Design of an Intelligent Handling System using a Multi-Objective Optimization Approach

• Published in: 2018 IEEE 23rd International Conference on Emerging Technologies and Factory Automation (ETFA) Vol. 1; pp. 887 - 894
• Main Authors: Bonini, Tommaso, Forni, Andrea, Mazzolini, Mauro
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-09-2018
• Subjects: Genetic Algorithms; Knowledge-based engineering; Manufacturing system design; Optimization
• DOI: 10.1109/ETFA.2018.8502481
• EISSN: 1946-0759

Increasing product differentiation and shortening of product lifecycle impose a deep change in production systems and automation solutions, moving toward architectures and technologies that enable real time distributed flexibility, rapid reconfiguration and global optimization pursuing specific strategic objectives. The use of modern information technologies, such as the cyber-physical systems or the internet of things, and the processing of vast amounts of data (Big Data), is a foundation of a concept known as Industry 4.0. There is therefore the strong need for new design paradigms and structured approaches supported by knowledge-based systems technology, artificial intelligence (AI) and optimization techniques. Especially the adoption of multi-level and multi-objective optimization strategies involving all the phases of the system lifecycle, starting from its conception and design to commissioning and day-to-day operative management is becoming mandatory to fulfill required high level of manufacturing performance. Within the presented high complexity of the modern manufacturing world, this paper is intended to be an example of how it is possible to handle this challenging and demanding context using a multi-objective optimization technique (in this case a genetic algorithm) for an intelligent handling system design.