WSC 2008 Final Abstracts

Simulation of the Research Process
Muaz Niazi (Foundation University), Amir Hussain (University of Stirling), Abdul Rauf Baig (FAST-NU) and Saeed Akhtar Bhatti (Foundation University)

Abstract:
This paper presents first steps towards the development of a formal model of the research process. We evaluate the use of simulation as a tool for the evaluation of research strategies in nascent research organizations faced with the absence of significant data. We start by modeling the research process by using the "Publish or Perish" paradigm, a well-known criteria of evaluation of research. We demonstrate the use of this model for researchers to evaluate the effects of selection of a particular publishing venue over time. We then perform various experiments using this basic idea. By means of various visualization techniques, we see how researchers with similar publishing policies might self-organize in the form of groups. We also evaluate the effects of giving higher weights to articles in journals and see where the effects of publishing in these venues breaks even for both top as well as average acceptance rates.

The Improvement of Deformations and Charisteristics of HGA During Clamping using Finite Element Analysis
Thoatsanope Kamnerdtong, Surachate Chutima, and Jukkraphun Parirukvijit (King Mongkut’s University of Technology Thonburi)

Abstract:
This paper presents the study of Head Gimbal Assembly (HGA) clamping for characteristic parameters measure-ment which is an important stage of hard disk drive assembly process. During clamping, HGA is deformed. The values of static attitude and gram load which are significant characteristics of HGA are affected and could exceed acceptable values. Consequently, a lot of HGAs are rejected or these might decrease the performance and the read-write precision of hard disk drive. In this research, finite element analysis is performed to study the behaviors of HGA under various clamping forces and configurations for the process development. Results show that using lower clamping force and having rectangular shape for the upper part and with fillet for the lower part of clamping unit have less deformation than the other configurations of clamping. These make considerable improvement of parameter measurement during HGA clamping process efficiently and effectively.

Randomized Methods for Solving the Winner Determination Problem in Combinatorial Auctions
Joshua Chi-Chun Chan and Dirk Pieter Kroese (University of Queensland)

Abstract:
Combinatorial auctions, where buyers can bid on bundles of items rather than bidding them sequentially, often lead to more economically efficient allocations of financial resources. However, the problem of determining the winners once the bids are submitted, the so-called Winner Determination Problem (WDP), is known to be NP hard. We present two randomized algorithms to solve this combinatorial optimization problem. The first is based on the Cross-Entropy (CE) method, a versatile adaptive algorithm that has been successfully applied to solve various well-known difficult combinatorial optimization problems. The other is a new adaptive simulation approach by Botev and Kroese, which evolved from the CE method and combines the adaptiveness and level-crossing ideas of CE with Markov Chain Monte Carlo techniques. The performance of the proposed algorithms are illustrated by various examples.

Simulation Down Under
Trevor Spedding and Matthew Pepper (University of Wollongong)

Abstract:
This paper provides a brief literature review of the current applications of simulation in Australia. The paper identi-fies areas that use simulation modeling and dynamic tools for growth and improvement, while also highlighting opportunities for future applications of simulation. The simulation applications highlighted in this paper are focused on some of the most significant issues facing Australia today.

Simulation and Experimental Design Applied to Sizing Supermarket Cashiers in Colombia
Jorge Andres Alvarado and Luis Manuel Pulido (Pontificia Universidad Javeriana)

Abstract:
A framework for Colombian supermarkets challenges in order to find an adequate number of cashiers and baggers was developed, translated to a simulation model using Promodel® and proved through an experiment varying register item time, number of cashiers and number of baggers for eight real Colombian supermarkets. The framework proved to be successful in finding a cashier-bagger combination for average waiting and system times, but not powerful enough for service promises involving all of the most part of the clients due to variability of maximum times. An interaction among cashiers and baggers combination was found that makes valuable to increase the number of baggers only for some specific number of cashiers working.

Simulation and Optimization in a Health Center in Medellin, Colombia
Karol Pérez, Laura Cardona, Sebastián Gómez, Tomás Olarte, and Paula Escudero (EAFIT University)

Abstract:
Simulation has been widely applied to health care cases in numerous countries. In Colombia, these applications are scarce. We use a systemic approach, discrete event simulation, simulation-optimization and linear programming to reduce waiting times in a health center in Medellín, Colombia. With this reduction we ensure a high level of satisfaction of patients with a relatively low additional cost.

