Christoph Laroque

Fast converging, automated experiment runs for material flow simulations using distributed computing and combined metaheuristics

The analysis of production systems using discrete, event-based simulation is wide spread and generally accepted as a decision support technology. It aims either at the comparison of competitive system designs or the identification of a “best possible” parameter configuration of a simulation model. Here, combinatorial techniques of simulation and optimization methods support the user in finding optimal solutions, but typically result in long computation times, which often prohibits a practical application in industry. This paper presents a fast converging procedure as a combination of heuristic approaches, namely Particle Swarm Optimization and Genetic Algorithm, within a material flow simulation to close this gap. Our integrated implementation allows automated, distributed simulation runs for practical, complex production systems. First results show the proof of concept with a reference model and demonstrate the benefits of combinatorial and parallel processing.

Simulation-based planning of maintenance activities by a shifting priority method

Machine failures are major causes of direct downtime as well as system losses (blocked and idle times) in production flows. A previous case study shows that prioritizing bottleneck machines over others has the potential to increase the throughput by about 5%. However, the bottleneck machine in a production system is not static throughout the process of production but shifts from time to time. The approach for this paper is to integrate dynamic maintenance strategies into scheduling of reactive maintenance using Discrete Event Simulation. The aim of the paper is to investigate how a shifting priority strategy could be integrated into the scheduling of reactive maintenance. The approach is applied to and evaluated in an automotive case-study, using simulation for decision support. This shows how to shift prioritization by tracking the momentary bottleneck of the system. The effect of shifting priorities for planning maintenance activities and its specific limitations is discussed.

Simulation-based planning of maintenance activities in the automotive industry

Factories world-wide do not utilize their existing capacity to a satisfactory level. Several studies indicate an average Overall Equipment Efficiency (OEE) of around 55% in manufacturing industry. One major reason is machine downtime leading to substantial system losses culminating in production plans with unsatisfactory robustness. This paper discusses an approach to integrate maintenance strategies into a production planning approach using discrete event simulation. The aim is to investigate how and where in the planning process maintenance strategies can be integrated and how different maintenance strategies influence production performance and the overall robustness of production plans. The approach is exemplified in an automotive case study, integrating strategies for reactive maintenance in a simulation model to support decision making on how repair orders should be prioritized to increase production performance. The results show that introducing priority-based planning of maintenance activities has a potential to increase productivity by approximately 5%.

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.

Simulation aided, knowledge based routing for AGVs in a distribution warehouse

Traditional routing algorithms for real world AGV systems in warehouses compute static paths, which can only be adjusted to a limited degree in the event of unplanned disturbances. In our approach, we aim for a higher reactivity in such events and plan small steps of a path incrementally. The current traffic situation and also up to date time constraints for each AGV can then be considered. We compute each step in real time based on empirical data stored in a knowledge base. It contains information covering a broad temporal horizon of the system to prevent costly decisions that may occur when only considering short term consequences. The knowledge is gathered through machine learning from the results of multiple experiments in a discrete event simulation during preprocessing. We implemented and experimentally evaluated the algorithm in a test scenario and achieve a natural robustness against delays and failures.

Multi-user support and motion planning of humans and humans driven vehicles in interactive 3D material flow simulations

The visualization of simulated production processes is used for their analysis. Huge plants are normally planned by a team. So a solution for many users who are modeling and interacting with a running model in an immersive 3D environment is required. We discuss an approach where several users work cooperatively on one simulation model. To optimize their work, the users need some guidance. For this we suggest small maps and arrows to guide the user to significant objects (machines). In many production scenarios, objects (forklifts, workers) are moving in an unguided fashion. In actual implementations these paths have to be modeled manually. In spite of taking these efforts, we are presenting an automated approach which is based on the 3D layout of the plant. If the user as part of the simulation is standing in the way of the object, the object stops in our approach (as hopefully in reality).

A technical concept for plant engineering by simulation-based and logistic-integrated project management

Customized planning, engineering and build-up of factory plants are very complex tasks, where project management contains lots of risks and uncertainties. Existing simulation techniques could help massively to evaluate these uncertainties and achieve improved and at least more robust plans during project management, but are typically not applied in industry, especially at SMEs (small and medium-sized enterprises). This paper presents some results of the joint research project simject of the Universities of Paderborn and Kassel, which aims at the development of a demonstrator for a simulation-based and logistic-integrated project planning and scheduling. Based on the researched state-of-the-art, requirements and a planning process are derived and described, as well as a draft of the current technical infrastructure of the intended modular prototype. First plug-ins for project simulation and multi-project optimization are implemented and already show possible benefits for the project management process.

Automated 3d-motion planning for ramps and stairs in intra-logistics material flow simulations

Commercial software of material flow simulations has the ability to layout the simulated models. Arranged equipment, such as conveyors or machines, includes the need to model and determine motion paths for moving objects like forklifts or automatically guided vehicles, so that the simulation framework is able to navigate all vehicles across those motion paths. After analyzing first scenarios, the user often carries out layout changes in the simulation model, e.g. moving, adding or deleting equipment. However, those changes cause time consuming, additional modeling of the motion paths for the user. Our motion planning algorithm reduces these changes by automatically determining the motion paths for moving objects, depending on an actual model layout without colliding with other objects. The algorithm works on the basis of the virtual scenes 3D-data used for the simulation models visualization. We demonstrate the technique with a multi-floor building example.

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.