A modular hybrid simulation framework for complex manufacturing system design

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM–DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning. To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM–DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated

Process software simulation model of Lean-Kanban Approach

Software process simulation is important for reducing errors, helping analysis of the risks and for improving software quality. In recent years, the Lean-Kanban approach has been widely applied in software practice including software development and maintenance. The Lean-Kanban approach minimizes the Work-In-Progress (WIP), which is the number of items that are worked on by the team at any given time. It has been demonstrated that such approach can help to improve software maintenance and development processes in industrial environments. The goal of the simulation model itself is to increase the understanding and to support decisions for planning such kind of projects. Considering the threats to validity of the study, the accuracy and reliability of the simulation model could be shown and the simulation model implementation allows for deriving hypothesis on the impact of distribution on parameters such as throughput. In this thesis, we describe our simulation studies, which show that the Lean-Kanban approach can indeed help to reduce the average time needed to complete maintenance or development issues. This simulation model can simulate existing maintenance and development processes that does not use a WIP limit, as well as a maintenance and development processes that adopt a WIP limit. We performed some case studies using real data collected from different projects. The results confirmthat the WIP-limited process as advocated by the Lean- Kanban approach could be useful to increase the efficiency of software maintenance and development, as reported in previous industrial practices

Process software simulation model of Lean-Kanban Approach

Software process simulation is important for reducing errors, helping analysis of the risks and for improving software quality. In recent years, the Lean-Kanban approach has been widely applied in software practice including software development and maintenance. The Lean-Kanban approach minimizes the Work-In-Progress (WIP), which is the number of items that are worked on by the team at any given time. It has been demonstrated that such approach can help to improve software maintenance and development processes in industrial environments. The goal of the simulation model itself is to increase the understanding and to support decisions for planning such kind of projects. Considering the threats to validity of the study, the accuracy and reliability of the simulation model could be shown and the simulation model implementation allows for deriving hypothesis on the impact of distribution on parameters such as throughput. In this thesis, we describe our simulation studies, which show that the Lean-Kanban approach can indeed help to reduce the average time needed to complete maintenance or development issues. This simulation model can simulate existing maintenance and development processes that does not use a WIP limit, as well as a maintenance and development processes that adopt a WIP limit. We performed some case studies using real data collected from different projects. The results confirmthat the WIP-limited process as advocated by the Lean- Kanban approach could be useful to increase the efficiency of software maintenance and development, as reported in previous industrial practices

A Modeling and Analysis Framework To Support Monitoring, Assessment, and Control of Manufacturing Systems Using Hybrid Models

The manufacturing industry has constantly been challenged to improve productivity, adapt to continuous changes in demand, and reduce cost. The need for a competitive advantage has motivated research for new modeling and control strategies able to support reconfiguration considering the coupling between different aspects of plant floor operations. However, models of manufacturing systems usually capture the process flow and machine capabilities while neglecting the machine dynamics. The disjoint analysis of system-level interactions and machine-level dynamics limits the effectiveness of performance assessment and control strategies. This dissertation addresses the enhancement of productivity and adaptability of manufacturing systems by monitoring and controlling both the behavior of independent machines and their interactions. A novel control framework is introduced to support performance monitoring and decision making using real-time simulation, anomaly detection, and multi-objective optimization. The intellectual merit of this dissertation lies in (1) the development a mathematical framework to create hybrid models of both machines and systems capable of running in real-time, (2) the algorithms to improve anomaly detection and diagnosis using context-sensitive adaptive threshold limits combined with context-specific classification models, and (3) the construction of a simulation-based optimization strategy to support decision making considering the inherent trade-offs between productivity, quality, reliability, and energy usage. The result is a framework that transforms the state-of-the-art of manufacturing by enabling real-time performance monitoring, assessment, and control of plant floor operations. The control strategy aims to improve the productivity and sustainability of manufacturing systems using multi-objective optimization. The outcomes of this dissertation were implemented in an experimental testbed. Results demonstrate the potential to support maintenance actions, productivity analysis, and decision making in manufacturing systems. Furthermore, the proposed framework lays the foundation for a seamless integration of real systems and virtual models. The broader impact of this dissertation is the advancement of manufacturing science that is crucial to support economic growth. The implementation of the framework proposed in this dissertation can result in higher productivity, lower downtime, and energy savings. Although the project focuses on discrete manufacturing with a flow shop configuration, the control framework, modeling strategy, and optimization approach can be translated to job shop configurations or batch processes. Moreover, the algorithms and infrastructure implemented in the testbed at the University of Michigan can be integrated into automation and control products for wide availability.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/147657/1/migsae_1.pd

