Faculty

# K-Projekt HOPL (Hagenberg)

This project aims to develop novel algorithms in order to gain additional optimization potential by modeling and optimizing interrelated logistics and production processes in an integrative way.

Efficient utilization of resources is essential for companies in order to offer products and services in a competitive and sustainable manner. Therefore, optimization is a key-technology in manifold domains to solve complex practical problems. A severe limitation of current optimization techniques is the difficulty of sufficiently formalizing the entire problem situation and complexity. Many existing problem models are abstracted and isolated formulations of real world situations in order to make existing optimization techniques applicable. Consequently, the optimized solutions are often hard to transfer into the real world as the inherent complexity and volatility of the problem situations have been lost.

The here proposing research consortium combines expertise in heuristic and exact optimization methods, production, logistics, and mechatronic systems. Novel, holistic optimization approaches will be developed and transferred into practice for solving real-world optimization problems that are crucial for the industrial partners. This will lead to optimized production and logistics systems and higher efficiency in terms of time and resources, so that the international competitiveness of the involved high-wage production companies is significantly strengthened.

In the presented K-Project the consortium plans to extend and integrate isolated problem models, simulation, and mathematical models into a new flexible system for real-world problem modeling and optimization. By combining these individual elements, a novel holistic optimization methodology will be researched and developed to enable an integrated optimization of the overall system; novel algorithmic concepts need to be researched, developed and applied in order to integrate multiple sub-problems of complex real-world systems as an optimization network. Thereby, the main focus will be laid on the (automated) interplay of different modeling and optimization methods such as simulation models, exact methods and metaheuristics in order to reach system-wide optimization of an overall complex real-world system.

The main goals for the application of optimization networks in this project are:

Integrated storage, transport, and schedule optimization

Strategic planning and design of production and logistics systems

Integration of data-based modeling in the optimization of production processes

The consortium is led by the research group HEAL of FH OÖ and includes highly competent scientific partners as well as research oriented company partners that face complex interrelated challenges requiring new algorithmic innovations.

Efficient utilization of resources is essential for companies in order to offer products and services in a competitive and sustainable manner. Therefore, optimization is a key-technology in manifold domains to solve complex practical problems. A severe limitation of current optimization techniques is the difficulty of sufficiently formalizing the entire problem situation and complexity. Many existing problem models are abstracted and isolated formulations of real world situations in order to make existing optimization techniques applicable. Consequently, the optimized solutions are often hard to transfer into the real world as the inherent complexity and volatility of the problem situations have been lost.

The here proposing research consortium combines expertise in heuristic and exact optimization methods, production, logistics, and mechatronic systems. Novel, holistic optimization approaches will be developed and transferred into practice for solving real-world optimization problems that are crucial for the industrial partners. This will lead to optimized production and logistics systems and higher efficiency in terms of time and resources, so that the international competitiveness of the involved high-wage production companies is significantly strengthened.

In the presented K-Project the consortium plans to extend and integrate isolated problem models, simulation, and mathematical models into a new flexible system for real-world problem modeling and optimization. By combining these individual elements, a novel holistic optimization methodology will be researched and developed to enable an integrated optimization of the overall system; novel algorithmic concepts need to be researched, developed and applied in order to integrate multiple sub-problems of complex real-world systems as an optimization network. Thereby, the main focus will be laid on the (automated) interplay of different modeling and optimization methods such as simulation models, exact methods and metaheuristics in order to reach system-wide optimization of an overall complex real-world system.

The main goals for the application of optimization networks in this project are:

Integrated storage, transport, and schedule optimization

Strategic planning and design of production and logistics systems

Integration of data-based modeling in the optimization of production processes

The consortium is led by the research group HEAL of FH OÖ and includes highly competent scientific partners as well as research oriented company partners that face complex interrelated challenges requiring new algorithmic innovations.

