WSC 2008 Final Abstracts
Tuesday 8:30:00 AM 10:00:00 AM
System Design 4
Chair: Charles McLean (National Institute of Standards and Technology)
A Generic Framework for Real-Time Discrete Event Simulation (DES) Modelling
Siamak Tavakoli (Systems Engineering Reseach Group) and Alireza Mousavi and Alexander Komashie (Systems Engineering Research Group)
This paper suggests a generic simulation platform that can be used for real-time discrete event simulation modeling. The architecture of the proposed system is based on a tested flexible input data architecture developed in Labview, a real-time inter-process communication module between the Labview application and a discrete event simulation software (in this case Arena). Two example applications in the healthcare and manufacturing sectors are provided to demonstrate the ease of adaptability to such physical system.
Application of Fuzzy-MRP2 in Fast Moving Consumer Goods Manufacturing Industry
Jiping Niu (University of Technology, Sydney)
In today’s global marketplace, the information associated with a product is fast becoming a critical link in the supply chain. Especially in fast moving consumer goods industry (FMCG), there is more fierce competition and more unstable requirement. Most plants use Manufacturing Resource Planning (MRP-II) system to manage the process of production. But how to deal with the uncertain and imprecise requirement in the middle term production schedule and make a best plan for best profit is a main problem in this system. In this paper, we investigate the method named Fuzzy-MRP-II to deal with the uncertainty and imprecision. Fuzzy-MRP-II shows all of the information for the decision makers allowing them to consider all possibilities of the orders.
Integrated Dynamic and Simulation Model
on Coupled Closed-Loop Workstation Capacity Controls in a
Multi-Workstation Production System
Tao Wu (The University of Wisconsin-Madison)
In this paper, a dynamic model coupled with a simulation model is introduced to control a multi-workstation production system such that a given performance measure is achieved. In particular, we consider closed loop capacity controls for regulating WIP (Work-in-Process) at individual workstation. The capacity adjustments are consisted of both compensation for local disturbances and predictive control of downstream effects of capacity adjustments make up-stream in the system. Using real data collected from an industrial production system, we are able to demonstrate that our hybrid dynamic and simulation framework can effectively predict lead times associated with each workstation and thus to correctly plan production using a static capacity control system.
Tuesday 10:30:00 AM 12:00:00 PM
Humans and Ergonomics
Chair: Juhani Heilala (VTT, Technical Research Centre of Finland)
Knowledge-Based Event Control for Flow-shops Using Simulation and Rules
Mark Aufenanger, Wilhelm Dangelmaier, Christoph Laroque, and Nando Ruengener (Heinz Nixdorf Institute)
The requirements on production systems and their plan-ning 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.
Embedding Human Scheduling in a Steel Plant Simulation
David Briggs (Corus Long Products)
A simulation was commissioned to understand the interactions that constrain the capacity of a steel plant. The aim was for this to become a reusable tool that could evaluate the effect of future changes to market requirements and operational practices. This paper describes how a simulation model incorporating human decision-making was conceived and constructed. The use of simulation as a tool for knowledge capture in scheduling is considered. The resulting tool has been in use for four years and has acted as a driver to reconsider where the real processing bottlenecks are and what part scheduling can play in managing them.
Linking Ergonomics Simulation to Production Process Development
Salla Lind, Boris Krassi, Juhani Viitaniemi, Sauli Kiviranta, and Juhani Heilala (VTT Technical Research Centre of Finland) and Cecilia Berlin (Chalmers University of Technology)
Production development can conflict with production ergonomics and management of environmental impacts. In this paper, we describe how ergonomics can be assessed in production system design by means of a joint simulation tool – SIMTER. The tool enables ergonomics and environmental impacts assessment in conjunction with production process development. The ergonomics sub-tool is based on digital human model OSKU, which has been improved by introducing an updated data measurement system and neural network processing and inference functionality. The results will extend the new simulation modeling capabilities of the existing digital human model by increasing the motion prediction accuracy and providing freedom to model a multitude of task-related motions in a realistic way.