Modeling and Development of an Arena® Interface for Petri Nets: A Case Study in a Colombian Cosmetics Company
Gonzalo Mejía Delgadillo, Diego Fernando Martínez Rodriguez, and Fidel Torres (Universidad de los Andes)

Abstract:
Petri nets are a tool able to simulate diverse situations. They have been recognized for their modeling power in areas such as medicine, manufacturing, and services. Despite their popularity, their connection with Discrete Event Simulation with Entities (DESE) software has been poor. In the present project, a computational prototype able to simulate Petri Nets under the software Arena® was developed. A real life case study in a cosmetics company was analyzed using classical DESE and Petri Nets. The results were very much encouraging both in model development and running times. Quantitative and qualitative analysis is also discussed.

Multi-resolution Spatial Simulation for Molecular Crowding
Matthias Jeschke and Adelinde M. Uhrmacher (University of Rostock)

Abstract:
Spatial phenomena attract increasingly interest in computational biology. Molecular crowding, i.e. a dense population of macromolecules, is known to have a significant impact on the kinetics of molecules. However, an in-detail inspection of cell behavior in time and space is extremely costly. To balance between cost and accuracy, multi-resolution approaches offer one solution. Particularly, a combination of individual and lattice-population based algorithms promise an adequate treatment of phenomena like macromolecular crowding. In realizing such an approach, central questions are how to specify and synchronize the interaction between population and individual spatial level, and to decide what is best treated at a specific level, respectively. Based on an algorithm which combines the Next Subvolume Method and a simple, individual-based spatial approach, we will present possible answers to these questions, and will discuss first experimental results.

Simulation Optimization with Mathematical Programming Representation of Discrete Event Systems
Andrea Matta (Politecnico di Milano)

Abstract:
Optimization-via-simulation consists in applying iteratively two detached models until an optimality condition is reached: a simulation model for predicting the system performance, and a model for generating potential optimal solutions. Mathematical programming representation has been recently used to describe the behavior of discrete event systems as well as their formal properties. This paper proposes explicit mathematical programming representations for jointly simulating and optimizing discrete event systems. The main advantage of such models is the rapidity of searching for the optimal solution, given to the explicit knowledge of objective function and constraints. Three types of formulations are proposed for solving the buffer allocation problem in flow lines with finite buffer capacities: an exact mixed integer linear model, an approximate LP model and a stochastic programming model. Numerical analysis shows that the computational time required to solve resource allocation problems can be significantly reduced by using the proposed formulations.

Supportive Role of the Simulation in the Process of Ship Engine Crankcase Production Process of Reengineering (Case Study)
Pawel Pawlewski (Poznan University of Technology), Jesus Trujillo (University of Valladolid), Paulina Golinska (Poznan University of Technology), Zbigniew Pasek (University of Windsor) and Marek Fertsch (Poznan University of Technology)

Abstract:
The following paper presents the results of a case study conducted in a company producing engines for ships. The scope of the research enhances the elaboration of the method of reengineering the production process with the support of simulation. Authors present the background of the research including the comparative analysis of five methodologies. On analysis, the conclusion is defined that there is a gap in reengineering methodologies since they do not account for industry-based requirements for simulation. To fulfill this gap the Petri nets application for simulation was proposed. Authors discuss the most distinctive elements of a Petri net and define the methodology of manufacturing processes modeling. The obtained output was not sufficient to make a final decision about the real reengineering process. Therefore, an additional analysis with Rapid RE (Rapid Reengineering) methodology was performed. The proposal of the potential hybrid solution combining the advantages of both methods is presented.

Mexican Public Hospitals: A Model for Improving Emergency Room Waiting Times
Rodolfo Medina and Antonio Vazquez (Universidad Politecnica de Aguascalientes) and Hector A. Juarez and Ricardo A. Gonzalez (Universidad de Guadalajara)

Abstract:
Public health care services are facing a growing demand, in a context where public funds to these services are being stretched. Public Hospitals should find a way to optimize use of resources and improve the quality of services being offered. Even thought this Conference has documented successful experiences with simulation through the years, it has also opened discussion to reach a general, robust model to face emergency department challenges successfully. This paper presents a brief state of the art around the world, a brief review of simulation work done in Mexican Public Health Care System, and finally a proposal to improve these services using simulation.

