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Multi-criteria optimization of workflow-based assembly tasks in manufacturing

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arXiv 2023年

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

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. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

Multi tree operators for genetic programming to identify optimal energy flow controllers 21

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Multi tree operators for genetic programming to identify opt...

2021 Genetic and evolutionary Computation Conference, GECCO 2021

作者： Kefer, Kathrin Hanghofer, Roland Kefer, Patrick Stöger, Markus Hofer, Bernd Affenzeller, Michael Winkler, Stephan Fronius International GmbH Thalheim Austria Research and Develoment Fh OO7die Forschungs und Entwicklungs GmbH Research Group Heuristic and Evolutionary Algorithms Laboratory Hagenberg Austria Research and Develoment Fh Oö Forschungs und Entwicklungs GmbH Research Group Asic Wels Austria

ISBN: (纸本)9781450383516

Genetic programming is known to be able to find nearly optimal solutions for quite complex problems. So far, the focus was more on solution candidates that hold just one symbolic regression tree. For complex problems like controlling the energy flows of a building in order to minimize its energy costs, this is often not sufficient. This is why this work presents a solution candidate implementation in heuristicLab where they hold multiple symbolic regression trees. Additionally, also new crossover and mutation operators were implemented as the existing ones cannot handle multiple trees in one solution candidate. The first type of operators applies them on all trees in the solution candidate, whereas the second one only applies them to one of the trees. It is found that applying the mutator to only one of the trees significantly reduces the training duration. Applying the crossover to one of the trees instead of all needs longer training times but can also achieve better results. © 2021 ACM.

关键词： Genetic programming

Online Diversity Control in Symbolic Regression via a Fast Hash-based Tree Similarity Measure

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arXiv 2019年

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

Diversity represents an important aspect of genetic programming, being directly correlated with search performance. When considered at the genotype level, diversity often requires expensive tree distance measures which have a negative impact on the algorithm’s runtime performance. In this work we introduce a fast, hash-based tree distance measure to massively speed-up the calculation of population diversity during the algorithmic run. We combine this measure with the standard GA and the NSGA-II genetic algorithms to steer the search towards higher diversity. We validate the approach on a collection of benchmark problems for symbolic regression where our method consistently outperforms the standard GA as well as NSGA-II configurations with different secondary objectives. Copyright © 2019, The Authors. All rights reserved.

关键词： Genetic programming

Identification of dynamical systems using symbolic regression

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arXiv 2021年

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

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. Copyright © 2021, The Authors. All rights reserved.

关键词： Genetic programming

Smooth symbolic regression: Transformation of symbolic regression into a real-valued optimization problem

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arXiv 2021年

作者： Pitzer, Erik Kronberger, Gabriel Heuristic and Evolutionary Algorithms Laboratory School of Informatics Communications and Media University of Applied Sciences Upper Austria Franz-Fritsch Strasse 11 Wels4600 Austria

The typical methods for symbolic regression produce rather abrupt changes in solution candidates. In this work, we have tried to transform symbolic regression from an optimization problem, with a landscape that is so rugged that typical analysis methods do not produce meaningful results, to one that can be compared to typical and very smooth real-valued problems. While the ruggedness might not interfere with the performance of optimization, it restricts the possibilities of analysis. Here, we have explored different aspects of a transformation and propose a simple procedure to create real-valued optimization problems from symbolic regression problems. Copyright © 2021, The Authors. All rights reserved.

关键词： Optimization

A tree-search based heuristic for a complex stacking problem with continuous production and retrieval 28

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A tree-search based heuristic for a complex stacking problem...

28th European Modeling and Simulation Symposium, EMSS 2016

作者： Raggl, Sebastian Andreas, Beham Tricoire, Fabien Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Softwarepark 11 Hagenberg4232 Austria Institute for Formal Models and Verification Johannes Kepler University Linz Altenberger Straße 69 Linz4040 Austria Department of Business Administration University of Vienna Oskar-Morgenstern-Platz 1 Vienna1090 Austria

ISBN: (纸本)9788897999683

We present a real world steel stacking problem featuring non-instantaneous crane movements, continuous production and retrieval and stacking constraints based on the dimensions as well as temperature of the slabs. An exact Branch & Bound solver as well as three tree-search based heuristics is developed. Random benchmark instances derived from the real world problem are used to evaluate the performance of the heuristic solvers and compare them to the exact solver.

