Discrete Event Simulations edited by Aitor Goti SCIYO Discrete Event Simulations Edited by Aitor Goti Published by Sciyo Janeza Trdine 9, 51000 Rijeka, Croatia Copyright © 2010 Sciyo All chapters are Open Access articles distributed under the Creative Commons Non Commercial Share Alike Attribution 3.0 license, which permits to copy, distribute, transmit, and adapt the work in any medium, so long as the original work is properly cited. After this work has been published by Sciyo, authors have the right to republish it, in whole or part, in any publication of which they are the author, and to make other personal use of the work. Any republication, referencing or personal use of the work must explicitly identify the original source. Statements and opinions expressed in the chapters are these of the individual contributors and not necessarily those of the editors or publisher. No responsibility is accepted for the accuracy of information contained in the published articles. The publisher assumes no responsibility for any damage or injury to persons or property arising out of the use of any materials, instructions, methods or ideas contained in the book. Publishing Process Manager Ana Nikolic Technical Editor Goran Bajac Cover Designer Martina Sirotic Image Copyright Stavklem, 2010. Used under license from Shutterstock.com First published September 2010 Printed in India A free online edition of this book is available at www.sciyo.com Additional hard copies can be obtained from publication@sciyo.com Discrete Event Simulations, Edited by Aitor Goti p. cm. ISBN 978-953-307-115-2 SCIYO.COM WHERE KNOWLEDGE IS FREE free online editions of Sciyo Books, Journals and Videos can be found at www.sciyo.com Chapter 1 Chapter 2 Chapter 3 Chapter 4 Chapter 5 Chapter 6 Chapter 7 Chapter 8 Chapter 9 Chapter 10 Preface VII Discrete Event Simulation 1 Professor Eduard Babulak and Dr Ming Wang A dynamically configurable discrete event simulation framework for many-core chip multiprocessors 11 Christopher Barnes and Jaehwan John Lee Modelling methods based on discrete algebraic systems 35 Hiroyuki Goto Supply chain design: guidelines from a simulation approach 63 Eleonora Bottani and Roberto Montanari A simulation technology for supply-chain integration 79 Shigeki Umeda Optimisation of reordering points considering purchasing, storing and service breakdown costs 105 Aitor Goti and Miguel Ortega Reverse logistics: end-of-life recovery pledge 115 R.C. Michelini and R.P. Razzoli Simulating service systems 141 Raid Al-Aomar Evaluation of methods for scheduling clinic appointments in surgical service: a statecharts-based simulation study 165 Boris G. Sobolev, PhD, Victor Sanchez, MSc and Lisa Kuramoto, MSc Condition based maintenance optimization of multi-equipment manufacturing systems by combining discrete event simulation and multiobjective evolutionary algorithms 187 Aitor Goti and Alvaro Garcia Contents VI Chapter 11 Chapter 12 Chapter 13 Chapter 14 Chapter 15 Chapter 16 Advanced discrete event simulation methods with application to importance measure estimation in reliability 205 Arne Huseby, Bent Natvig, Jørund Gåsemyr, Kristina Skutlaberg and Stefan Isaksen Agent-based modelling and simulation of network cyber-attacks and cooperative defence mechanisms 223 Igor Kotenko Wireless sensor networks: modeling and simulation 247 Sajjad A. Madani, Jawad Kazmi and Stefan Mahlknecht Discrete event simulation of wireless cellular networks 263 Enrica Zola, Israel Martín-Escalona and Francisco Barceló-Arroyo Discrete-event supervisory control for under-load tap-changing transformers (ULTC): from synthesis to PLC implementation 285 Ali A. Afzalian, S. M. Noorbakhsh and W. M. Wonham Stability analysis of 2-d linear discrete feedback control systems with state delays on the basis of lagrange solutions 311 Guido Izuta This book is an initiative encouraged by Sciyo to promote the Discrete Event Simulation (DES) technique. Considered by many authors as a technique for modelling stochastic, dynamic and discretely evolving systems, this evolution of Monte Carlo stochastic but static technique has gained widespread acceptance among the practitioners who want to represent and improve complex systems. Since DES is a technique applied in incredibly different areas, this book entitled Discrete Event Simulations reects many different points of view about DES, thus, all authors describe how DES is understood and applied within their context of work, providing an extensive understanding of what DES is. It can be said that the name of the book itself, Discrete Event Simulations, reects the plurality that these points of view represented. The manuscript embraces a number of topics covering theory, methods and applications to a wide range of sectors and problem areas that have been categorised into the