Faculté / Département ⤶ |
Répertoire par Unités | Répertoire par Projets | Répertoire des Chercheurs |
Répertoire par Collaborations | Classement par Domaines | Classement par Frascati | Disciplines CREF |
(BEWARE RW)
Unité : Scheduling Optimisation Security | ULB842
The goal of the PRALINEH project is to design new autonomous planning optimization algorithms that can be applied to very hard and dynamic real-life cases, thus able to make optimal decisions in near real-time. By optimal we mean decisions that can improve significantly operational KPIs such as lead-times, on-time-delivery, inventory, equipment efficiency, throughput and last but not least use of resources whether these are consumables, energy, waste, … thus contributing to higher durability and lower footprint. The economic impact of these optimizations can be easily as lower costs and higher revenues. It is common knowledge that legacy planning & scheduling solutions do not provide the flexibility, agility and optimality required as they are often just simulations or interactive charts with very basic logic. To build the system that we want, we need new parallel metaheuristics and real-time optimization tools based upon machine learning methods. The originality of the project is to combine the following four elements: parallel execution (for higher performance and lower response latency), real-time constraints (to satisfy use case limits), various metaheuristics (as each problem poses new challenges) and machine learning (in order to both fine tune the heuristics and to adapt to uncertain or low-quality data) Parallel execution: in order to speed up the execution of the heuristics upon modern, i.e., multicore, architectures the heuristics must be parallelized (multithreading). This poses various synchronization challenges. Metaheuristics: we assume the genericity of the target applications: making relatively few assumptions about the optimization problems. Thus, we need to use a large toolset of metaheuristics and let the system discover which one is the best fit. This way, the solution may be usable for a variety of problems. Real-time constraints: while the project targets generic industry and logistics scheduling problems we target real-time applications where the time to decide when and at which each scheduled activity completes is important, typically we have to meet a deadline or at least minimize the latency. Machine learning, we aim to use those techniques at two different levels: o Tuning the metaheuristic’s parameters (model and solver), e.g., the degree of parallelism (number of threads). This offline learning stage would be based on Monte Carlo experiments and self-generated sample data. o Re-tuning
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