WIP on a Coordination Language to Automate the Generation of Co-Simulations

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Multi-agent Multi-Model Simulation of Smart Grids in the MS4SG Project

International audienceThis paper illustrates how the multi-agent approach, or paradigm, can help in the modeling and the simulation of smart grids in the context ofMS4SG (a joint project between LORIA-INRIA and EDF R&D). Smart grids simulations need to integrate together pre-existing and heterogeneous models and their simulation software; for example modeling tools of the power grids, of telecommunication networks, and of the information and decision systems. This paper describes the use of MECSYCO as a valid approach to integrate these heterogeneous models in a multi-agent smart grid simulation platform. Several use cases show the ability of MECSYCO to effectively take into account the requirements of smart grids simulation in MS4SG

Co-simulation of cyber-physical systems using a DEVS wrapping strategy in the MECSYCO middleware

International audienceMost modeling and simulation (M&S) questions about cyber-physical systems (CPS) require expert skills belonging to different scientific fields. The challenges are then to integrate each domain's tools (formalism and simulation software) within the rigorous framework of M&S process. To answer this issue, we give the specifications of the MECSYCO co-simulation middle-ware which enables to interconnect several pre-existing and heterogeneous M&S tools, so they can simulate a whole CPS together. The middleware performs the co-simulation in a parallel, decentralized and distributable fashion thanks to its modular multi-agent architecture. In order to rigorously integrate tools which use different formalisms, the co-simulation engine of MECSYCO is based on DEVS. The central idea of MECSYCO is to use a DEVS wrapping strategy to integrate each tool into the middleware. Thus, heterogeneous tools can be homogeneously co-simulated in the form of a DEVS system. By using DEVS, MECSYCO benefits from the numerous scientific works which have demonstrated the integrative power of this formalism and gives crucial guidelines to rigorously design wrappers. We demonstrate that our discrete framework can integrate a vast amount of continuous M&S tools by wrapping the FMI standard. To this end, we take advantage of DEVS efforts of the literature (namely, the DEV&DESS hybrid formalism and QSS solvers) to design DEVS wrappers for FMU components. As a side-effect, this wrapping is not restricted to MECSYCO but can be applied in any DEVS-based platform. We evaluate MECSYCO with the proof of concept of a smart-heating use-case, where we co-simulate non DEVS-centric M&S tools

xDEVS: A toolkit for interoperable modeling and simulation of formal discrete event systems

Employing Modeling and Simulation (M&S) extensively to analyze and develop complex systems is the norm today. The use of robust M&S formalisms and rigorous methodologies is essential to deal with complexity. Among them, the Discrete Event System Specification (DEVS) provides a solid framework for modeling structural, behavior and information aspects of any complex system. This gives several advantages to analyze and design complex systems: completeness, verifiability, extensibility, and maintainability. DEVS formalism has been implemented in many programming languages and executable on multiple platforms. In this paper, we describe the features of an M&S framework called xDEVS that builds upon the prevalent DEVS Application Programming Interface (API) for both modeling and simulation layers, promoting interoperability between the existing platform-specific (C++, Java, Python) DEVS implementations. Additionally, the framework can simulate the same model using sequential, parallel, or distributed architectures. The M&S engine has been reinforced with several strategies to improve performance, as well as tools to perform model analysis and verification. Finally, xDEVS also facilitates systems engineers to apply the vision of model-based systems engineering (MBSE), model-driven engineering (MDE), and model-driven systems engineering (MDSE) paradigms. We highlight the features of the proposed xDEVS framework with multiple examples and case studies illustrating the rigor and diversity of application domains it can support