Proposal of an Approach to Automate the Generation of a Transitic System's Observer and Decision Support using MDE

International audienceShort term decision support for manufacturing systems is generally difficult because of the initial data needed by the calculations. Previous works suggest the use of a discrete event observer in order to retrieve these data from a virtual copy of the workshop, as up to date as possible at any time. This proposal offered many perspectives, but suffers from the difficulties to generate a decision support tool combining decision calculations and observation. Meanwhile, interesting developments were made in literature about automatic generation of logic control programs for those same manufacturing systems, especially using the Model Driven Engineering. This paper suggests the use of MDE to generate logic control programs, the observer and the decision support tool at the same time, based on the same data collected by the designer of the system. Thus, the last section presents the evolution needed in the initial data structure, as well as the conception flow suggested to automatize the generatio

Hybrid Job Shop and Parallel Machine Scheduling Problems: Minimization of Total Tardiness Criterion

International audienc

Simulation

Welcome to this graduate course on Discrete-Event Simulation, a hybrid discipline that combines knowledge and techniques from Operations Research (OR) and Computer Science (CS) (Figure 1). Due to the fast and continuous improvements in computer hardware and software, Simulation has become an emergent research area with practical industrial and services applications. Today, most real-world systems are too complex to be modeled and studied by using analytical methods. Instead, numerical methods such as simulation must be employed in order to study the performance of those systems, to gain insight into their internal behavior and to consider alternative (“what-if”) scenarios. Applications of Simulations are widely spread among different knowledge areas, including the performance analysis of computer and telecommunication systems or the optimization of manufacturing and logistics processes. This course introduces concepts and methods for designing, performing and analyzing experiments conducted using a Simulation approach. Among other concepts, this course discusses the proper collection and modeling of input data and system randomness, the generation of random variables to emulate the behavior of the real system, the verification and validation of models, and the analysis of the experimental outputs. FigurePeer ReviewedPostprint (published version

Une approche hybride par simulation et optimisation pour un problème de production

International audienceDans ce papier, nous proposons un modèle conjoint de simulation et d'optimisation d'un système de production de meubles. L'atelier de production est composé de deux unités. Dans la première unité, les produits entrant dans la composition des meubles sont découpés à partir de panneaux standard en MDF (Medium Density Fiberboard), puis dans la deuxième unité, les produits découpés subissent une succession d'opérations de peinture, de ponçage et de laquage. Nous nous intéressons ici au problème complexe combinant le problème de découpe qui consiste à minimiser les chutes lors de la préparation des pièces composant les meubles, et le problème de détermination de la taille des lots de production dans l'atelier de peinture et de laquage des pièces. Nous proposons un modèle conjoint de simulation et d'optimisation des flux de production. A l'aide d'un logiciel commercial de simulation, nous proposons plusieurs préconisations pour une gestion efficace de l'atelier de production

MAXIMIZING THROUGHPUT USING DYNAMIC RESOURCE ALLOCATION AND DISCRETE EVENT SIMULATION

This research studies a serial two stage production system with two flexible servers which can be dynamically assigned to either station. This is modeled using discrete event simulation and more specifically the Arena software package by Rockwell. The goal is to determine dynamic allocation policies based upon the inventory level at each station to maximize the throughput of finished goods out of the system. This model adds to previous work by including actual switching time. The effect of the pre-emptive resume assumption is gauged, and the effectiveness of the OptQuest optimization package is also tested. Studies are conducted to determine the throughput of the system using easily implementable heuristics including when workers are together and separate. Additionally, the effect of buffer allocation and buffer sizing are studied, and it is shown that buffer allocation is not sensitive to changes in buffer ratio as long as there is buffer space available at each station while adding buffer space has a diminishing rate of return

