Intelligent production control for time-constrained complex job shops

Im Zuge der zunehmenden Komplexität der Produktion wird der Wunsch nach einer intelligenten Steuerung der Abläufe in der Fertigung immer größer. Sogenannte Complex Job Shops bezeichnen dabei die komplexesten Produktionsumgebungen, die deshalb ein hohes Maß an Agilität in der Steuerung erfordern. Unter diesen Umgebungen sticht die besonders Halbleiterfertigung hervor, da sie alle Komplexitäten eines Complex Job-Shop vereint. Deshalb ist die operative Exzellenz der Schlüssel zum Erfolg in der Halbleiterindustrie. Diese Exzellenz hängt ganz entscheidend von einer intelligenten Produktionssteuerung ab. Ein Hauptproblem bei der Steuerung solcher Complex Job-Shops, in diesem Fall der Halbleiterfertigung, ist das Vorhandensein von Zeitbeschränkungen (sog. time-constraints), die die Transitionszeit von Produkten zwischen zwei, meist aufeinanderfolgenden, Prozessen begrenzen. Die Einhaltung dieser produktspezifischen Zeitvorgaben ist von größter Bedeutung, da Verstöße zum Verlust des betreffenden Produkts führen. Der Stand der Technik bei der Produktionssteuerung dieser Dispositionsentscheidungen, die auf die Einhaltung der Zeitvorgaben abzielen, basiert auf einer fehleranfälligen und für die Mitarbeiter belastenden manuellen Steuerung. In dieser Arbeit wird daher ein neuartiger, echtzeitdatenbasierter Ansatz zur intelligenten Steuerung der Produktionssteuerung für time-constrained Complex Job Shops vorgestellt. Unter Verwendung einer jederzeit aktuellen Replikation des realen Systems werden sowohl je ein uni-, multivariates Zeitreihenmodell als auch ein digitaler Zwilling genutzt, um Vorhersagen über die Verletzung dieser time-constraints zu erhalten. In einem zweiten Schritt wird auf der Grundlage der Erwartung von Zeitüberschreitungen die Produktionssteuerung abgeleitet und mit Echtzeitdaten anhand eines realen Halbleiterwerks implementiert. Der daraus resultierende Ansatz wird gemeinsam mit dem Stand der Technik validiert und zeigt signifikante Verbesserungen, da viele Verletzungen von time-constraints verhindert werden können. Zukünftig soll die intelligente Produktionssteuerung daher in weiteren Complex Job Shop-Umgebungen evaluiert und ausgerollt werden

Adaptive Order Dispatching based on Reinforcement Learning: Application in a Complex Job Shop in the Semiconductor Industry

Heutige Produktionssysteme tendieren durch die Marktanforderungen getrieben zu immer kleineren Losgrößen, höherer Produktvielfalt und größerer Komplexität der Materialflusssysteme. Diese Entwicklungen stellen bestehende Produktionssteuerungsmethoden in Frage. Im Zuge der Digitalisierung bieten datenbasierte Algorithmen des maschinellen Lernens einen alternativen Ansatz zur Optimierung von Produktionsabläufen. Aktuelle Forschungsergebnisse zeigen eine hohe Leistungsfähigkeit von Verfahren des Reinforcement Learning (RL) in einem breiten Anwendungsspektrum. Im Bereich der Produktionssteuerung haben sich jedoch bisher nur wenige Autoren damit befasst. Eine umfassende Untersuchung verschiedener RL-Ansätze sowie eine Anwendung in der Praxis wurden noch nicht durchgeführt. Unter den Aufgaben der Produktionsplanung und -steuerung gewährleistet die Auftragssteuerung (order dispatching) eine hohe Leistungsfähigkeit und Flexibilität der Produktionsabläufe, um eine hohe Kapazitätsauslastung und kurze Durchlaufzeiten zu erreichen. Motiviert durch komplexe Werkstattfertigungssysteme, wie sie in der Halbleiterindustrie zu finden sind, schließt diese Arbeit die Forschungslücke und befasst sich mit der Anwendung von RL für eine adaptive Auftragssteuerung. Die Einbeziehung realer Systemdaten ermöglicht eine genauere Erfassung des Systemverhaltens als statische Heuristiken oder mathematische Optimierungsverfahren. Zusätzlich wird der manuelle Aufwand reduziert, indem auf die Inferenzfähigkeiten des RL zurückgegriffen wird. Die vorgestellte Methodik fokussiert die Modellierung und Implementierung von RL-Agenten als Dispatching-Entscheidungseinheit. Bekannte Herausforderungen der RL-Modellierung in Bezug auf Zustand, Aktion und Belohnungsfunktion werden untersucht. Die Modellierungsalternativen werden auf der Grundlage von zwei realen Produktionsszenarien eines Halbleiterherstellers analysiert. Die Ergebnisse zeigen, dass RL-Agenten adaptive Steuerungsstrategien erlernen können und bestehende regelbasierte Benchmarkheuristiken übertreffen. Die Erweiterung der Zustandsrepräsentation verbessert die Leistung deutlich, wenn ein Zusammenhang mit den Belohnungszielen besteht. Die Belohnung kann so gestaltet werden, dass sie die Optimierung mehrerer Zielgrößen ermöglicht. Schließlich erreichen spezifische RL-Agenten-Konfigurationen nicht nur eine hohe Leistung in einem Szenario, sondern weisen eine Robustheit bei sich ändernden Systemeigenschaften auf. Damit stellt die Forschungsarbeit einen wesentlichen Beitrag in Richtung selbstoptimierender und autonomer Produktionssysteme dar. Produktionsingenieure müssen das Potenzial datenbasierter, lernender Verfahren bewerten, um in Bezug auf Flexibilität wettbewerbsfähig zu bleiben und gleichzeitig den Aufwand für den Entwurf, den Betrieb und die Überwachung von Produktionssteuerungssystemen in einem vernünftigen Gleichgewicht zu halten

