New fuzzy ranking algorithm for discrete event simulation
01 Dec 2005-pp 1-4
TL;DR: A ranking algorithm that can generate all possible system evolutions and shows similar utilization rates and convergence to a comparable triangular distribution and a normal distribution used as benchmarks are presented.
Abstract: Discrete event simulation requires modeling input parameters using probability distributions However, in some cases it may not be possible to obtain a probability distribution for an input parameter because of lack of data Fuzzy set theory may be used in these cases to model the input parameters using fuzzy sets The event list will contain events with fuzzy time sets that usually overlap and the problem becomes how to rank these fuzzy sets and advance the simulation clock In this paper the authors present a ranking algorithm that can generate all possible system evolutions The algorithm was applied to a single server model where the inter-arrival time and the service time were modeled as triangular fuzzy numbers Results obtained were compared with results obtained from a comparable triangular distribution and a normal distribution used as benchmarks Performance parameter used is the utilization percentage of the facility Results show similar utilization rates and convergence
Citations
More filters
TL;DR: A new approach for calculating the event times to increase the accuracy of simulation time estimation in FDES is presented and the proposed FDES approach was implemented in a construction project scheduling example.
Abstract: Discrete event simulation (DES) has been widely used for scheduling and analysis of construction projects. Integration of DES with fuzzy logic enhances the capabilities of DES by capturing imprecise, subjective, and linguistically expressed knowledge in the simulation inputs using fuzzy numbers. However, available fuzzy discrete event simulation (FDES) methodologies have significant shortcomings in handling fuzzy ranking and updating of the simulation time. These limitations often result in the inaccurate estimation of the start and completion times for individual activities and the whole project. This paper presents a new approach for calculating the event times to increase the accuracy of simulation time estimation in FDES. The major contributions of this paper are in integrating DES with fuzzy logic to increase its applicability in the construction domain and increasing the accuracy of FDES results. The proposed FDES approach was implemented in a construction project scheduling example, which c...
18 citations
References
More filters
Book•
01 Jan 1976
TL;DR: In this paper, the authors present a rigorous mathematical foundation for modeling and simulation and provide a comprehensive framework for integrating the various simulation approaches employed in practice, including cellular automata, chaotic systems, hierarchical block diagrams, and Petri nets.
Abstract: From the Publisher:
Although twenty-five years have passed since the first edition of this classical text, the world has seen many advances in modeling and simulation, the need for a widely accepted framework and theoretical foundation is even more necessary today. Methods of modeling and simulation are fragmented across disciplines making it difficult to re-use ideas from other disciplines and work collaboratively in multidisciplinary teams. Model building and simulation has been made easier and faster by riding piggyback on advances in software and hardware. However, difficult and fundamental issues such as model credibility and interoperation have received less attention. These issues are now front and center under the impetus of the High Level Architecture (HLA) standard mandated by the U.S. DoD for all contractors and agencies.
This book concentrates on integrating the continuous and discrete paradigms for modeling and simulation. A second major theme is that of distributed simulation and its potential to support the co-existence of multiple formalisms in multiple model components. Prominent throughout are the fundamental concepts of modular and hierarchical model composition.
This edition presents a rigorous mathematical foundation for modeling and simulation. Also, it now provides a comprehensive framework for integrating the various simulation approaches employed in practice. Including such popular modeling methods as cellular automata, chaotic systems, hierarchical block diagrams, and Petri nets. A unifying concept, called the DEVS Bus, enables models, as expressed in their native formalisms, to be transparently mapped into the Discrete Event System Specification (DEVS). The book shows how to construct computationally efficient, object-oriented simulations of DEVS models on parallel and distributed environments. If you are doing integrative simulations, whether or not they are HLA compliant, this is the only book available to provide the foundation to understand, simplify and successfully accomplish your task.
Herbert Praehofer is an Assistant Professor at the Johannes Kepler University in Linz, Austria. He has over 50 publications in international journals and conference proceedings on Modeling and Computer Simulation, Systems Theory, and Software Engineering.
Tag Gon Kim is a Professor of Electrical Engineering at the Korea Advanced Institutes of Science and Technology (KAIST), Taejon, Korea. His research interests include discrete event systems modeling/simulation, computer/communication systems analysis, and object-oriented simulation engineering. He is a senior member of IEEE and SCS, and a member of ACM.
* Provides a comprehensive framework for continuous and discrete event modeling and simulation
* Explores the mathematical foundation of simulation modeling
* Discusses system morphisms for model abstraction and simplification
* Presents a new approach to discrete event simulation of continuous processes
* Includes parallel and distributed simulation of discrete event models
* Presentation of a concept to achieve simulator interoperability in the form of the DEVS-Bus
* Complete coverage necessary for compliance with High Level Architecture (HLA) standards
Bernard P Zeigler, is a Professor of Electrical & Computer Engineering at the University of Arizona and heads the Artificial Intelligence Simulation Research Group. He is the author of numerous books and publications, and he is the Editor-in-Chief of the Transactions of the Society for Computer Simulation International.
