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)
Abstract:
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)
Abstract:
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)
Abstract:
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)
Abstract:
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)
Abstract:
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)
Abstract:
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
Optimization 1
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)
Abstract:
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)
Abstract:
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)
Abstract:
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
Optimization 2
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)
Abstract:
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)
Abstract:
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)
Abstract:
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.