Design and Management of Manufacturing Systems

Although the design and management of manufacturing systems have been explored in the literature for many years now, they still remain topical problems in the current scientific research. The changing market trends, globalization, the constant pressure to reduce production costs, and technical and technological progress make it necessary to search for new manufacturing methods and ways of organizing them, and to modify manufacturing system design paradigms. This book presents current research in different areas connected with the design and management of manufacturing systems and covers such subject areas as: methods supporting the design of manufacturing systems, methods of improving maintenance processes in companies, the design and improvement of manufacturing processes, the control of production processes in modern manufacturing systems production methods and techniques used in modern manufacturing systems and environmental aspects of production and their impact on the design and management of manufacturing systems. The wide range of research findings reported in this book confirms that the design of manufacturing systems is a complex problem and that the achievement of goals set for modern manufacturing systems requires interdisciplinary knowledge and the simultaneous design of the product, process and system, as well as the knowledge of modern manufacturing and organizational methods and techniques

Working Notes from the 1992 AAAI Spring Symposium on Practical Approaches to Scheduling and Planning

The symposium presented issues involved in the development of scheduling systems that can deal with resource and time limitations. To qualify, a system must be implemented and tested to some degree on non-trivial problems (ideally, on real-world problems). However, a system need not be fully deployed to qualify. Systems that schedule actions in terms of metric time constraints typically represent and reason about an external numeric clock or calendar and can be contrasted with those systems that represent time purely symbolically. The following topics are discussed: integrating planning and scheduling; integrating symbolic goals and numerical utilities; managing uncertainty; incremental rescheduling; managing limited computation time; anytime scheduling and planning algorithms, systems; dependency analysis and schedule reuse; management of schedule and plan execution; and incorporation of discrete event techniques

Advanced Process Monitoring for Industry 4.0

This book reports recent advances on Process Monitoring (PM) to cope with the many challenges raised by the new production systems, sensors and “extreme data” conditions that emerged with Industry 4.0. Concepts such as digital-twins and deep learning are brought to the PM arena, pushing forward the capabilities of existing methodologies to handle more complex scenarios. The evolution of classical paradigms such as Latent Variable modeling, Six Sigma and FMEA are also covered. Applications span a wide range of domains such as microelectronics, semiconductors, chemicals, materials, agriculture, as well as the monitoring of rotating equipment, combustion systems and membrane separation processes

A CAPACITY MODEL FOR RESEARCH BASED GOVERNMENT MANUFACTURING SYSTEMS

Manufacturing systems take longer than necessary to be designed and implemented, hence the greater developmental cost. A class of manufacturing systems exist which would benefit from the concepts of reverse engineering, to reduce lead times for establishing critical manufacturing capabilities essential to national safety and security. There is a need to reverse engineer these manufacturing systems as no current system and/or body of knowledge exists. Manufacturing systems vary in their ability to deliver products in an efficient and reliable manner and hence the variability in national readiness. Presently the design of manufacturing systems for some critical operations ranges from an educated trial and error process to duplicating from documentation and professional expertise. The literature search highlights the non-existence of a current systematic operational reverse engineering model that could be the standard for designing manufacturing systems. One of the main constraints in the manufacturing is that the time for production is limited and denoted by time available (TA). The time to finish (TF) is the time needed to complete the manufacturing operations in a facility so that the entire quantity demanded is produced, from start to end, in the production line. If the TF is less than the TA there is sufficient capacity to meet the demand. Literature search indicates that no study has been conducted to compute the TF. Further, it also indicates that no study has been carried out focusing on the vi impact of variations and disruptions at the design stage, even though these topics are covered in analysis of existing operational systems. The algorithms and mathematical model were developed. The model will compute the exact TF taking into account variation, disruption and flow issues. The equation for TF was developed. The model to be designed is validated using information from a government manufacturing system

