model

To meet today’s mass customization needs, companies try to efficiently adapt to changes production capacity and processing functions by introducing reconfigurable equipment. Classical discrete manufacturing systems are often setup as a rigid production system. However, especially for assembly systems, the need for reconfigurations and rebalancing due to product volumes, mix and design changes is rising. Reconfigurable assembly systems can lower the cost of these actions by having intelligent control capabilities fully exploiting the reconfiguration potential. Nevertheless, reconfigurability imposes challenges on managing and scheduling change-over tasks. Therefore it is important to build flexible manufacturing operations management systems on a uniform model representing the enormous diversity in configurations of personnel, equipment and material used. Furthermore, a common representation of resource structures can improve the processing of automated systems that make use of the information. We have build such a State Enabled Manufacturing model, as described here

Learning-forgetting models for cycle time predictions of manual assembly tasks Learning and Forgetting

Industry 4.0 provides a tremendous potential of data from the work floor. For manufacturing companies, these data can be very useful in order to support assembly operators as well as to attribute tasks across the work force. We have adapted existing models to describe the learning and forgetting effect after a break of manual assembly operators to allow for real time prediction purpose. Mores specifically we have defined a model that accounts for real time registered cycle times of an operator. Further, a new mathematical model for learning and forgetting has been developed proposed to predict the future cycle time of an operator depending on the product mix of his actual assembly schedule, whereby this model accounts for job similarity between the task that is being learned and is being forgotten. In our experimental study, results confirm that a higher job similarity results in a lower forgetting effect for the main task.

Condition Based Maintenance and Production OptimisationIntegrated Production and Maintenance Planning

Today production planning and maintenance planning are still two different worlds. We have developed a joint optimization algorithm of both the lot-sizing and condition based maintenance for a stochastically deteriorating production system. In addition, the influence of the lot-size quantity on the evolution of the equipment degradation is considered. To optimally integrate production and maintenance, a stochastic dynamic programming model is developed that optimizes the total expected production and maintenance cost including production setup cost, inventory holding cost, lost sales cost, preventive maintenance cost and corrective maintenance cost. The algorithm is run on a set of instances and the results show that the joint optimization model provides considerable cost savings compared to the separate optimization of lot-sizing and condition based maintenance. 

Reconfigurable Assembly Line - PlanningPlanning of multi-product assembly lines with reconfigurable cells

Reconfigurable Assembly Lines (RAL) will provide the flexibility to decrease lot sizes while assembling in a cost-effective manner. However, reconfiguration of the line over time to adapt to possible product functionality and demand changes should be done at minimum reconfiguration, operational and material handling costs while ensuring the demand is met within each period. We considered an assembly line considered consisting of hexagonal cells. These have multiple slots where processing modules can be inserted to perform certain operations. In addition, each cell has a single central slot where a central module can be inserted for inter-cellular and intra-cellular transportation of parts. An Integer Quadratic Programming (IQP) model has been developed to solve the following problems simultaneously: (i) assigning processing modules and a central module to the cells; (ii) installation of the cells and conveyors between the cells; and (iii) routing products, ensuring that availability of the resources is not exceeded. The IQP model is implemented and solved for an illustrative problem and its extensions using Gurobi.

To Kit or not To Kit (Assembly Line Feeding)

KitOrNot.pngWe developed a decision model to choose between kitting and line stocking at the level of single parts, while taking into account the variable operator walking distances. Different ways of feeding assembly lines, such as kitting and line stocking not only have an impact on in-plant logistics flows but also determine the amount of stock that is available at the line. This, in turn, has an impact on operator walking distances during assembly. We used data from a truck manufacturing company along with artificial data sets. We are currently looking to set up joint research projects to apply the models in the decision process of company cases. 

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Research & test infrastructure

See here our list of infrastructure