Rapid Control Prototyping for Reconfigurable Assembly Workstations

Department of System Design and Control EngineeringDiverse customer demands and rapid technology change have led to a paradigm shift in the manufacturing industry, from mass production to mass customization, and eventually to personalization. In the past, manufacturers have faced a challenge to produce a large volume of a product at low cost. Today, they should however produce a very small volume of a highly personalized product at mass production cost. In order to meet these challenges, rapid configuration or reconfiguration of manufacturing systems are crucial. Therefore, many studies have discussed reconfigurable manufacturing systems, emphasizing on dynamic scheduling and flexible shop floor logistics. However, little attention has given to the hardware control and the corresponding software development, although they are very important and time-consuming tasks for manufacturing system reconfiguration. Therefore, the main objective of this paper is to quickly design, test, and verify the control software both in a virtual and in a real environment. To do this, we propose a procedure of rapid control prototyping consisting of virtual factory construction, control software development and a final calibration procedure. Rapid control prototyping facilitates engineers to quickly develop control software including communication inputs and outputs, prior to constructing a real shop floor. The proposed simultaneous procedure of manufacturing system design and its control software development will significantly reduce the reconfiguration time of a manufacturing system.clos

On Discrete-Event Simulation and Integration in the Manufacturing System Development Process

DES is seldom used in the manufacturing system development process, instead it is usually used to cure problems in existent systems. This has the effect that the simulation study alone is considered being the cost driver for the analysis of the manufacturing system. It is argued that this is not a entirely correct view since the analysis has to be performed anyway, and the cost directly related to the simulation study is mainly in the model realization phase. It is concluded that it is preferred if the simulation study life cycle coincides with the corresponding manufacturing system's life cycle to increase the usability of the simulation model and to increase efficiency in the simulation study process. A model is supplied to be used for management and engineering process improvements and for improvements of the organizational issues to support simulation activities. By institutionalizing and utilizing well defined processes the conceived complexity related to DES is considered to be reduced over time. Cost is highly correlated to the time consumed in a simulation study. The presented methodology tries to reduce time consumption and lead-time in the simulation study by: (i)~reducing redundant work, (ii)~reducing rework, and (iii)~moving labor intensive activities forward in time. To reduce the time to collect and analyze input data a framework is provided that aims at delivering high granularity input data without dependencies. The input data collection framework is designed to provide data for operation and analysis of the manufacturing system in several domains. To reduce the model realization time two approaches are presented. The first approach supplies a set of modules that enables parameterized models of automated subassembly systems. The second approach builds and runs the simulation model based on a copy of an MRP database, i.e. there is no manual intervention required to build the simulation model. The approach is designed to forecast the performance of an entire enterprise. Since the model is generated from a database, the approach is highly scalable. Furthermore, the maintenance of the simulation model is reduced considerably

Online Simulation in Semiconductor Manufacturing

In semiconductor manufacturing discrete event simulation systems are quite established to support multiple planning decisions. During the recent years, the productivity is increasing by using simulation methods. The motivation for this thesis is to use online simulation not only for planning decisions, but also for a wide range of operational decisions. Therefore an integrated online simulation system for short term forecasting has been developed. The production environment is a mature high mix logic wafer fab. It has been selected because of its vast potential for performance improvement. In this thesis several aspects of online simulation will be addressed: The first aspect is the implementation of an online simulation system in semiconductor manufacturing. The general problem is to achieve a high speed, a high level of detail, and a high forecast accuracy. To resolve these problems, an online simulation system has been created. The simulation model has a high level of detail. It is created automatically from underling fab data. To create such a simulation model from fab data, additional problems related to the underlying data arise. The major parts are the data access, the data integration, and the data quality. These problems have been solved by using an integrated data model with several data extraction, data transformation, and data cleaning steps. The second aspect is related to the accuracy of online simulation. The overall problem is to increase the forecast horizon, increase the level of detail of the forecast and reduce the forecast error. To provide useful forecast results, the simulation model contains a high level of modeling details and a proper initialization. The influences on the forecast quality will be analyzed. The results show that the simulation forecast accuracy achieves good quality to predict future fab performance. The last aspect is to find ways to use simulation forecast results to improve the fab performance. Numerous applications have been identified. For each application a description is available. It contains the requirements of such a forecast, the decision variables, and background information. An application example shows, where a performance problem exists and how online simulation is able to resolve it. To further enhance the real time capability of online simulation, a major part is to investigate new ways to connect the simulation model with the wafer fab. For fab driven simulation, the simulation model and the real wafer fab run concurrently. The wafer fab provides several events to update the simulation during runtime. So the model is always synchronized with the real fab. It becomes possible to start a simulation run in real time. There is no further delay for data extraction, data transformation and model creation. A prototype for a single work center has been implemented to show the feasibility

