Mark Aufenanger

Simulation data architecture for sustainable development

Reducing costs, improving quality, shortening the time-to-market, and at the same time act and think sustainable are major challenges for manufacturing industries. To strive towards these objectives, discrete event simulation (DES) has proven to be an effective tool for production system decision support.

Design and implementation of an MDA interface for flexible data capturing

The need for high flexibility to react to market changes and customer demand is constantly growing for both short- and long-term success of companies that want to succeed in global markets. The simulation of material flows in modern factories offers these companies the possibility to plan and optimize their floor shops in a fast and cost-efficient way and enables them to react to changes and malfunctions. The most expensive factor in such simulation experiments is the data capturing process from the actual factory. This paper describes the concept and implementation of a generic interface for machine data acquisition into the simulation system d3FACT insight. The interface enables data transfer from a real production system into simulation to initialize and update the simulation model. Within this approach the Devices Profile for Web Services specification will be used. By the use of the developed approach, simulation tools are doing one step further to a possible daily use.

Knowledge-based event control for flow-shops using simulation and rules

The requirements on production systems and their planning and control systems are constantly growing. Systems have to be flexible and provide viable solutions at the same time. Different planning and control approaches, such as optimization, simulation and combination of techniques etc., that attempt to solve the scheduling problems are available. Mathematical solutions which can be found in literature didn¿t solve the real-world problems in an appropriate way. Current knowledge based solutions did not give any value about decision reliability as well as their decision attributes are not differentiate enough. We are developing a new rule based approach by using a combination of simulation and a knowledge generation within a dynamic production planning and -control for flow-shops. Ideas of how knowledge can be trained by simulation are presented. Furthermore which kind of rules and attributes can be used and how decisions about the rule selection can be made are shown.

A real-time rescheduling heuristic using decentralized knowledge-based decisions for flexible flow shops with unrelated parallel machines

In a manufacturing planning and control system, a change of system environment or of the production requirements may invalidate the current production schedule. In that case, rescheduling as a self-adaption function of the system is necessary for generating a new schedule, regarding the current state of the production system. This rescheduling process is time critical and normally requires real time solutions. In this paper we present a rescheduling approach with offline self-learning and online self-decision-making abilities. It solves the rescheduling problem of flexible flow shops (FFS) with unrelated parallel machines. The optimality criterion is the makespan. The approach uses a centralized heuristic to guarantee the generation of active schedules. In addition, it integrates a decentralized knowledge-based decision making system in the heuristic. This decision making system can learn from previous scheduling problems and their schedules. Consequently, it uses the obtained knowledge to dynamically select the most appropriate dispatching rule for scheduling the production, depending on the current system state. Computational results show that the proposed approach is superior to only using one single dispatching rule constantly. Furthermore, due to its efficient runtime the approach is suitable for real time applications.

Adaptive flow control in flexible flow shop production systems: a knowledge-based approach

Today simulation is essential when researching manufacturing processes or designing production systems. But in the field of manufacturing, simulation can not only be used for purposes of research or design, it can also be utilized by flow control systems in order to make better and faster decisions. In this paper we focus on real-time scheduling in a special kind of flexible flow shop systems. These consist of production stages, which represent groups of machines doing the same work, but working at different speeds. Flow control in these flexible flow shop environments with uniform machines is exceedingly complex and it is even more complex when uncertainties are taken into consideration. For this reason we develop an adaptive scheduling heuristic, utilizing both simulation and artificial intelligence in order to make globally good decisions without causing noticeable manufacturing delays.

Planning for mechatronics systems-Architecture, methods and case study

Mechatronic systems are a relatively new class of technical systems. The integration of electro-mechanical systems with hard- and software enables systems that adapt to changing operation conditions and externally defined objective functions. To gain superior system performance from this ability, sophisticated decision making processes are required. Planning is an ideal method to integrate long-term considerations beyond the time horizon of classical controlled systems into the decision making process. Unfortunately, planning employs discrete models, while mechatronic systems or controlled systems in general emphasize the time continuous behavior of processes. As a result, deviations of the actual behavior during the execution from the planned behavior plan cannot be entirely avoided. We introduce a hybrid planning architecture, which combines planning and learning from artificial intelligence with simulation techniques to optimize the general system behavior. The presented approach is able to handle the inevitable deviations during plan execution, and thus maintains feasibility and quality of the created plans.

Simulation Assisted Optimization and Real-Time control Aspects of Flexible Production Systems Subject to Disturbances

Several types of production systems have been studied and researched in the past using either simulation and\or optimization methods. In this paper we describe the design and development of a simulation assisted predictive-reactive system for scheduling and rescheduling a typical flexible production system configuration. Aspects like the combined use of simulation and optimization to solve complex scheduling and rescheduling tasks are described in view of system stability and some of the broader production system elements like buffer sizing and material handling equipment. Results show that combining simulation and optimization for predictive scheduling resulted in better and valid performance measures for a typical example. Results also show that some newly addressed aspects of stability and real-time control can be handled efficiently using a combination of simulation and optimization. Our discussions only bolster the claim that simulation is an indispensable tool in managing complex production systems

Aspects of Agent Based Planning in the Demand Driven Railcab Scenario

Multi-agent Systems are a promising approach to implement flexible and effective decentralized control mechanismen for the logistic domain. This paper intends to introduce the key features of a multi-agent system that is under development for several years. The agents autonomously plan, optimize, and control a railway transportation system. For this purpose the agents interact, communicate and negotiate. This paper presents an overview how different techniques from operations research, artificial intelligence and soft computing are integrated into a multi-agent system to solve this difficult and complex real life problem.

To Automatic Model Abstraction: A Technical Review

Discrete event models in material flow simulation are growing constantly in scope and resolution. Model abstraction is necessary to allow simulation experiments of efficient runtime. Automatic model abstraction is able to make the work of simulation experts easier. Thus only the models with highest complexity have to be created and maintained. In this paper techniques necessary for automatic model abstraction are reviewed. At the beginning, definitions complexity and validity are discussed. Following are methods to measure these model characteristics. And finally methods for abstraction and some practices are presented. Concluding the lack of a unified modeling framework and the lack of quantitative measures of abstraction results is asserted.

Simulation-based adaption of scheduling knowledge

Nowadays, markets are changing frequently and so are the orders that were placed. Therefore, the time from ordering a product until the delivery date becomes shorter and shorter. Furthermore, production systems are subject to different exogenous and endogenous disturbances like machine breakdowns, urgent orders, material failures and so on due to companies acting in a fast and complex world. Currently available scheduling and rescheduling mechanisms are lacking in solution quality or need large calculation times. Therefore, new self-adapting systems that are able to generate good solutions quickly and refine it-self over time are needed. A new approach for a simulation based adaption mechanism for a knowledge based system is presented in this paper. Adaption of the knowledge based and the used classifier is supported by the mechanism. It is shown, that the solution quality increases when using the adaption mechanism instead of the native system without adaption component.