System dynamics simulation and optimization with fuzzy logic

In this research, we broaden the scope of system dynamics formulations by building on a previously proposed approach to bridge fuzzy logic with dynamic modeling. Our methodology illustrates how to formulate fuzzy dynamic variables in a meaningful way. We highlight several modeling challenges, including the selection of a fuzzification and defuzzification method, their implementation in a system dynamics formulations and the validation of the results. We use a physician prescription decision‐making model substructure as an example, and apply the fuzzy rule‐based inference system to determine how a patient is categorized as “low‐risk,” “average‐risk” or “high‐risk.” We emphasize various interpretation challenges and suggest careful selection of the fuzzy operators and defuzzification method, to ensure that the defuzzified values behave reasonably in a dynamic context. Copyright © 2020 System Dynamics Society

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“…Ng et al . () also apply a fuzzy logic approach to design more appealing and intuitive table functions for a market growth SD model.…”

Section: The Relevant Literaturementioning

confidence: 99%

“…The fuzzy logic approach has also been applied to model the reaction and awareness of management in response to arising organizational conflicts (Usenik and Turnšek, 2013). Ng et al (2009) also apply a fuzzy logic approach to design more appealing and intuitive table functions for a market growth SD model.…”

Section: The Relevant Literaturementioning

confidence: 99%

Fuzzy rule‐based inference in system dynamics formulations

Sabounchi

Triantis

Kianmehr

et al. 2019

System Dynamics Review

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“…For instance, Karavezyris et al [97] designed a system for waste management based on fuzzy system dynamics. This kind of task can be carried out using the simulation tools provided by Matlab [98]. Moreover, most system dynamics software (such as Stella [99], iThink 5 , Vensim 6 , and so on) are ready to embed fuzzy systems built with general purpose open source fuzzy modeling tools such as those introduced in Section III.…”

Section: F Fuzzy Systems Software For Solving Specific Problems Of Smentioning

confidence: 99%

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Alcalá‐Fdez

Alonso

2016

IEEE Trans. Fuzzy Syst.

106

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Fuzzy systems have been used widely thanks to their ability to successfully solve a wide range of problems in different application fields. However, their replication and application requires a high level of knowledge and experience. Furthermore, few researchers publish the software and/or source code associated with their proposals, which is a major obstacle to scientific progress in other disciplines and in industry. In recent years, most fuzzy system software has been developed in order to facilitate the use of fuzzy systems. Some software is commercially distributed but most software is available as free and open source software, reducing such obstacles and providing many advantages: quicker detection of errors, innovative applications, faster adoption of fuzzy systems, etc. In this paper, we present an overview of freely available and open source fuzzy systems software in order to provide a well-established framework that helps researchers to find existing proposals easily and to develop well founded future work. To accomplish this, we propose a twolevel taxonomy and we describe the main contributions related to each field. Moreover, we provide a snapshot of the status of the publications in this field according to the ISI Web of Knowledge. Finally, some considerations regarding recent trends and potential research directions are presented.

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“…However, there are also some applications of simulation optimisation using the System Dynamics simulation approach. For instance, Ng [19] reported an approach for integrating simulation and optimisation of System Dynamics models using Matlab R and Simulink R and demonstrated how to combine genetic algorithms, fuzzy logic expert input and System Dynamics modelling for improving decision-making. They applied their approach in the classical market growth model.…”

Section: Introductionmentioning

confidence: 99%

Multiobjective simulation optimisation in software project management

Rodríguez

Ruiz

Riquelme

et al. 2011

Proceedings of the 13th Annual Conference on Genetic and Evolutionary Computation

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Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which considers a single objective (or multiple objectives combined into a single fitness function). This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimations is determined. The generated data is combined with a well-known multiobjective optimisation algorithm, NSGA-II, to find optimal solutions for the output variables. The NSGA-II algorithm was able to quickly converge to a set of optimal solutions composed of multiple and conflicting variables from a medium size software project simulation model. Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managers for decision making. Furthermore, visual representations of such solutions are intuitive and can help project managers in their decision making process.

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System dynamics simulation and optimization with fuzzy logic

Cited by 5 publications

References 6 publications

Fuzzy rule‐based inference in system dynamics formulations

Fuzzy rule‐based inference in system dynamics formulations

A Survey of Fuzzy Systems Software: Taxonomy, Current Research Trends, and Prospects

Multiobjective simulation optimisation in software project management

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