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18th International Conference on Computer Aided Systems Theory, EUROCAST 2022

作者： Holzinger, Florian Beham, Andreas Heuristic and Evolutionary Algorithms Laboratory School of Informatics Communications and Media University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria

ISBN: (纸本)9783031253119

Industrial manufacturing is currently amidst it’s fourth great revolution, pushing towards the digital transformation of production processes. One key element of this transformation is the formalization and digitization of processes, creating an increased potential to monitor, understand and optimize existing processes. However, one major obstacle in this process is the increased diversification and specialisation, resulting in the dependency on multiple experts, which are rarely amalgamated in small to medium sized companies. To mitigate this issue, this paper presents a novel approach for multi-criteria optimization of workflow-based assembly tasks in manufacturing by combining a workflow modeling framework and the heuristicLab optimization framework. For this endeavour, a new generic problem definition is implemented in heuristicLab, enabling the optimization of arbitrary workflows represented with the modeling framework. The resulting Pareto front of the multi-criteria optimization provides the decision makers a set of optimal workflows from which they can choose to optimally fit the current demands. The advantages of the herein presented approach are highlighted with a real world use case from an ongoing research project. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Identification of Dynamical Systems Using Symbolic Regression 17th

Identification of Dynamical Systems Using Symbolic Regressio...

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17th International Conference on Computer Aided Systems Theory, EUROCAST 2019

作者： Kronberger, Gabriel Kammerer, Lukas Kommenda, Michael Josef Ressel Centre for Symbolic Regression Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria

ISBN: (纸本)9783030450922

We describe a method for the identification of models for dynamical systems from observational data. The method is based on the concept of symbolic regression and uses genetic programming to evolve a system of ordinary differential equations (ODE). The novelty is that we add a step of gradient-based optimization of the ODE parameters. For this we calculate the sensitivities of the solution to the initial value problem (IVP) using automatic differentiation. The proposed approach is tested on a set of 19 problem instances taken from the literature which includes datasets from simulated systems as well as datasets captured from mechanical systems. We find that gradient-based optimization of parameters improves predictive accuracy of the models. The best results are obtained when we first fit the individual equations to the numeric differences and then subsequently fine-tune the identified parameter values by fitting the IVP solution to the observed variable values. © 2020, Springer Nature Switzerland AG.

关键词： Initial value problems

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Asynchronous surrogate-assisted optimization networks 18

Asynchronous surrogate-assisted optimization networks

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2018 Genetic and evolutionary Computation Conference, GECCO 2018

作者： Karder, Johannes Beham, Andreas Werth, Bernhard Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

ISBN: (纸本)9781450357647

This paper introduces a new, highly asynchronous method for surrogate-assisted optimization where it is possible to concurrently create surrogate models, evaluate fitness functions and do parameter optimization for the underlying problem, effectively eliminating sequential workflows of other surrogate-assisted algorithms. Using optimization networks, each part of the optimization process is exchangeable. First experiments are done for single objective benchmark functions, namely Ackley, Griewank, Schwefel and Rastrigin, using problem sizes starting from 2D up to 10D, and other EGO implementations are used as baseline for comparison. First results show that the implemented network approach is competitive to other EGO implementations in terms of achieved solution qualities and more efficient in terms of execution times. © 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.

关键词： Optimization

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Confidence-based ensemble modeling in medical data mining 18

Confidence-based ensemble modeling in medical data mining

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2018 Genetic and evolutionary Computation Conference, GECCO 2018

作者： Kammerer, Lukas Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

ISBN: (纸本)9781450357647

A recent approach for improving the accuracy of ensemble models is confidence-based modeling. Thereby, confidence measures, which indicate an ensemble prediction's reliability, are used for identifying unreliable predictions in order to improve a model's accuracy among reliable predictions. However, despite promising results in previous work, no comparable results for public benchmark data sets have been published yet. This paper applies confidence-based modeling with GP-based symbolic binary classification ensembles on a set of medical benchmark problems to make statements about the concept's general applicability. Moreover, extensions for multiclass classification problems are proposed. © 2018 Copyright held by the owner/author(s).

关键词： Machine learning

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Investigating the Dynamic Block Relocation Problem 17th

Investigating the Dynamic Block Relocation Problem

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17th International Conference on Computer Aided Systems Theory, EUROCAST 2019

作者： Raggl, Sebastian Beham, Andreas Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

ISBN: (纸本)9783030450922

The dynamic block relocation problem is a variant of the BRP where the initial configuration and retrieval priorities are known but are subject to change during the implementation of an optimized solution. This paper investigates two kinds of potential changes. The exchange of assigned priorities between two blocks and the arrival of new blocks. For both kind of events we present algorithms that can adjust an existing solution to the changed situation. These algorithms are combined with a branch and bound based solver to enable online optimization with look-ahead. Our experiments show that the algorithms enable finding better solutions in a shorter time after a event occurs. © 2020, Springer Nature Switzerland AG.