following ve groups: The rst group presents some of the latest evolutions in the technologies of DES. Thus, it begins with the work by Babulak & Wang, who present the state of the art in the DES technologies. Secondly, the manuscript developed by Barnes & Lee discusses the design and construction of a dynamically congurable DES framework for many-core chip multiprocessors. Third, Goto goes deep into the modelling methods, presenting a choice based on discrete algebraic systems. The second set of chapters introduces elements related to the design and management of supply chains: Bottani & Montanari start this set of chapters by describing some important guidelines for the design of supply chains, and this work is followed by Umeda, who demonstrated the modelling capabilities of a simulation framework proposed. After that, Goti & Ortega introduce a DES based optimizer of reordering points they have developed and applied within the context of a research project. Lastly, Michelini & Razzoli present a software tool for consultation aid for the management of reverse end-of-life logistics. The third group deals with the management of simulation of system services in general. Al-Aomar begins this section by presenting the simulation basics of service systems with application case studies. After that Sobolev, Sanchez & Kuramoto summarise a simulation study based on state charts for the evaluation of methods for scheduling clinic appointments in surgical services. Finally, Goti & Garcia present a maintenance optimisation case where DES and multi-objective evolutionary algorithms are applied. Preface VIII The fourth arrangement analyses issues related to dependability, the dependability being a system property that integrates such attributes as reliability, availability, safety, security, and maintainability. In this area Huseby, Natvig, Gasemyr, Skutlaberg&Isaksen, use the advantage that DES provides in the modelling of multi-component systems for its application to the area of reliability estimation. After that and embracing the whole concept of vulnerability, the work of Kotenko represents the conceptual framework for modelling and simulation, the implementation peculiarities of the simulation environment as well as the experiments aimed on the investigation of distributed network attacks and defence mechanisms. The last series of chapters is closely related to information technologies and electric-electronic hardware and software: within this group, Madani, Kazmi & Mahlknecht present their latest developments in the modelling and simulation of wireless sensor networks by using DES. Zola, Martin-Escalona & Barcelo-Arroyo work in the same direction, but they centre on the simulation of wireless cellular networks, analysing layout and mobility issues, concerns related to the radio channel used, technology dependent restrictions and simulation elements. After that, Afzalian, Noorbakhsh & Wonham describe the procedure they have followed to implement in Programmable-Logic-Controllers or otherwise known PLCs, a discrete-event supervisory control for under-load tap-changing transformers. This series ends by presenting a stability analysis for a class of 2-d feedback control systems, this being a work non-limited to but mainly applied in the world of electronics. As well as the previously explained variety of points of view concerning DES, there is one additional thing to remark about this book: its richness when talking about actual data or actual data based analysis. When most academic areas are lacking application cases, roughly the half part of the chapters included in this book deal with actual problems or at least are based on actual data. Thus, the editor rmly believes that this book will be interesting for both beginners and practitioners in the area of DES. Editor Aitor Goti University of Mondragon – Mondragon Unibertsitatea, Spain Discrete Event Simulation 1 Discrete Event Simulation Professor Eduard Babulak and Dr Ming Wang X Discrete Event Simulation Professor Eduard Babulak and Dr Ming Wang University of the South Pacific, Suva, Fiji and Air Industry babulak@ieee.org, ming604@telus.net Abstract Discrete-event simulation represents modeling, simulating, and analyzing systems utilizing the computational and mathematical techniques, while creating a model construct of a conceptual framework that describes a system. The system is father simulates by performing experiment(s) using computer implementation of the model and analyzed to draw conclusions from output that assist in decision making process. Discrete event simulation technologies have been extensively used by industry and academia to deal with various industrial problems. By late 1990s, the discrete event simulation was in doldrums as global manufacturing industries went through radical changes. The simulation software industry also went through consolidation. The changes have created new problems, challenges and opportunities to the discrete event simulation. This chapter reviews the discrete event simulation technologies; discusses challenges and opportunities presented by both global manufacturing and the knowledge economy. The authors believe that discrete event simulation remains one of the most effective decision support tools but much need to be done in order to address new challenges. To this end, the chapter calls for development of a new generation of discrete event simulation software. Keywords: Discrete and interactive simulations, hybrid manufacturing systems, what-if- analysis, systems modeling. 