Real-time data-driven maintenance logistics
Dankzij Internet-of-Things zijn voor onderhoudslogistiek veel verschillende, real-time gegevens beschikbaar over assets aan de ene kant, en de reserve-onderdelen en monteurs die deze machines repareren aan de andere kant.
Deze real-time gegevens bieden de mogelijkheid om de instandhouding van assets veel kostenefficiënter te organiseren. Hier moeten bedrijven de transitie maken van een organisatie waar onderhoudslogistieke processen statisch en tijdgedreven zijn, naar één waar de onderhouds-logistieke processen dynamisch en datagedreven zijn. Dit project ontwikkelt aanpakken om deze transitie mogelijk te maken.
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Wetenschappelijke publicaties
Black-box Mixed-Variable Optimisation Using a Surrogate Model that Satisfies Integer Constraints
A challenging problem in both engineering and computer science is that of minimising a function for which we have no mathematical formulation available, that is expensive to evaluate, and that contains continuous and integer variables, for example in automatic algorithm configuration.
Black-box combinatorial optimization using models with integer-valued minima
When a black-box optimization objective can only be evaluated with costly or noisy measurements, most standard optimization algorithms are unsuited to find the optimal solution. Specialized algorithms that deal with exactly this situation make use of surrogate models.
Continuous Surrogate-Based Optimization Algorithms Are Well-Suited for Expensive Discrete Problems
One method to solve expensive black-box optimization problems is to use a surrogate model that approximates the objective based on previous observed evaluations. The surrogate, which is cheaper to evaluate, is optimized instead to find an approximate solution to the original problem.
The Robust Malware Detection Challenge and Greedy Random Accelerated Multi-Bit Search
Training classifiers that are robust against adversarially modified examples is becoming increasingly important in practice. In the field of malware detection, adversaries modify malicious binary files to seem benign while preserving their malicious behavior. We report on the results of a recently held robust malware detection challenge.
Attention and long short-term memory network for remaining useful lifetime predictions of turbofan engine degradation
In Prognostics and Health Management (PHM) su cient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven prediction methods assume that training (source) and testing (target) condition monitoring data have similar distributions.
Data-Driven Policy on Feasibility Determination for the Train Shunting Problem
Parking, matching, scheduling, and routing are common problems in train maintenance. In particular, train units are commonly maintained and cleaned at dedicated shunting yards. The planning problem that results from such situations is referred to as the Train Unit Shunting Problem (TUSP). This problem involves matching arriving train units to service tasks and determining the schedule for departing trains.
Remaining useful lifetime prediction via deep domain adaptation
In Prognostics and Health Management (PHM) sufficient prior observed degradation data is usually critical for Remaining Useful Lifetime (RUL) prediction. Most previous data-driven methods assume that training (source) and testing (target) condition monitoring data have similar distributions.
Learning 2-opt Heuristics for the Traveling Salesman Problem via Deep Reinforcement Learning
Recent works using deep learning to solve the Traveling Salesman Problem (TSP) have focused on learning construction heuristics. Such approaches find TSP solutions of good quality but require additional procedures such as beam search and sampling to improve solutions and achieve state-of-the-art performance.
Decisions for information or information for decisions? Optimizing information gathering in decision-intensive processes
Decision-intensive business processes are performed by decision makers who gather different pieces of information to reach the process objective: a final decision of high quality, for instance, the final price of a quote or the diagnosis of a failure of a hightech machine, as a result of an information-gathering process with minimum costs and efforts.
Decision support for declarative artifact-centric process models
Data-driven business processes involve knowledge workers that process information to take decisions. Such processes have been modelled successfully using artifact-centric process models. Artifacts represent business entities about which the knowledge workers collect and process information.