Tuesday 1:30:00 PM 3:00:00 PM
Chair: Marcus Andersson (University of Skövde)
Optimizing Inspection Strategies for Multi-stage Manufacturing Processes Using Simulation Optimization
Vahid Sarhangian and Abolfazl Vaghefi (Iran University of Science and Technology), Hamidreza Eskandari (Tarbiat Modares University) and Mostafa Kamali Ardakani (The Catholic University of America)
This paper deals with the problem of determining the optimal inspection strategy for a multi-stage production process using simulation optimization. An optimal inspection strategy is the one that results in the lowest total inspection cost, while still assuring a required output quality. Because of the complexity of the problem, simulation is used to model the multi-stage process subject to inspection and to calculate the resulting inspection costs. Simulation optimization is then used to find the optimal inspection strategy. The performance of the proposed method is presented through the use of a numerical example.
Practical Approach to Experimentation in a Simulation Study
Benny Tjahjono and Raul Fernandez (Cranfield University)
Simulation study of complex production facilities can be a challenging task for manufacturing engineers as it requires skills to build the models and to conduct experiments. Accurate modeling but inadequate experimentation may lead to poor decision and can be detrimental particularly when financial investment is involved. This paper proposes a practical approach to simulation experimentation in the context of simulation study of an engine assembly line. The overall aim of the study was to increase the productivity and efficiency of the line. The approach was deployed in the form of a methodology that was used to select the most feasible outcome from a series of simulation experiments, taking into account the minimum effort/investment needed to implement the improvement.
A Simulation-Based Optimization Algorithm for Slack Reducton and Workforce Scheduling
Daniel Noack and Oliver Rose (Dresden University of Technology)
In an assembly line with high labor proportion, the work-force planning and scheduling is a very complex problem. At the background of increasing labor costs, it is very important to increase workforce efficiency. It is essential for companies to remain competitive on global markets. Increasing efficiency is our motivation to work on simulation-based workforce scheduling for complex assembly lines. In this paper, we will focus on the heuristic algorithm in our simulation-based optimization approach. The objective is workforce quantity and slack reduction. To improve the objective, an algorithm assigns the number of workers for activities, scheduled in the simulation run. We will present three different strategies implemented in these optimization algorithms. They basically use the performance indicator slack time, work center utilization and a mix of both parameters. We will compare the algorithms according to their achieved objective and the required computation time.
Tuesday 3:30:00 PM 5:00:00 PM
Chair: Oliver Rose (Dresden University of Technology)
Simulation Optimization Applied to Injection Molding
Maria Guadalupe Villarreal, Rachmat Mulyana, and Jose M Castro (The Ohio State University) and Mauricio Cabrera-Rios (University of Puerto Rico-Mayagüez)
In this work, a simulation optimization method developed by Villarreal and Cabrera-Rios (2007) is applied to injection molding. The method uses design of experiments and adaptive metamodeling techniques. The application of the method to several global optimization test functions as well as non linear polynomial and non polynomial functions point towards a quick convergence to highly attractive solutions with a low number of simulations. Here the method is used to select the best processing conditions for injection molding a simple rectangular plaque and a real automotive part using different performance criteria.
Simulation Optimization for Industrial Scheduling Using Hybrid Genetic Representation
Marcus Andersson, Amos Ng, and Henrik Grimm (University of Skövde)
Simulation modeling has the capability to represent complex real-world systems in details and therefore it is suitable to develop simulation models for generating detailed operation plans to control the shop floor. In the literature, there are two major approaches for tackling the simulation-based scheduling problems, namely dispatching rules and using meta-heuristic search algorithms. The purpose of this paper is to illustrate that there are advantages when these two approaches are combined. More precisely, this paper introduces a novel hybrid genetic representation as a combination of both a partially completed schedule (direct) and the optimal dispatching rules (indirect), for setting the schedules for some critical stages (e.g. bottlenecks) and other non-critical stages respectively. When applied to an industrial case study, this hybrid method has been found to outperform the two common approaches, in terms of finding reasonably good solutions within a shorter time period for most of the complex scheduling scenarios.
Aggregated 3D-Visualization of a Distributed Simulation Experiment of a Queuing System
Wilhelm Dangelmaier, Matthias Fischer, Daniel Huber, Christoph Laroque, and Tim Süß (University of Paderborn)
The paper describes an approach for an aggregated animation of a simulation experiment in an interactive 3D environment, visualizing multiple, distributed simulation runs. Although the general approach of a 3-dimensional visualization of material flow simulation helps to understand the dynamic behavior of a system better as well as faster, it remains unclear, how typical the animated simulation represents the model, if there is a stochastic influence for even some parameters. By the integrated visualization of multiple distributed simulation runs, this uncertainty can be solved, which will be shown in this paper for a typical simulation study of a queuing system.