DE2M: A Solution for Analyzing Supply Chain
María de los Milagros Gutiérrez (Universidad Tecnologia Nacial. Facultad Regional Santa Fe) and Horacio Leone (INGAR - Conicet)

Abstract:
Nowadays, the incremental use of component-based simulation presents a new challenge to overcome. So, the researchers and software developers are putting attention to solve problems as interoperability and reuse of components. In the supply chain context, this simulation paradigm is very valuable because allows us develop independent models and then interconnect them using middleware software such as HLA, CORBA among other. However, the interoperability of these simulation components is a mayor problem to overcome. In this paper, we present an environment to analyze supply chain using this paradigm. We propose to use the SCOR model as roles that the component can play in conjunction with an Ontology base on SCOR. The modules are developed using DEVS formalism and HLA as middleware.

Simulation-Optimization Using a Reinforcement Learning Approach
Carlos D. Paternina-Arboleda (Universidad del Norte), Jairo Montoya-Torres (Universidad de La Sabana) and Aldo Fábregas-Ariza (Univesity of South Florida)

Abstract:
The global optimization of complex systems such as industrial systems often necessitates the use of computer simulation. In this paper, we suggest the use of reinforcement learning (RL) algorithms and artificial neural networks for the optimization of simulation models. Several types of variables are taken into account in order to find global optimum values. After a first evaluation through mathematical functions with known optima, the benefits of our approach are illustrated through the example of an inventory control problem frequently found in manufacturing systems. Single-item and multi-item inventory cases are considered. The efficiency of the proposed procedure is compared against a commercial tool.

Data Farming Around the World Overview
Gary Horne (Naval Postgraduate School) and Klaus-Peter Schwierz (EADS)

Abstract:
Data Farming combines the rapid prototyping capability inherent in certain simulation models with the exploratory power of high performance computing to rapidly generate insight into questions. The Data Farming process focuses on a more complete landscape of possible system re-sponses, rather than attempting to pinpoint an answer. Data Farming allows decision makers to more fully understand the landscape of possibilities and also consider outliers that may be discovered. Over the past decade, an international community has formed around these ideas. In 2008, International Data Farming Workshop 16 took place in Monterey, California, USA and workshop number 17 was held in Garmisch Partenkirchen, Germany. In addition to a summary of these two workshops, this paper will present an overview of the process that has developed to include the development of both methods and applications in the International Data Farming Community.

Data Farming in Singapore: A Brief History
Chwee Seng Choo, Ee Chong Ng, Ching Lian Chua, and Dave Ang (DSO National Laboratories)

Abstract:
This paper gives a summary of Singapore's involvement in Data Farming since 2002, tracing the country's progression from basically "zero" to a key partner in the international Data Farming community. It highlights how the Data Farming methodology, coupled with advanced experiment designs, visualization concepts, and evolutionary algorithms, has helped to extend the analytical capability of the defence community in Singapore when conducting Operational Analysis (OA) studies and experiments. The paper also described some past and current Research and Development (R&D) projects inspired by the ideas shared among this highly interactive and dynamic international community. These include Systematic Data Farming (SDF), Automated Red Teaming (ART), Automated Co-Evolution (ACE), and modelling the effects of human intangibles. Last but not least, the way forward for Singapore in Data Farming is discussed.

Automated Red Teaming: An Objective-Based Data Farming Approach for Red Teaming
Ching Lian Chua (DSO National Laboratories), Wee Chung Sim (Singapore Armed Forces Operations Research Office), Chwee Seng Choo (DSO National Laboratories) and Victor Tay (Defence Science & Technology Agency)

Abstract:
In this paper, we describe an objective-based Data Farming approach for red teaming called Automated Red Teaming (ART). The main idea is to develop an ART framework using Evolutionary Algorithms (EAs), Parallel Computing and Simulation, and apply it to uncover exploitable gaps in military operational concepts, complementing the Manual Red Teaming (MRT) effort. The capability of the ART framework was evaluated vis-à-vis MRT using two maritime security scenarios addressed at the International Data Farming Workshops (IDFWs) 14 and 15. The evaluation showed that, in general, results from ART were better than those obtained from MRT, some of which were non-intuitive and surprising solutions.