Extending the effectiveness of simulation-based DSS through genetic algorithms

Fazlollahi, Bijan; Vahidov, Rustam

doi:10.1016/s0378-7206(01)00079-9

Cited by 15 publications

(3 citation statements)

References 29 publications

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“…The solution of the scheduling problem in flexible manufacturing systems via genetic algorithms (for example, Fazlollahi and Vahidov 2001, Iyer and Saxena 2004, Yu and Liang 2001) is one of the examples most commonly mentioned. In this article, the scheduling process of maintenance tasks is the core of the analysis process.…”

Section: Generation Evaluation and Selection Of Alternativesmentioning

confidence: 99%

Simulation-Optimization In Geographic Maintenance Scheduling

Otamendi

2007

ECMS 2007 Proceedings Edited By: I. Zelinka, Z. Oplatkova, A. Orsoni

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An application of a combination of scheduling theory, simulation modelling and combinatorial optimization is presented. The system under study deals with human resource and assets management to perform maintenance in large support networks. The paper includes the development of the framework of the software tool that will be embedded into the commercial maintenance package called INGRID. Following the modelling approach, a fully detailed explanation of the data requirements, the decision variables and the output variables is given. A detailed discussion on the genetic algorithm that is going to be used is also provided. The routines have been developed in spreadsheets so that there is an easy link with the rest of the software application.

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Section: Generation Evaluation and Selection Of Alternativesmentioning

confidence: 99%

Simulation-Optimization In Geographic Maintenance Scheduling

Otamendi

2007

ECMS 2007 Proceedings Edited By: I. Zelinka, Z. Oplatkova, A. Orsoni

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“…Genetic algorithms are robust algorithms that can search through large spaces quickly by mimicking the Darwinian "survival of the fittest" law. They can be used to increase the effectiveness of simulation-based DSSs (Fazlollahi and Vahidov, 2001). …”

Section: Other Forms Of Individual Decision Supportmentioning

confidence: 99%

Decision‐Making Models and Decision Support

Lehto

Nah

2006

Handbook of Human Factors and Ergonomics

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“…In 1992, Arinze and Burton (1992) developed a simulation model as the heart of a marketing decision support system (MKDSS) to model the stochastic element of the marketing mix, marketing dynamics, the interactions between marketing instruments and competitive effects, to support decision making process and developing the marketing mix. In 2001, Fazlollani andVahidov (2001) attempted to extend the effectiveness of simulation-based DSS through genetic algorithms. They applied a hybrid method based on the combination of Mont Carlo Simulation and GA to the marketing mix problem to improve the process for searching and evaluating alternatives for decision support.…”

Section: Literature Reviewmentioning

confidence: 99%

Fuzzy expert marketing-mix model

Aly¹,

Vrána²

2005

AGR ECON-CZECH

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A marketing-mix setting model is presented. The all four P's are integrated and identified. The model makes strong utilization of relevant experts' knowledge and expertise through a fuzzy-decision-making system tailored to dynamically set the marketing-mix periodically in response to changing business environment. Quantitative, qualitative, uncertain, and vague variables are handled necessary to provide a realistic solution. The proposed model is particularly applicable to changing business environments that are full of such qualitative, stochastic, uncertain, and vague variables as in the case of agricultural business (e.g., agro-food companies, fertilizers and agro-chemical producers), and generally applicable to any other business sector. The method can be considered as the fuzzy-expert system for determining values of the marketing decision variables.Key words: marketing-mix, fuzzy-decision-making system, stochastic variables, subjective variables, fuzzy expert system Abstrakt: Článek popisuje výkonnou metodu pro nastavení marketingového mixu. Metoda silně využívá relevantní znalost experta a expertízy prostřednictvím fuzzy systému pro podporu rozhodování, který je uzpůsoben tak, aby periodicky dynamicky nastavil marketingový mix v souladu se změnami podnikatelského prostředí. K získání realistického řešení se využívají kvantitativní, kvalitativní, nejisté a neurčité proměnné. Navržená metoda je obzvlášť vhodná pro turbulentní podnikatelské prostředí se spoustou náhodných, neurčitých a nejistých proměnných, což je typické pro agro-potravinářský komplex (od prvovýroby přes zpracovatelský průmysl až po obchodní řetězce, včetně výroby vstupů, např. hnojiv, herbicidů, pesticidů, ap.). Metoda je ale dostatečně obecná pro použití i v jiných sektorech. Metodu lze považovat za fuzzy expertní systém pro určení hodnot marketingových rozhodovacích proměnných.Klíčová slova: marketingový mix, fuzzy systém pro podporu rozhodování, stochastické proměnné, subjektivní proměnné, fuzzy expertní systém

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Extending the effectiveness of simulation-based DSS through genetic algorithms

Cited by 15 publications

References 29 publications

Simulation-Optimization In Geographic Maintenance Scheduling

Simulation-Optimization In Geographic Maintenance Scheduling

Decision‐Making Models and Decision Support

Fuzzy expert marketing-mix model

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