关键词： Benchmarking

Performance of industrial sensor data persistence in data vault 30

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Performance of industrial sensor data persistence in data va...

30th European Modeling and Simulation Symposium, EMSS 2018

作者： Bachinger, Florian Zenisek, Jan Kammerer, Lukas Stimpfl, Martin Kronberger, Gabriel Josef Ressel Centre for Symbolic Regression School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria Heuristic and Evolutionary Algorithms Laboratory University of Applied Sciences Upper Austria Hagenberg Austria Miba AG Dr. Mitterbauer Str. 3 Postfach 3 LaakirchenA-4663 Austria

ISBN: (纸本)9788885741065

Today manufacturing companies are facing important challenges from the market in terms of flexibility, ever growing product mixes, small lot sizes, high competition, etc. To meet these market conditions, digitalization and the use of data are offering a viable toolset considering the advances in the field throughout the last couple of years. The increasing use of sensor technology and the need for interconnecting data from different departments in smart production leads to a surge of recorded data. Persistence and integration of heterogeneous data, generated in a variety of software systems, is a key factor to gain value from data and its analysis. High flexibility in regards to the model is required to accommodate the data. Hence, application of the data vault modelling approach is a fitting candidate to design a data warehouse model. In this paper we present a data vault model for factory sensor data. We analyze the performance of the data warehouse in regards to bulk load of data and common analytic queries. © Institute of Information Science. All rights reserved.

关键词： Data warehouses

Optimization approaches for the physical internet

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Optimization approaches for the physical internet

Confederated International International Workshop on Enterprise Integration, Interoperability and Networking, EI2N 2017, Fact Based Modeling, FBM 2017, Industry Case Studies Program, ICSP 2017, International Workshop on Methods, Evaluation, Tools and Applications for the Creation and Consumption of Structured Data for the e-Society, Meta4eS 2017, OnTheMove Academy, OTMA 2017 and ODBASE posters 2017, held as part of OTM 2017

作者： Hauder, Viktoria A. Pitzer, Erik Affenzeller, Michael Heuristic and Evolutionary Algorithms Laboratory School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Campus Hagenberg im Mühlkreis Austria Institute for Production and Logistics Management Johannes Kepler University Linz Linz Austria Institute for Formal Models and Verification Johannes Kepler University Linz Linz Austria

ISBN: (纸本)9783319738048

With the Physical Internet (PI), a global economic transformation towards a more holistic consideration and collaboration of different agents is promoted. Its goal is the implementation of global sustainable logistics by increasing the utilization of available resources. This should be reached by developing a network in which goods of different organizations are transported, stored and handled. One aspect of the PI is the optimization of the transport procedure. In this paper, potential optimization models for a subset of three key elements of the PI, the containers, movers (all kinds of transportation means), and nodes (all kinds of locations), are presented. Moreover, corresponding algorithmic approaches are proposed. The novel modeling approach of optimization networks (ON) with the objective of finding an overall optimized solution considering the involved interrelated subtasks in a holistic sense is compared to highly reactive solution methods based on rules that are learned offline. With the proposed approaches, interoperability between different (economic) agents and (software) systems is supported. The goal of multidisciplinary collaboration of different participants and their integration into one PI network should be approached. © Springer International Publishing AG 2018.

关键词： Interoperability

Music Segmentation With Genetic algorithms

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Music Segmentation With Genetic Algorithms

International Workshop on Database and Expert Systems Applications

作者： Brigitte Rafael Stefan Oertl Michael Affenzeller Stefan Wagner Re-Compose GmbH Vienna Austria Heuristic and Evolutionary Algorithms Laboratory School of Informatics Communications and Media Upper Austria University of Applied Sciences Hagenberg Austria

Music segmentation is a key issue in music information retrieval (MIR) as it provides an insight into the structure of a composition. Based on structural information, several tasks related to MIR such as searching and browsing large music collections, visualizing musical structure, lyric alignment, and music summarization can be further *** approaches are available to achieve an appropriate segmentation of a given composition. The authors of this paper present an approach to apply genetic algorithms for a solution to the segmentation problem.

关键词： Genetic algorithms Genetic mutations Runtime Music information retrieval Visualization Encoding Databases Expert systems evolutionary computation Laboratories