关键词： Changing priorities Dynamic block relocation problem

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Simulation-based optimisation for worker cross-training

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International Journal of Simulation and Process Modelling 2021年第3期16卷 185-194页

作者： Karder, Johannes Beham, Andreas Hauder, Viktoria A. Altendorfer, Klaus Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria Department of Operations Research University of Applied Sciences Upper Austria Steyr Austria

Worker cross-training is a problem arising in many companies that involve human work. To perform certain activities, workers are required to possess certain skills. Cross-trained workers possess even multiple skills, which enables a more flexible deployment, but also incurs higher costs. Thus, companies seek to balance the available skills such that customer deadlines can be met in a cost-efficient way. In this work we compare solution approaches for a simulation-based problem formulation with three objectives. We apply evolutionary multi-objective optimisation to a production system scenario with two lines and six workstations. Their performance is compared for a hard scenario where cross-training is essential to achieve high service levels. Results indicate that the algorithms are able to solve this three-objective formulation quite well using the described encoding and operators. Employing this technology at companies could lead to better qualification strategies and a better contribution of qualification efforts to company goals. © 2021 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Multiobjective optimization

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Comparing Shape-Constrained Regression algorithms for Data Validation 18th

Comparing Shape-Constrained Regression Algorithms for Data ...

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18th International Conference on Computer Aided Systems Theory, EUROCAST 2022

作者： Bachinger, Florian Kronberger, Gabriel Josef Ressel Center for Symbolic Regression Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Johannes Kepler University Linz Austria

ISBN: (纸本)9783031253119

Industrial and scientific applications handle large volumes of data that render manual validation by humans infeasible. Therefore, we require automated data validation approaches that are able to consider the prior knowledge of domain experts to produce dependable, trustworthy assessments of data quality. Prior knowledge is often available as rules that describe interactions of inputs with regard to the target e.g. the target must be monotonically decreasing and convex over increasing input values. Domain experts are able to validate multiple such interactions at a glance. However, existing rule-based data validation approaches are unable to consider these constraints. In this work, we compare different shape-constrained regression algorithms for the purpose of data validation based on their classification accuracy and runtime performance. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Regression analysis

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Solution approaches for the dynamic stacking problem 20

Solution approaches for the dynamic stacking problem

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2020 Genetic and evolutionary Computation Conference, GECCO 2020

作者： Raggl, Sebastian Beham, Andreas Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Institute for Formal Models and Verification Johannes Kepler University Linz Austria

ISBN: (纸本)9781450371278

In this paper we describe a dynamic stacking problem as it arises in a more complex form in the steel industry. We describe a simulation model and the simulated processes that are implemented. The model covers a gantry crane that performs relocations of blocks among three types of stacks. In the real world the crane operators and dispatchers solve complex stacking problems targeting to minimize relocation effort while adhering to many constraints, to various time windows, and to satisfy quality demands. While our model does not include all the real-world constraints, the challenges and benefits of solving this problem as a dynamic optimization problem in contrast to a static formulation become apparent. We adapt an existing solution approach from a static formulation that is based on the block relocation problem and compare it with a hand-written rule-based approach. Furthermore, we devise a hybrid approach in the form of an open-ended evolutionary algorithm. © 2020 ACM.

关键词： Steelmaking

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Surrogate-Assisted Fitness Landscape Analysis for Computationally Expensive Optimization 17th

Surrogate-Assisted Fitness Landscape Analysis for Computatio...

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17th International Conference on Computer Aided Systems Theory, EUROCAST 2019

作者： Werth, Bernhard Pitzer, Erik Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Johannes Kepler University Altenberger Straße 68 Linz4040 Austria

ISBN: (纸本)9783030450922

Exploratory fitness landscape analysis (FLA) is a category of techniques that try to capture knowledge about a black-box optimization problem. This is achieved by assigning features to a certain problem instance utilizing only information obtained by evaluating the black-box. This knowledge can be used to obtain new domain knowledge but more often the intended use is to automatically find an appropriate heuristic optimization algorithm [9]. FLA-based algorithm selection and parametrization hinges on the idea, that, while no optimization algorithm can be the optimal choice for all black-box problems, algorithms are expected to work similarly well on problems with similar statistical characteristics [8, 15]. © 2020, Springer Nature Switzerland AG.

关键词： Optimization

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Dynamic transport-lot assignment for the hot-storage area 36

Dynamic transport-lot assignment for the hot-storage area

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36th European Modeling and Simulation Symposium, EMSS 2024, Held at the 21st International Multidisciplinary Modeling and Simulation Multiconference, I3M 2024

作者： Leitner, Sebastian Fleck, Philipp Wagner, Stefan Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Austria Josef Ressel Center for Adaptive Optimization in Dynamic Environments University of Applied Sciences Upper Austria Austria

ISBN: (纸本)9791281988026

In this paper, we present a novel transport-lot optimization problem at the boundary between the continuous casters and the hot-rolling in steel production. The problem and the corresponding solver are part of a system that jointly optimizes the cranes in the hot-storage area and the vehicles feeding the rolling mill. The goal is to group steel slabs into transport lots and assign transporters, handover locations, and due dates. We develop heuristics to iteratively build the full solution by choosing from several promising choices that satisfy a wide variety of safety and performance requirements. We evaluate the performance of different heuristics to search the tree of possible choices on a diverse collection of real-world problem instances. We find that the pilot method significantly outperforms the five other tree-search heuristics we tried. © 2024 The Authors.

关键词： heuristic methods

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