- FH-Prof. Priv.-Doz. Dipl.-Ing. Dr. Michael Affenzeller
- Dipl.-Ing. Andreas Beham
- Bogdan Burlacu
- Philipp Fleck MSc
- Mag. Viktoria Hauder
- Eva-Maria Holzleitner
- Johannes Karder MSc
- DI(FH) Dr. Michael Kommenda MSc
- FH-Prof. Dipl.-Ing. Dr. Gabriel Kronberger
- Dipl.-Ing. (FH) Dr. Erik Pitzer
- Sebastian Josef Raggl MSc
- Mag.Dipl.Ing. Judith Scagnetti
- FH-Prof. Dipl.-Ing. Dr. Stefan Wagner
- Prof.(FH) Dipl.-Ing. Dr. Stephan Winkler

2018

P. Fleck, M. Kommenda, T. Prante, M. Affenzeller - Novel Robustness Measures for Engineering Design Optimisation - International Journal of Simulation and Process Modelling, Vol. 13, No. 4, 2018
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2018

S. M. Winkler, M. Affenzeller, S. Winkler, G. K. Kronberger, M. Kommenda, P. Fleck - Similarity-based Analysis of Population Dynamics in Genetic Programming Performing Symbolic Regression in Genetic Programming Theory and Practice XIV (Contributions to Book: Part/Chapter/Section 1), (Editors: R. Riolo,…
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2018

A. Beham, S. Wagner, M. Affenzeller - Algorithm selection on generalized quadratic assignment problem landscapes - GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference, Kyoto, Japan, Japan, 2018, pp. 253-260
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2018

F. Tricoire, J. Scagnetti, A. Beham - New insights on the block relocation problem - COMPUTERS & OPERATIONS RESEARCH, 2018, pp. 127-139
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2018

J. Hidalgo, J. Colmenar, J. Velasco, G. K. Kronberger, S. M. Winkler, O. Garnica, J. Lanchares - Identification of Models for Glucose Blood Values in Diabetics by Grammatical Evolution in Handbook of Grammatical Evolution (Contributions to Book: Part/Chapter/Section 15), (Editors: Conor Ryan, Michael…
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2018

P. Fleck, D. Entner, C. Münzer, M. Kommenda, T. Prante, M. Schwarz, M. Hächl, M. Affenzeller - Box-Type Boom Design Using Surrogate Modeling: Introducing an Industrial Optimization Benchmark in Evolutionary and Deterministic Methods for Design Optimization and Control With Applications to Industrial…
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2018

G. K. Kronberger, M. Kommenda, E. Lughofer, S. Saminger-Platz, A. Promberger, F. Nickel, S. M. Winkler, M. Affenzeller - Using robust generalized fuzzy modeling and enhanced symbolic regression to model tribological systems - Applied Soft Computing, 2018, pp. 610-624
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2018

S. Raggl, A. Beham, F. Tricoire, M. Affenzeller - Solving a Real World Steel Stacking Problem - International Journal of Service and Computing Oriented Manufacturing, Vol. 3, No. 2, 2018, pp. 14
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2018

J. Fechter, A. Beham, S. Wagner, M. Affenzeller - Approximate Q-Learning for Stacking Problems with Continuous Production and Retrieval - Applied Artificial Intelligence, 2018
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2018

S. Raggl, A. Beham, V. Hauder, S. Wagner, M. Affenzeller - Discrete Real-world Problems in a Black-box Optimization Benchmark - GECCO '18: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Kyoto, Japan, Japan, 2018, pp. 1745-1752
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2018

M. Kommenda - Local Optimization and Complexity Control for Symbolic Regression - Phd Thesis, Johannes Kepler Universität, Austria, 2018, pp. 1-157
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2017

A. Beham, E. Pitzer, S. Wagner, M. Affenzeller - Integrating Exploratory Landscape Analysis into Metaheuristic Algorithms - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 473-480
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2017

J. Karder, S. Wagner, A. Beham, M. Kommenda, M. Affenzeller - Towards the Design and Implementation of Optimization Networks in HeuristicLab - GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, Germany, 2017, pp. 1209-1214
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2017

S. Raggl, B. Roljic, K. Schneeberger, A. Beham, S. Wagner, R. Cihal, M. Lettner, M. Wasner - Stahllogistik 4.0 - Die Stahlbranche in Bewegung in Logistics Research Yearbook 2017 (Contributions to Book: Part/Chapter/Section 6), - Trauner Verlag Linz, 2017, pp. 211-223
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2017

J. Karder, V. A. Hauder, A. Beham, S. Wagner, M. Affenzeller - Optimization Networks: An Innovative Solution Approach for Practical Logistics Problems in Logistics Research Yearbook 2017 - Trauner Verlag Linz, 2017, pp. 47-58
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2017