2,569 citations
TL;DR: A method of ranking fuzzy numbers with integral value is proposed, which is independent of the type of membership functions used and the normality of the functions, and can rank more than two fuzzy numbers simultaneously.
1,098 citations
"New fuzzy ranking algorithm for dis..." refers background or methods in this paper
...Classical Simulation Using triangular distributions Inter-arrival time [6,9,12] Service time [5,8,11] 0....
[...]
...Fuzzy Simulation Inter-arrival time [6,9,12] Service time [5,8,11] 0....
[...]
...The Integral Value algorithm is the most exploited technique in fuzzy simulation....
[...]
...The use of fuzzy simulation in studying the effects of uncertainty on estimating working processing time in semi-conductors manufacturing has been applied by [2] in which the authors have developed an algorithm based on the calculation of the Integral Value [5] to rank the occurrence times of the next possible events....
[...]
...The ranking of two or more fuzzy sets does not give a unique result [5, 6]....
[...]
01 Jul 1989
TL;DR: L.A. Zadeh's (1975) possibility theory is used as a general framework for modeling temporal knowledge pervaded with imprecision or uncertainty, and Deductive patterns of reasoning involving fuzzy and/or uncertain temporal knowledge are established.
Abstract: L.A. Zadeh's (1975) possibility theory is used as a general framework for modeling temporal knowledge pervaded with imprecision or uncertainty. Ill-known dates, time intervals with fuzzy boundaries, fuzzy durations, and uncertain precedence relations between events can be dealt with in this approach. An explicit representation (in terms of possibility distributions) of the available information, which may be neither precise nor certain, is maintained. Deductive patterns of reasoning involving fuzzy and/or uncertain temporal knowledge are established, and the combination of fuzzy partial pieces of information is considered. A scheduled example with fuzzy temporal windows is discussed. >
304 citations
07 May 2000
TL;DR: F fuzzy set theory was applied to discrete event simulation to model uncertainty in input data and the results show how the new ranking algorithm can be very useful in a fuzzy simulation environment.
Abstract: In this paper fuzzy set theory was applied to discrete event simulation to model uncertainty in input data. Various approaches to fuzzy simulation have been proposed in the literature, even if many of the problems are still to be solved. The key points are how to manage the simulation event list and how to update the fuzzy simulation clock. These two tasks are mainly based on the ranking algorithm. In the following, the classical algorithms were applied to rank temporal fuzzy sets and the results obtained were compared with the ones obtained by the proposed ranking algorithm. The comparison was performed by analyzing a simple case study in the manufacturing field. The results show how the new ranking algorithm can be very useful in a fuzzy simulation environment.
15 citations
"New fuzzy ranking algorithm for dis..." refers background or methods in this paper
...The ranking result varies according to the crisp parameter γ∈[0, 1 ]....
[...]
...The authors in [ 1 ]claim that all possible system evolutions are reproduced by varying the values of the two parameters in the range [0,1]....
[...]
...Another parametric ranking algorithm is given in [ 1 ] which uses an example to show how it overcomes the shortcomings of the two previous ranking algorithms....
[...]
...The authors in [1]claim that all possible system evolutions are reproduced by varying the values of the two parameters in the range [0, 1 ]....
[...]
...In fuzzy discrete event simulation the selection of the next event that will occur in the simulation run and the updating of the simulation clock are not easy tasks as in the probabilistic case [ 1 ,4]....
[...]
TL;DR: A discrete-event simulation model for performance evaluation of a batch-manufacturing facility previously developed in the laboratory has been extended to treat uncertainties modeled by fuzzy numbers, to demonstrate the advantages of possibilistic production data modeling in a real-world application, i.e., semiconductor manufacturing.
Abstract: In the current literature dealing with job shop scheduling, most of the approaches have developed models based on the assumption that the problem domain does not contain any imprecision. However, this hypothesis is strongly challenged in the implementation phase of such models-imprecision is inherent to production systems involving human intervention. The aim of this paper is to demonstrate the advantages of possibilistic production data modeling in a real-world application, i.e., semiconductor manufacturing. In this work, a discrete-event simulation model (MELISSA) for performance evaluation of a batch-manufacturing facility previously developed in our laboratory has been extended to treat uncertainties modeled by fuzzy numbers. Due to the confidential nature of industrial data, an illustrative example, presenting the same typical features as a real problem, is treated and analyzed using fuzzy concepts. Inclusion of fuzzy techniques provides the decision-maker with a range of possible values for completion times, average storage times, and operator workload instead of a unique value (which has little significance due to the variety of human operators). In addition, the negative portion of average waiting times yields useful information for the manager to detect deficient resources in the production system.
13 citations
"New fuzzy ranking algorithm for dis..." refers methods in this paper
...The use of fuzzy simulation in studying the effects of uncertainty on estimating working processing time in semi-conductors manufacturing has been applied by [2] in which the authors have developed an algorithm based on the calculation of the Integral Value [5] to rank the occurrence times of the next possible events....
[...]