Design, Implementation and Evaluation of Reinforcement Learning for an Adaptive Order Dispatching in Job Shop Manufacturing Systems

Modern production systems tend to have smaller batch sizes, a larger product variety and more complex material flow systems. Since a human oftentimes can no longer act in a sufficient manner as a decision maker under these circumstances, the demand for efficient and adaptive control systems is rising. This paper introduces a methodical approach as well as guideline for the design, implementation and evaluation of Reinforcement Learning (RL) algorithms for an adaptive order dispatching. Thereby, it addresses production engineers willing to apply RL. Moreover, a real-world use case shows the successful application of the method and remarkable results supporting real-time decision-making. These findings comprehensively illustrate and extend the knowledge on RL

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems

AI based state observer for optimal process control: application to digital twins of manufacturing plants

Les plantes de fabricació estan subjectes a restriccions dinàmiques que requereixen una optimització robusta per millorar el rendiment i l' eficiència del sistema. En aquest projecte es presenta un nou sistema de control òptim basat en IA per a un bessó digital d' una planta de fabricació. El sistema proposat implementa un observador d' estat basat en IA per predir l' estat intern d' un model de procés altament incert i no lineal, tal com seria un sistema de producció real. Una funció d' optimització multi-objectiu es utilitzada per controlar els paràmetres de producció i mantenir el procés funcionant en condicions òptimes. El mètode d'Optimització del Control basat en AI es va implementar en un cas d'estudi d'una planta de fabricació d'acer. El rendiment del sistema es va avaluar utilitzant els KPIs de fabricació rellevants, com ara les taxes d'utilització i productivitat de l'equip del procés. L'ús de sistema de control optimitzat via AI millora amb èxit els KPIs de procés i potencialment podria reduir els costos de producció.Las plantas de fabricación están sujetas a restricciones dinámicas que requieren una optimización robusta para mejorar el rendimiento y la eficiencia. En este informe se presenta un nuevo sistema de control óptimo basado en IA para un gemelo digital de una planta de fabricación. El sistema propuesto implementa un observador de estado basado en IA para predecir el estado interno de un modelo de proceso altamente incierto y no lineal, tal y como sería un sistema de producción real. Una función de optimización multiobjetivo es utilizada para controlar los parámetros de producción y mantener el proceso funcionando en condiciones óptimas. El método de Optimización del Control basado en AI se implementó en un caso de estudio de una planta de fabricación de acero. El rendimiento del sistema se evaluó utilizando los KPIs de fabricación relevantes, como la utilización del equipo y las tasas de productividad del proceso. El uso del sistema de control óptimo de IA mejora los KPIs del proceso y podría reducir potencialmente los costos de producción.Manufacturing plants are subject to dynamic constrains requiring robust optimization methods for improved performance and efficiency. A novel AI based optimal control system for a Digital Twin of a manufacturing plant is presented in this report. The proposed system implements an AI based state observer to predict the internal state of a highly uncertain and non-linear process model, such as a real production system. A multi-objective optimization function is used to control production parameters and keeps the process running at an optimal condition. The AI Optimization Control method was implemented on a study case on a steel manufacturing plant. The performance of the system was evaluated using the relevant manufacturing KPIs such as the equipment utilization and productivity rates of the process. The use of the AI optimal control system successfully improves the process KPIs and could potentially reduce production costs

Regionalized implementation strategy of smart automation within assembly systems in China

Produzierende Unternehmen in aufstrebenden Nationen wie China, sind bestrebt, die Produktivität der Produktion durch eine Verbesserung der Lean Produktion mit disruptiven Technologien zu erreichen. Smart Automation ist dabei eine vielversprechende Lösung, allerdings können Unternehmen aufgrund von mangelnden Ressourcen oft nicht alle Smart Automation Technologien gleichzeitig implementieren. Ebenso beeinflusst eine Vielzahl an Einflussfaktoren, wie z.B. Standortfaktoren. Dementsprechend herausfordernd ist die Auswahl und Priorisierung von Smart Automation Technologien in Form von Einführungsstrategien für produzierende Unternehmen. Der Stand der Forschung untersucht nur unzureichend die Analyse der Interdependenzen zwischen Standortfaktoren, Smart Automation Technologien und Key Performance Indikatoren (KPIs). Darüber hinaus mangelt es an einer Methode zur Ableitung der Einführungsstrategie von Smart Automation Technologien unter Berücksichtigung dieser Interdependenzen. Entsprechend trägt diese Arbeit dazu bei, eine regionalisierte Einführungsstrategie von Smart Automation Technologien in Montagesystemen zu ermöglichen. Zunächst werden die Standortfaktoren, Smart Automation Technologien und KPIs identifiziert. In einem zweiten Schritt werden, mit Hilfe von qualitativen und quantitativen Analysen, die Interdependenzen bestimmt. Anschließend werden diese Interdependenzen auf ein Montagesystem mittels hybrider Modellierung und Simulation übertragen. Im vierten Schritt wird eine regionalisierte Einführungsstrategie durch eine Optimierung und eine Monte-Carlo-Simulation abgeleitet. Die Methodik wurde im Rahmen des deutsch-chinesischen Forschungsprojekts I4TP entwickelt, das vom Bundesministerium für Bildung und Forschung (BMBF) unterstützt wird. Die Validierung wurde erfolgreich mit einem produzierenden Unternehmen in Beijing durchgeführt. Die entwickelte Methodik stellt einen neuartigen Ansatz zur Entscheidungsunterstützung bei der Entwicklung einer regionalisierten Einführungsstrategie für Smart Automation Technologien in Montagesystemen dar. Dadurch sind produzierende Unter-nehmen in der Lage, individuelle Einführungsstrategien für disruptive Technologien auf Basis wissenschaftlicher und rationaler Analysen effektiv abzuleiten

Improving just-in-time delivery performance of IoT-enabled flexible manufacturing systems with AGV based material transportation

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Autonomous guided vehicles (AGVs) are driverless material handling systems used for transportation of pallets and line side supply of materials to provide flexibility and agility in shop-floor logistics. Scheduling of shop-floor logistics in such systems is a challenging task due to their complex nature associated with the multiple part types and alternate material transfer routings. This paper presents a decision support system capable of supporting shop-floor decision-making activities during the event of manufacturing disruptions by automatically adjusting both AGV and machine schedules in Flexible Manufacturing Systems (FMSs). The proposed system uses discrete event simulation (DES) models enhanced by the Internet-of-Things (IoT) enabled digital integration and employs a nonlinear mixed integer programming Genetic Algorithm (GA) to find near-optimal production schedules prioritising the just-in-time (JIT) material delivery performance and energy efficiency of the material transportation. The performance of the proposed system is tested on the Integrated Manufacturing and Logistics (IML) demonstrator at WMG, University of Warwick. The results showed that the developed system can find the near-optimal solutions for production schedules subjected to production anomalies in a negligible time, thereby supporting shop-floor decision-making activities effectively and rapidly

Daydreaming factories

Optimisation of factories, a cornerstone of production engineering for the past half century, relies on formulating the challenges with limited degrees of freedom. In this paper, technological advances are reviewed to propose a “daydreaming” framework for factories that use their cognitive capacity for looking into the future or “foresighting”. Assessing and learning from the possible eventualities enable breakthroughs with many degrees of freedom and make daydreaming factories antifragile. In these factories with augmented and reciprocal learning and foresighting processes, revolutionary reactions to external and internal stimuli are unnecessary and industrial co-evolution of people, processes and products will replace industrial revolutions