1. Overview of Discrete Event Simulation Technologies Discrete event simulation quantitatively represents the real world, simulates its dynamics on an event-by-event basis, and generates detailed performance report. It has long become one of the mainstream computer-aided decision-making tools due to availability of powerful computer [1]. Figure 1 illustrates the ways of study a system. Most often system is studied via experiment with actual model, or experiment with a model of actual system. 1 Discrete Event Simulations 2 Fig. 1. Ways to study a system [15]. Figure 2, illustrates the model taxonomy used in the simulation process utilizing either deterministic or stochastic models. Fig. 2. Model Taxonomy [15] The development of the discrete event simulation software has been evolved progressively since 1960s, and many systems have been developed by industry and academia to deal with various industrial problems. In brief, four generation of simulation software products have evolved [2], these being: 1st Generation (late 1960s) - Programming in high level languages (H.L.L) such as FORTRAN. The modeler was obliged to program both the model logic and the code to control the events and activities, or 'simulation engine', in the model. 2nd Generation (late 1970s) - Simulation languages that have commands like event control “engine”, statistical distribution generation, reporting, etc. A model in the simulation language was compiled and then linked with the supplied subroutines to produce an executable model. Examples are GPSS (IBM), See Why (AT&T), AutoMod(ASI). 3rd Generation (early 1980s) - Simulation language generators that are front-end packages that generate the code in a simulation language. The generated code is complied and then linked to produce an executable model. It reduced the model development time, but still required the modeler to master all aspects of the simulation mechanism. Examples are SIMAN (Systems Modeling), EXPRESS (AT&T). 4th Generation (late 1980s) - Interactive simulation packages that enable “what you see is what you get”, allow models to be modified at any time, speed up 'what-if' analysis. The simulation models can be built very quickly by industrial managers and engineers, thus encouraging those people with knowledge and first hand experience of the problem to build the model themselves. The example is WITNESS (AT&T), ARENA (Systems Modeling). By mid 1990s, the virtual reality technology had created a new excitement among the simulation community. A significant amount of effort was made in developing an integrated simulation environment by which engineers can simulate product design and manufacture without going through different simulation packages. The two leading simulation software vendors at that time, Lanner Group and Deneb Inc., announced a plan to jointly develop a new generation of simulation software to support both process and detailed simulation with superior modeling and graphic capabilities. However, the excitement was soon overshadowed by unprecedented changes in manufacturing industries as a result of globalization. The simulation software vendors went through the industrial consolidation. AutoSimulation, System Modeling, Simple++, Deneb, are now part of large corporations. There are new breed of vendors with different business models and using internet for online product sales and support, noticeably Simul8 Inc. Overall, there is no significant development in the discrete event simulation technologies and software since the 4 th generation. On the other hand, tremendous changes in business environment have presented new challenges and opportunities to the discrete event simulation as discussed below. The paper presents in first section the review of discrete event simulation technologies. The second section discusses the applications of discrete event simulations in manufacturing sector and in the third section in education sector. In last two sections four and five, authors discuss future opportunities and conclusions. 