Challenges in Real-time Optimization of Maintenance Decisions of Distributed Parallel Machines under Sustainable Production Constraints
Abstract
The sustainable production approach should increase product quality while reducing production costs through intelligent maintenance strategies that enable zero-downtimes and low waste in production machines, including low energy loss. Indeed, very often, machines operating with degraded components increase energy consumption and reduce products' quality. Also, the increase in corrective maintenance frequency increases failure hazards related to human factors. To allow machines to reach high availability and effectiveness in sustainable production units, the maintenance actions must consider a minimum of preventive maintenance actions and predictive maintenance when the cost over effectiveness is low. Among many others, this ratio is a useful feature to develop efficient and intelligent maintenance decision support algorithms for distributed parallel machines in sustainable production contexts. This keynote will consider advanced challenges in real-time optimization of distributed parallel machines' maintenance decisions under sustainable production constraints. Specific algorithms and some heuristics will be suggested to face these challenges while taking advantage of new features extracted from machines operation and environment data and data of maintenance activities. In the last step of this keynote, maintenance logistics variables will be considered in the decision support model.