SLF Thesis Award for preventive maintenance concept development for new systems via multi-objective optimization
Joep Hoedemakers has won the SLF Thesis Award
At the Service Logistics Summit 2020 on November 19th, J.A.J. (Joep) Hoedemakers won the SLF Thesis Award for his work on preventive maintenance concepts development for new high-tech systems in the agricultural sector via multi-objective optimization.
The project has been carried out at Lely, a developer and producer of high-tech equipment for the dairy cattle industry. Maintenance is also carried out by Lely Centers. Farming is a continuously operating business in which farmers rely on their systems for day-to-day operations. Unavailability of these systems and unscheduled downs negatively affect both the production process and animals’ health. At Lely, maintenance is currently based on experience and gut feeling of engineers. There is no systematic and sound approach to support maintenance decisions via explicitly defined rules. To increase the availability of their equipment, moving from a reactive to a preventive and eventually predictive approach to maintenance is paramount. However, it is challenging to develop optimal preventive maintenance concepts for new systems due to the large amount of uncertainty in the expected failure behavior of the new systems for which on field data are available. In his thesis, Joep developed a generally applicable multi-component maintenance model which adopts an iterative procedure to iteratively optimize intervention thresholds for individual components and visits schedule for the system. Decisions are optimized based on multiple criteria: minimization of maintenance costs and system downtime. Implementation to a case study shows that considerable cost and downtime saving can be achieved if the developed framework is implemented.
The jury was impressed especially by the high practical relevance of the work and therefore its potential impact in the field, by the general applicability of the method, as well as the quality of the work based on a thorough and solid use of existing models and techniques.