2. Applications of Discrete Event Simulation in Manufacturing Sector Discrete event simulation is traditionally used for industrial applications. In the 1980s and 1990s, there had been a rapid development of advanced manufacturing technology in [...]... the discrete event simulation technologies and software since the 4th generation On the other hand, tremendous changes in business environment have presented new challenges and opportunities to the discrete event simulation as discussed below The paper presents in first section the review of discrete event simulation technologies The second section discusses the applications of discrete event simulations. .. and five, authors discuss future opportunities and conclusions 2 Applications of Discrete Event Simulation in Manufacturing Sector Discrete event simulation is traditionally used for industrial applications In the 1980s and 1990s, there had been a rapid development of advanced manufacturing technology in 4 Discrete Event Simulations industrialized countries: CAD (Computer-aided Design), CAM (Computer-aided... higher resource utilization) Many of them have found that discrete event simulation can help them make right decision In the way similar to manufacturing applications, they use the discrete event simulation software to model their business processes and evaluate behavior of the service system under different sets of conditions; 6 Discrete Event Simulations carry out 'what-if' scenario analysis in order... Sciences at University of South Pacific in Suva, Fiji Dr Ming Wang is industry consultant in Vancouver, BC, Canada 10 Discrete Event Simulations A dynamically configurable discrete event simulation framework for many-core chip multiprocessors 11 2 X A dynamically configurable discrete event simulation framework for many-core chip multiprocessors Christopher Barnes and Jaehwan John Lee Indiana University... party component procurement activities”[5] When a company is going through transformation, applications of discrete event simulation are always in doldrums The large scale of “industrial transformation” has led to new Discrete Event Simulation 5 problems to managers and new challenges to discrete event simulation technologies, as described below: 1) Virtual corporation: Global manufacturing and supply... there is a great potential for discrete event simulation technologies in service sector However, new approach and techniques are required to model and simulate knowledge workers and their decisionmaking processes 4 New Opportunities for Discrete Event Simulation The changing business environment and technological developments have created other opportunities for discrete event simulation technologies... process The authors believe that discrete event simulation continue to be one of the most effective decision support tools both in global manufacturing and knowledge economy There are new opportunities for discrete event simulation such as business intelligence systems and simulation-based education At the same time, there is a strong need to develop a new generation of discrete event simulation software... Society for Performance Improvement However, the discrete event simulation software has not taken the findings into account and it remains problematic to model the human performance At present, commercial discrete event simulation software are not able to handle these issues both effectively and efficiently Much more work need to be done to make the discrete event simulation software capable of modeling... is a need for a highly configurable discrete event simulation environment for the study of heterogeneous processor designs Introduced in this chapter is Mhetero, a novel simulation framework that enables users to easily construct and perform discrete event simulations that meet this need Our simulation framework addresses the need for fast as well as configurable simulations by taking advantage of the... synchronism between events in the simulation (Lee & Vincentelli, 1998) Hence, Mhetero's simulation infrastructure can be categorized as a synchronous, discrete time-simulation infrastructure which by definition itself is a discrete event simulation infrastructure (Lee & Vincentelli, 1998) As a result, the framework is not only an interesting and powerful alternative to other discrete event simulators . BC, Canada. Discrete Event Simulations 10 A dynamically congurable discrete event simulation framework for many-core chip multiprocessors 11 A dynamically congurable discrete event simulation. Mondragon – Mondragon Unibertsitatea, Spain Discrete Event Simulation 1 Discrete Event Simulation Professor Eduard Babulak and Dr Ming Wang X Discrete Event Simulation Professor Eduard Babulak. generation of discrete event simulation software. Keywords: Discrete and interactive simulations, hybrid manufacturing systems, what-if- analysis, systems modeling. 1. Overview of Discrete Event