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Monday | Abstracts of presentations by PhD students I


Session A

Scalable Policies for the Dynamic Traveling Multi-Maintainer Problem with Alerts


Session A: St. Jan-zaal

Peter Verleijsdonk -

Monday, 14.30 - 14.55

Supervisors: Stella Kapodistria, Willem van Jaarsveld



Downtime of industrial assets such as wind turbines and medical imaging devices is costly. To avoid such downtime costs, companies seek to initiate maintenance just before failure, which is challenging because: (i) Asset failures are notoriously difficult to predict, even in the presence of real-time monitoring devices which signal degradation; and (ii) Limited resources are available to serve a network of geographically dispersed assets. In this work, we study the dynamic traveling multi-maintainer problem with alerts ($K$-DTMPA) under perfect condition information with the objective to devise scalable solution approaches to maintain large networks with $K$ maintenance engineers. Since such large-scale $K$-DTMPA instances are computationally intractable, we propose an iterative DRL algorithm optimizing long-term discounted maintenance costs that potentially improves upon any heuristic solution. We extend existing heuristics to devise both quality benchmarks for specific instances and suitable initial policies for the DRL algorithm. In our numerical experiments, we show that DRL can solve single maintainer instances up to optimality, regardless of the chosen initial solution. Moreover, DRL can be successfully applied to improve state-of-the-art dispatching heuristics. Experiments with hospital networks containing up to $35$ assets show that the proposed DRL algorithm is scalable and it is cost-efficient to share resources over the network, as opposed to subdividing the network into smaller regions.




Predictive aircraft maintenance: from Remaining Useful Life prognostics to maintenance planning


Session A: St. Jan-zaal

Ingeborg de Pater -

Monday, 14.55 - 15.20

Supervisor: Mihaela Mitici (UU)



The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. In this presentation, I will present a predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance.



A Sampling-Based Gittins Index Approximation


Session A: St. Jan-zaal

Stef Baas -

Monday, 15.20 - 15.45

Supervisors: Richard Boucherie, Jean-Paul Fox, Aleida Braaksma



A sampling-based method is introduced to approximate the Gittins index index for general families of alternative bandit processes. The approximation consists of truncation of the horizon and support for the optimized discounted reward, an optimal stopping value approximation and a stochastic approximation procedure to find the root of the mean of a stochastic function. Finite-time error bounds are given for the three approximations, leading to a procedure to construct a confidence interval for the Gittins index using a finite number of Monte Carlo samples.

Furthermore, results are proven on almost sure convergence and convergence in distribution of the samples generated from the stochastic approximation procedure. In a numerical study, the approximation quality of the proposed method is verified for the Bernoulli and Gaussian bandit with known variance. Finally, the proposed strategy is applied in a non-standard Bayesian bandit setting where each arm consists of a random effects model where it is shown to significantly outperform Thompson sampling and the Bayesian Upper Confidence Bound algorithms. The approximation method can be applied to any family of alternative bandit processes and can hence be easily applied to more elaborate Bayesian experimental setups than those usually considered in the Bayesian multi-armed bandit literature.





Stochastic optimisation of warehouse capacity


Session A: St. Jan-zaal

Jeroen Landman -

Monday, 15.45 - 16.10

Supervisors: -



In order to use warehouse capacity in an optimal way, ensure customers next day delivery, and use warehouses in a cost efficient way we investigate stochastic optimisation approaches to improve existing ways of steering and planning capacity.

In this talk we present results from several projects and share ideas for further investigation.



Session B


A Unified Framework for Symmetry Handling


Session B: Angola-zaal

Jasper van Doornmalen -

Monday, 14.30 - 14.55

Supervisors: Christopher Hojny, Frits Spieksma



Handling symmetries in optimization problems is essential for devising efficient solution methods. In this article, we present a general framework that captures many of the already existing symmetry handling methods (SHMs). While these SHMs are mostly discussed independently from each other, our framework allows to apply different SHMs simultaneously and thus outperforming their individual effect. Moreover, most existing SHMs only apply to binary variables. Our framework allows to easily generalize these methods to general variable types. Numerical experiments confirm that our novel framework is superior to the state-of-the-art SHMs implemented in the solver SCIP.



Exact Methods for Hierarchical Clustering


Session B: Angola-zaal

Rick Willemsen -

Monday, 14.55 - 15.20

Supervisors: Wilco van den Heuvel, Michel van de Velden



The goal of hierarchical clustering (HC) methods is to build a hierarchy of nested clusters, which is often visualised in a dendrogram. For certain scientific applications, such as finding phylogenetic (evolutionary) trees, it takes a long time to collect the data and the samples are often small. For these applications it makes sense that more computation time could be spend on finding good dendrograms. In practice, agglomerative (bottom-up) and divisive (top-down) clustering approaches are used, which are not guaranteed to find an optimal solution. Since 2016, a lot of research has been devoted on defining a suitable objective function for HC, which has led to several proposed objective functions and corresponding approximation results. However, exact methods have received little attention. We propose several exact methods for HC, which are based on insights from the partition based clustering (e.g. k-means clustering) literature. We propose mixed-integer programming formulations as well as a branch-and-price approach, which can easily be adapted to include other objective functions or constraints. In our computational study, we investigate the performance of commonly used heuristics and approximation algorithms.



Mixed-Integer Optimization with Constraint Learning


Session B: Angola-zaal

Donato Maragno -

Monday, 15.20 - 15.45

Supervisor: Dick den Hertog



We establish a broad methodological foundation for mixed-integer optimization with learned constraints. We propose an end-to-end pipeline for data-driven decision making in which constraints and objectives are directly learned from data using machine learning, and the trained models are embedded in an optimization formulation. We exploit the mixed-integer optimization-representability of many machine learning methods, including linear models, decision trees, ensembles, and multi-layer perceptrons. The consideration of multiple methods allows us to capture various underlying relationships between decisions, contextual variables, and outcomes. We also characterize a decision trust region using the convex hull of the observations, to ensure credible recommendations and avoid extrapolation. We efficiently incorporate this representation using column generation and clustering. In combination with domain-driven constraints and objective terms, the embedded models and trust region define a mixed-integer optimization problem for prescription generation. We implement this framework as a Python package (OptiCL) for practitioners. We demonstrate the method in both chemotherapy optimization and World Food Programme planning. The case studies illustrate the benefit of the framework in generating high-quality prescriptions, the value added by the trust region, the incorporation of multiple machine learning methods, and the inclusion of multiple learned constraints.