TY - GEN
T1 - Multi-criteria Optimization of Workflow-Based Assembly Tasks in Manufacturing
AU - Holzinger, Florian
AU - Beham, Andreas
N1 - Funding Information:
Acknowledgments. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 101017151.
Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023/2/10
Y1 - 2023/2/10
N2 - 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.
AB - 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.
KW - ADAPT
KW - Assembly task optimization
KW - Decision making
KW - Multi-objective optimization
UR - http://www.scopus.com/inward/record.url?scp=85151145641&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-25312-6_5
DO - 10.1007/978-3-031-25312-6_5
M3 - Conference contribution
SN - 9783031253119
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 45
EP - 52
BT - Computer Aided Systems Theory – EUROCAST 2022 - 18th International Conference, Revised Selected Papers
A2 - Moreno-Díaz, Roberto
A2 - Pichler, Franz
A2 - Quesada-Arencibia, Alexis
PB - Springer
CY - Cham
ER -