S. Wagner, A. Beham, M. Affenzeller - Analysis and Visualization of the Impact of Different Parameter Configurations on the Behavior of Evolutionary Algorithms - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 439-446
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2017

G. K. Kronberger, B. Burlacu, M. Kommenda, S. M. Winkler, M. Affenzeller - Measures for the Evaluation and Comparison of Graphical Model Structures - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 283-290
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2017

V. A. Hauder, J. Karder, A. Beham, S. Wagner, M. Affenzeller - A General Solution Approach for the Location Routing Problem - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 8
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2017

M. Affenzeller, B. Burlacu, S. M. Winkler, M. Kommenda, G. K. Kronberger, S. Wagner - Offspring Selection Genetic Algorithm Revisited: Improvements in Efficiency by Early Stopping Criteria in the Evaluation of Unsuccessful Individuals - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria,…
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2017

V. A. Hauder, A. Beham, S. Wagner, M. Affenzeller - Optimization Networks for Real-World Production and Logistics Problems - GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, Germany, 2017, pp. 4
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2017

S. Raggl, A. Beham, S. Wagner, M. Affenzeller - Analysing a Hybrid Model-Based Evolutionary Algorithm for a Hard Grouping Problem - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 347-354
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2017

M. Kommenda, J. Karder, A. Beham, B. Burlacu, G. K. Kronberger, S. Wagner, M. Affenzeller - Optimization Networks for Integrated Machine Learning - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 392-399
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2017

B. Burlacu, M. Affenzeller, M. Kommenda, G. K. Kronberger, S. M. Winkler - Analysis of Schema Frequencies in Genetic Programming - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 7
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2017

R. Cihal, A. Beham, S. Raggl, E. Hölzl, K. Probst, H. Moser - Slab Stacking Automation in the Hot Slab Yard as Key Factor for Post-Processing of Cast Slabs - Proceedings of the 9th ECCC European Continuous Casting Conference (ECCC 2017), Vienna, Austria, 2017, pp. 1122-1131
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2017

A. Beham, M. Affenzeller, S. Wagner - Instance-based Algorithm Selection on Quadratic Assignment Problem Landscapes - GECCO '17: Proceedings of the Genetic and Evolutionary Computation Conference Companion, Berlin, Germany, Germany, 2017, pp. 1471-1478
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2017

E. Pitzer, M. Affenzeller - Facilitating Evolutionary Algorithm Analysis with Persistent Data Structures - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 416-423
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2017

J. Karder, A. Beham, S. Wagner, M. Affenzeller - Solving the Traveling Thief Problem using Orchestration in Optimization Networks - Lecture Notes in Computer Science 10671, Las Palmas de Gran Canaria, Spain, 2017, pp. 307-315
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2016

S. Raggl, A. Beham, F. Tricoire, M. Affenzeller - A Tree-Search Based Heuristic for a Complex Stacking Problem with Continous Production and Retrieval - Proceedings of the 28th European Modeling and Simulation Symposium EMSS 2016, Larnaca, Cyprus, 2016, pp. 6
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2016

E. Lughofer, G. K. Kronberger, M. Kommenda, S. Saminger-Platz, A. Promberger, F. Nickel, S. M. Winkler, M. Affenzeller - Robust Fuzzy Modeling and Symbolic Regression for Establishing Accurate and Interpretable Prediction Models in Supervising Tribological Systems - Proceedings of the 8th International…
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2016

V. A. Hauder, A. Beham, S. Wagner - Integrated Performance Measurement for Optimization Networks in Smart Enterprises - On the Move to Meaningful Internet Systems: OTM 2016 Workshops (Lecture Notes in Computer Science, LNCS 10034), Rhodes, Greece, 2016, pp. 10
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2016

A. Beham, S. Wagner, M. Affenzeller - Optimization Knowledge Center - Companion Publication of the 2016 Genetic and Evolutionary Computation Conference, GECCO'16 Companion, Denver, Colorado, United States of America, 2016, pp. 1331-1338
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2015

S. M. Winkler, M. Affenzeller, G. K. Kronberger, M. Kommenda, B. Burlacu, S. Wagner - Sliding Window Symbolic Regression for Detecting Changes of System Dynamics in Genetic Programming Theory and Practice XII (Contributions to Book: Part/Chapter/Section 6), (Editors: Rick Riolo, William P. Worzel, Mark…
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2015

