itsowl-ReSerW: Ressourceneffiziente Selbstoptimierende Wäscherei

Cost Pressure in industrial laundries

Because of the market situation and the competitive context it is necessary that industrial laundries are working fast and cost effective. Therefore, resources like energy, soap, water etc. will be used efficient. Furthermore, the handling of clothes needs high personal expenditure and the requirements to the hygiene are important. The requirements of hygiene affect the handling of dirty clothes by the operators and the proof of hygiene by, e.g. hospitals. The planning and configuration activities of automation and process technology are executed manually by the operator. The planning of the logistic process as well as the configuration of the machines is realized independent of each other. Thus, those activities offer high potential for optimization.

Resource Optimization

The objectives of this project are the optimization of the resource utilization, automating the handling of clothes, as well as the automation of planning, optimization and configuration activities.
For an optimal utilization of resources, established control and optimization methods will be used. The washing water will be used to determine the actual amount of soap to feedback the used water to the washing process, thus the utilization of water and soap will be reduced.
For the automatic handling of clothes new automation technologies like handling robots will be used, to reduce the handling of dirty clothes by individuals.
Furthermore, the objective of the automatic planning and configuration will be solved using planning and optimization algorithms. Therefore, knowledge about the actual placement of clothes in the laundry is necessary to optimize the planning after incoming orders. To achieve this objective, new modeling and simulation models, as well as a novel concept for the communication between the planning algorithms and the laundry are necessary. This enables a system wide process planning, optimization, controlling and monitoring to detect abnormal behavior in an early state for the optimization.