Technology Pathway Partnership Final Scientific Report

This report covers the scientific progress and results made in the development of high efficiency multijunction solar cells and the light concentrating non-imaging optics for the commercial generation of renewable solar energy. During the contract period the efficiency of the multijunction solar cell was raised from 36.5% to 40% in commercially available fully qualified cells. In addition significant strides were made in automating production process for these cells in order to meet the costs required to compete with commercial electricity. Concurrent with the cells effort Boeing also developed a non imaging optical systems to raise the light intensity at the photovoltaic cell to the rage of 800 to 900 suns. Solar module efficiencies greater than 30% were consistently demonstrated. The technology and its manufacturing were maturated to a projected price of < $0.015 per kWh and demonstrated by automated assembly in a robotic factory with a throughput of 2 MWh/yr. The technology was demonstrated in a 100 kW power plant erected at California State University Northridge, CA

Analysis and Evaluation of the Impacts of Predictive Analytics on Production System Performances in the Semiconductor Industry

Problem Statement: Predictive Analytics (PA) may effectively support semiconductor industry (SI) companies in order to manage the special challenges in SI value chains. To discover the implications of PA, the realistic benefits as well as its limitations of its application to semiconductor manufacturing, it is necessary to assess in which ways the application of PA affects the production system (PS) performances. However, based on the literature survey, the influences of PA on the various performance characteristics of an SI PS are not as clear as expected for the efficiently operative application. Besides, the existing performance models are not effective to predict the impacts of PA on the SI PS performances. Therefore, the overall aim of this thesis is to analyse and evaluate the impacts of PA on the SI PS performances and to identify under which conditions a PA application would generate the most significant performance improvements. The focus of this thesis is predictive maintenance (PdM). Research Methodology: Based on a post-positivist philosophy, the thesis applies a deductive research approach using mixed-methods for data collection. The research design has the following stages: (1) theory, (2) hypothesis, (3) state of research, (4) case study and (5) verification. Main Achievements: (1) The systematic literature review is carried out to identify the gaps of the existing research and based on these findings, a conceptual framework is proposed and developed. (2) The existing performance models are analysed and evaluated against their applicability to this study. (3) A causal loop model for SI PS is generated based on the assessment of experts with industrial engineering and equipment maintenance expertise. (4) An expert system is developed and evaluated in order to investigate transitive and contradictory effects of PdM on SI PS performances. (5) A simulation model is developed and validated for investigating the strengths and limitations of PdM regarding SI PS performances under different circumstances. Results: The results of the logical inference study show that PdM has 34 positive effects as well as 4 contradictory effects on SI PS performance characteristics. Based on the various simulation experiments, it has been found that (1) ’Mean Time to Repair’ decreases only if PdM supports proportionate reduction of failures and repair times. (2) Logistics performance improves only if the underlying workcenter is limited in capacity or the four partners are nonsynchronous. (3) PdM supports optimal cost decreases for workcenters where the degree of exhausting wear limits can be most effectively improved and (4) the degree of yield improvement gained by PdM is dependent on the operation scrap rate. However, (5) if a workcenter has overcapacity, PdM will potentially worsen PS performances, even if the particular workcenter performance can be improved. These new insights advance existing knowledge in production managements when adopting predictive technologies at SI PS in order to improve PS performances. The findings above enable SI practitioners to justify a PdM investment and to select suitable workcenters in order to improve SI PS performances by applying the proposed PdM. Contributions: The main contributions of this PhD project can be divided into practical application and theoretical work. The contributions from the theoretical perspective are: 1) The critical review and evaluation of the state of the research for PA in the context of semiconductor manufacturing and the models for predicting and evaluating SI PS performances. 2) A new framework for investigating the implications of PA on the challenges such as gaining high utilizations and controlling the variability in production processes in SI value chains. 3) The new knowledge about transitive and contradictory effects of PdM on SI PS performances, which indicates that PdM can be used to improve PS performances beyond a single machine. 4) The new knowledge about strengths and limitations of PdM in order to improve SI PS performances under particular circumstances. The contributions from the practical application perspective are: 1) A practical method for identifying workcenters where PdM delivers the most significant benefits for SI PS performances. 2) An expert system that provides a comprehensive knowledge base about causes and effects within SI PS in order to justify a PdM investment. 3) A concise review of important PA applications, their capabilities for the wafer fabrication and the most suited PA methods. These findings can be adopted by SI practitioners