J. Fechter, A. Beham, S. Wagner, M. Affenzeller - Modeling a Lot-Aware Slab Stack Shuffling Problem - Lecture Notes in Computer Science LNCS 9520, Las Palmas, Gran Canaria, Spain, 2015, pp. 334-341
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2015

B. Burlacu, M. Affenzeller, M. Kommenda - On the Effectiveness of Genetic Operations in Symbolic Regression - Lecture Notes in Computer Science LNCS 9520, Las Palmas, Gran Canaria, Spain, 2015, pp. 367-374
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2015

A. Scheibenpflug, A. Beham, M. Kommenda, J. Karder, S. Wagner, M. Affenzeller - Simplifying Problem Definitions in the HeuristicLab Optimization Environment - Companion Publication of the 2015 Genetic and Evolutionary Computation Conference, GECCO'15 Companion, Madrid, Spain, 2015, pp. 1101-1108
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2015

M. Affenzeller, A. Beham, S. Vonolfen, E. Pitzer, S. M. Winkler, S. Hutterer, M. Kommenda, M. Kofler, G. K. Kronberger, S. Wagner - Simulation-Based Optimization with HeuristicLab in Applied Simulation and Optimization (Contributions to Book: Part/Chapter/Section 1), (Editors: M. Mujica Mota, I. De…
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2015

A. Petrakova, M. Affenzeller, G. Merkuryeva - Heterogeneous versus Homogeneous Machine Learning Ensembles - Information Technology and Management Science, Vol. 18, No. 1, 2015, pp. 135-142
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2015

B. Burlacu, M. Kommenda, M. Affenzeller - Building Blocks Identification Based on Subtree Sample Counts for Genetic Programming - Proceedings of the 3rd Asia-Pacific Conference on Computer Aided System Engineering Conference (APCASE 2015), Quito, Ecuador, Ecuador, 2015, pp. 6
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2015

S. Raggl, J. Fechter, A. Beham - A Dynamic Multicommodity Network Flow Problem For Logistics Networks - Proceedings of the 27th European Modeling and Simulation Symposium EMSS 2015, Bergeggi, Italy, 2015, pp. 6
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2015

M. Kommenda, M. Affenzeller, G. K. Kronberger, B. Burlacu, S. M. Winkler - Multi-Population Genetic Programming with Data Migration for Symbolic Regression in Computational Intelligence and Efficiency in Engineering Systems (Contributions to Book: Part/Chapter/Section 6), (Editors: G. Borowik, Z. Chaczko,…
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2015

G. K. Kronberger, M. Kommenda - Search Strategies for Grammatical Optimization Problems – Alternatives to Grammar-Guided Genetic Programming in Computational Intelligence and Efficiency in Engineering Systems (Contributions to Book: Part/Chapter/Section 7), (Editors: G. Borowik, Z. Chaczko, L.G. Ford,…
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2015

G. K. Kronberger, M. Kommenda, S. M. Winkler, M. Affenzeller - Using Contextual Information in Sequential Search for Grammatical Optimization Problems - Lecture Notes in Computer Science LNCS 9520, Las Palmas, Gran Canaria, Spain, 2015, pp. 417-424
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2015

B. Burlacu, M. Affenzeller, S. M. Winkler, M. Kommenda, G. K. Kronberger - Methods for Genealogy and Building Blocks Analysis in Genetic Programming in Computational Intelligence and Efficiency in Engineering Systems (Contributions to Book: Part/Chapter/Section 5), (Editors: G. Borowik, Z. Chaczko,…
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2015

J. Fechter, A. Beham, S. Wagner, M. Affenzeller - Modelling a Clustered Generalized Quadratic Assignment Problem - Proceedings of the 27th European Modeling and Simulation Symposium EMSS 2015, Bergeggi, Italy, 2015, pp. 7
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2015

M. Kommenda, A. Beham, M. Affenzeller, G. K. Kronberger - Complexity Measures for Multi-Objective Symbolic Regression - Lecture Notes in Computer Science LNCS 9520, Las Palmas, Gran Canaria, Spain, 2015, pp. 409-416
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