A framework for the provision of online discrete event simulation for operational decision support in complex manufacturing environments

The engineering body of knowledge contains an array of methodologies and techniques to address the effectiveness and efficiency of operational activities within a manufacturing environment. One such example is simulation modelling, a powerful analytical tool that can potentially be valuable in assisting decision makers, managers and engineers to gauge improvement opportunities and achieve process advancements. However, the cost of ownership for simulation models is not insignificant even for large multinationals, this stems from the requirements for specialist skills in simulation software, model development, data mining and statistical analysis. Simulation projects typically require a large investment to develop and usually are used-once-and-thrown-away. To reuse the model, it would require repeating a large portion of the development cycle. In order for simulation modelling to achieve wider recognition as a decision support tool there is a necessity to reduce the cost of model maintainability, promote reusability, increase flexibility and improve user friendliness. The research proposed framework intends to achieve four goals. i.) Improve and advance the deployment and maintenance requirements of simulation projects in comparison to traditional methods. ii.) Integrate automation into model deployment phase of a simulation projects. Thus, allowing unique user-specified simulation models to be generated by automatically extracting and manipulating data from factory databases. iii.) Enforce a strong documentation technique to achieve interoperability and re-traceability of project progress, therefore permitting programme code or even entire models to be reused and utilised in future projects. iv.) Advance user friendliness and acceptance towards simulation modelling. Reducing the expertise required to conduct simulation studies will improve the programming exercise image associated with typical simulation studies. This framework assists in developing customised simulation modules. These modules facilitate automated online rapid development of reconfigurable, flexible, self-maintaining simulation models, aiming to deliver tailored analysis to support real-time operational decision making

A Hybrid Modelling Framework for Real-time Decision-support for Urgent and Emergency Healthcare

In healthcare, opportunities to use real-time data to support quick and effective decision-making are expanding rapidly, as data increases in volume, velocity and variety. In parallel, the need for short-term decision-support to improve system resilience is increasingly relevant, with the recent COVID-19 crisis underlining the pressure that our healthcare services are under to deliver safe, effective, quality care in the face of rapidly-shifting parameters. A real-time hybrid model (HM) which combines real-time data, predictions, and simulation, has the potential to support short-term decision-making in healthcare. Considering decision-making as a consequence of situation awareness focuses the HM on what information is needed where, when, how, and by whom with a view toward sustained implementation. However the articulation between real-time decision-support tools and a sociotechnical approach to their development and implementation is currently lacking in the literature. Having identified the need for a conceptual framework to support the development of real-time HMs for short-term decision-support, this research proposed and tested the Integrated Hybrid Analytics Framework (IHAF) through an examination of the stages of a Design Science methodology and insights from the literature examining decision-making in dynamic, sociotechnical systems, data analytics, and simulation. Informed by IHAF, a HM was developed using real-time Emergency Department data, time-series forecasting, and discrete-event simulation. The application started with patient questionnaires to support problem definition and to act as a formative evaluation, and was subsequently evaluated using staff interviews. Evaluation of the application found multiple examples where the objectives of people or sub-systems are not aligned, resulting in inefficiencies and other quality problems, which are characteristic of complex adaptive sociotechnical systems. Synthesis of the literature, the formative evaluation, and the final evaluation found significant themes which can act as antecedents or evaluation criteria for future real-time HM studies in sociotechnical systems, in particular in healthcare. The generic utility of IHAF is emphasised for supporting future applications in similar domains