András Pfeiffer

Stability-oriented evaluation of rescheduling strategies, by using simulation

The paper proposes a simulation-based evaluation technique for the testing, validation and benchmarking of rescheduling methods. Dynamic and stochastic scheduling problems with possible scheduling environments are presented. Based on our review of the related literature, frequently applied rescheduling approaches for solving dynamic and stochastic job-shop scheduling problems are analyzed. Schedule evaluation techniques, related measures, and simulation-based schedule evaluation solutions are also introduced and categorized. Certain stability-oriented evaluations of periodic and hybrid rescheduling methods are described for both single- and multi-machine (job-shop) cases. Finally, an industrial application of the proposed method is presented.

Digital enterprise solution for integrated production planning and control

Digital enterprise technologies combined with sophisticated optimization algorithms can significantly contribute to the efficiency of production. The paper introduces a novel approach for integrated production planning and control, with the description of the mathematical models and solution algorithms. The deterministic optimization algorithms are complemented by a discrete-event simulation system to assess solution robustness in case of disturbances. The methods are illustrated by describing two prototype systems and by some experimental results obtained in an industry-initiated project.

Real-Life Scheduling Using Constraint Programming and Simulation

Abstract The paper presents an integrated production planner and job shop scheduler system with flexible modelling capabilities and powerful, scalable solution methods. The system generates close-to-optimal production and capacity plans on the medium term, and detailed production schedules on the short-term. However, the constraint-based, deterministic scheduling model can hardly account for all the uncertainties on the shop floor. Hence, we included such factors into a discrete-event simulation model that is applied to evaluate the robustness of schedules in face of various uncertainties

Simulation as one of the core technologies for digital enterprises: assessment of hybrid rescheduling methods

The current paper tackles the problem of managing the uncertainties during the execution of predictive schedules in a dynamic environment. The dynamic environment in question is represented by a simulation model – connected to a production scheduler system – with flexible modelling capabilities. The paper addresses the simulation module of the proposed architecture highlighting its main functionalities and advantages, compared to former simulation-based solutions. By applying the proposed architecture, which constitutes a coherent part of a digital enterprise approach, the solution methods for stability-oriented rescheduling can be thoroughly tested and analysed. An evaluation of several scenarios of the rescheduling threshold and the timing of rescheduling are also presented in an industrial case-study.

Building agent-based systems in a discrete-event simulation environment

The paper outlines a discrete-event simulation environment for modeling agent-based manufacturing systems. Exploiting the advantages of a general discrete-event simulation package, in the developed system agent-based features are directly included in the simulation environment providing the possibility to build agent-based models inside the simulation. The paper describes the agent-based functionalities of the system by presenting the communication mechanisms and predefined collaboration protocols. The modeling system implements the heterarchical control concept that is based on the contract net protocol.

Robust production planning and control for multi-stage systems with flexible final assembly lines

Production planning of final assembly systems is a challenging task, as the often fluctuating order volumes require flexible solutions. Besides, the calculated plans need to be robust against the process-level disturbances and stochastic nature of some parameters like manual processing times or machine availability. In the paper, a simulation-based optimisation method is proposed that utilises lower level shop floor data to calculate robust production plans for final assembly lines of a flexible, multi-stage production system. In order to minimise the idle times when executing the plans, the capacity control that specifies the proper operator–task assignments is also determined. The analysed multi-stage system is operated with a pull strategy, which means that the production at the final assembly lines generates demands for the preceding stages providing the assembled components. In order to guarantee the feasibility of the plans calculated for the final assembly lines, a decomposition approach is proposed to optimise the production plan of preceding stages. By this way, the robust production can be ensured resulting in reduced losses and overall production costs even though the system is exposed to changes and disturbances.

Decision support solutions for factory and network logistics operations

The paper examines the relationship of decision levels, performance measures and modelling and decision support approaches through the example of two implemented decision support systems for manufacturing and logistics application fields. Aside from highlighting the relevance of decision support for making industrial networks fit for emerging challenges, the relevance of the two presented EU FP7 projects VFF and ADVANCE to the Factories of the Future vision is shown. A discussion of the two projects outlines future research, with particular focus on challenges that arise from integration across levels of the decision hierarchy, within an organisationally heterogeneous network.

Generic data structure and validation methodology for simulation of manufacturing systems

In recent years, the analysis and evaluation of manufacturing systems’ behaviour and their performance became essential. Discrete event simulation (DES) as a digital enterprise technology is an effective tool both in production related decision support processes and in structure or performance analysis of manufacturing systems. Building a DES model of a manufacturing system is a difficult task and requires special competence. Reducing the efforts spent on draft simulation is the aim of an ongoing research which is presented in the paper. The developed framework simplifies and accelerates the process of model building. A production oriented implementation of the ANSI/ISA-95 standard was used by the proposed modelling methodology to define a generic data structure supporting the creation of models without specific knowledge related to the simulation software applied. The data structure enables the development and application of proprietary simulation engines tailored for specific problems. On the other hand, numerous objective tests containing simulation model validation methods are also announced through a well-described example problem. By this method, several validating key performance indicators (KPIs) were tested, and it was indirectly determined whether the simulation model adequately represents the behaviour of the real system. The automatic simulation model generation in our approach enables using different simulation tools and presented through the examples of both commercial and self-developed software.

Applying model-reconstruction by exploring MES and PLC data for simulation support of production systems

The paper introduces a discrete-event simulation-based decision supporting system aiming at automatically mirroring the current state of a large-scale material handling system of a production system in a digital model and supporting the analysis of diverse control settings and rules. The discrete-event digital model is built in an automated way and all the data necessary for the model are taken from a manufacturing execution system (MES) and additionally directly from programmable logic controllers (PLC). Main focus is given to present the results of the PLC program code processing method (code mapping) generating a structured dataset, as well as the model-reconstruction method for the simulation software. The easy-of-use support tool is intended to be used both in planning and operation phases of an automotive manufacturing company, thus the capabilities of model reconstruction and simulation are tested on real-world data.

Automatic simulation model generation supported by data stored in low level controllers

Abstract One of the most widespread techniques to evaluate various aspects of a manufacturing system is discrete-event simulation (DES). However, building a simulation model of a manufacturing system is a difficult task and needs great resource expenditures. Automated data collection and model buildup can drastically reduce the time of the design phase as well as support model reusability. Since most of the manufacturing systems are controlled by low level controllers (e.g., PLCs, CNCs) they store structure and control logic of the system to be modeled by a DES system. The paper introduces an ongoing research of PLC program processing method for automatic simulation model generation of a conveyor system of a leading automotive factory. Results of the validation process and simulation experiments are also described through a case study.