A Bibliography of Genetic Algorithm Business Application Research: 1988–June 1996

Wong, Bo K.; Bodnovich, Tom A.

doi:10.1111/1468-0394.00066

Cited by 14 publications

(5 citation statements)

References 21 publications

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“…As Wong and Bodnovich (1998) and Kaboudan (2000) pointed out, these techniques will be intensively used in a near future to solve different problems in economics and finance. Other novel methods must be applied, new ones must be developed and the already existing must be improved to reach even more accurate predictions.…”

Section: Resultsmentioning

confidence: 99%

Forecasting tourist arrivals to Balearic Islands using genetic programming

Álvarez‐Díaz

Mateu-Sbert²,

Nadal

2009

IJCEE

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Traditionally, univariate time-series models have largely dominated forecasting for international tourism demand. In this paper, the ability of a genetic program (GP) to predict monthly tourist arrivals from UK and Germany to Balearic Islands, Spain is explored. GP has already been employed satisfactorily in different scientific areas, including economics. The technique shows different advantages regarding to other forecasting methods. Firstly, it does not assume a priori a rigid functional form of the model. Secondly, it is more robust and easy-to-use than other non-parametric methods. Finally, it provides explicitly a mathematical equation which allows a simple ad hoc interpretation of the results. Comparing the performance of the proposed technique against other method commonly used in tourism forecasting (no-change model, moving average and ARIMA), the empirical results reveal that GP can be a valuable tool in this field.

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Section: Resultsmentioning

confidence: 99%

Forecasting tourist arrivals to Balearic Islands using genetic programming

Álvarez‐Díaz

Mateu-Sbert²,

Nadal

2009

IJCEE

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“…Recently, we have seen an explosion in the number of seemingly intractable problems in an extremely diverse array of fields to which GAs have been successfully applied (Wong and Bodnovich, 1998). Nowadays, the range of firms actively pursuing business-specific applications of GA is quite broad and the list is ever growing.…”

Section: Ijphm 23mentioning

confidence: 99%

Modeling the effects of physician‐directed promotion using genetic algorithm‐partial least squares

Lim¹,

Kirikoshi²,

Okano³

2008

International Journal of Pharmaceutical and Healthcare Marketing

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Purpose -The aim is to explore the potential of a hybrid genetic algorithm-partial least squares (GA-PLS) modeling approach to understand the important promotional spending variables that influence physicians' prescribing habits and to help leverage managers' insight to plan better spend on their promotional activities. Design/methodology/approach -A GA was used as a variable-selecting tool, and PLS analysis was employed for correlating these variables with the observed variation in the volume of prescriptions. This approach is illustrated using database from a marketing consultant on four major brands in the US antibiotic universe. Findings -Good statistical models were derived that permit simpler and faster computational prediction of the effects of physician-directed promotion. All final models established had r 2 values ranging from 0.835 to 0.922 and cross-validated r 2 (q 2 ) values ranging from 0.791 to 0.911 whereas the mean absolute percentage error (MAPE) values were confined within 5 percent range on averaging all brand models. Further, thorough statistical analyses revealed the usefulness of promotional spending as a variable and the robustness of GA-PLS as a correlation tool. Research limitations/implications -Modeling frame was comprised of only antibiotic category, which may limit its general utility. Practical implications -Managers can become more adept at interpreting the effects of promotion on prescribing behaviors of physicians and are able to build predictive models that would help identify where and how their curious blend of promotional cocktail would yield the highest future returns. Moreover, if the impact of individual promotional spending element can be measured, then this is perhaps a testament to the way the efficacy of interventions to reduce the harmful consequences of pharmaceutical marketing could be validated given a growing number of public beliefs that physician-directed promotion has grown too heavy-handed and is undermining medical professionalism. Originality/value -This area of research has not received much attention in the pharmaceutical marketing literature until recent years, and hopefully this study will stimulate some interest.

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“…As a particular class of evolutionary algorithms (EA), the genetic algorithms (GAs) are used as an essential search algorithm in computing, searching and optimization, owing to their abilities for searching irregular and high‐dimensional solution spaces (Pavan et al , 2004; Unger, 2004; Wong and Bodnovich, 1998). This has been widely used in engineering, technique and economic areas (Setnes and Roubos, 2000; Yang and Honavar, 1998; Walters and Sheble, 1993).…”

Section: Introductionmentioning

confidence: 99%

Supply chain optimization modeling in uncertain environment with prediction mechanism

Chen²

2013

Grey Systems: Theory and Application

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PurposeThe purpose of this paper is to explore new methods to improve supply chain management in uncertain environment, more specifically, to tackle the uncertain demand problem and the inventory optimization problem faced by most supply chain systems.Design/methodology/approachThe paper develops a multi‐objective inventory optimization model, which combines the classic grey prediction GM(1,1) model with the metaheuristic method. The former is applied to achieve the forecasting mechanism in supply chain operations, and the latter is applied to optimize the model solution.FindingsResults show that the grey‐based forecasting mechanism performs better than other prediction methods, such as the double exponential smoothing method used in this paper. The solution of the multi‐objective inventory optimization model is also improved with the integration of grey prediction method. These indicate the importance of a forecasting mechanism in supply chain management.Originality/valueThe paper succeeds in constructing a novel inventory optimization model and in providing a novel supply chain management framework. It shows for the first time that grey prediction method combined with metaheuristic method may be a valid approach to supply chain management under uncertain environment.

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A Bibliography of Genetic Algorithm Business Application Research: 1988–June 1996

Cited by 14 publications

References 21 publications

Forecasting tourist arrivals to Balearic Islands using genetic programming

Forecasting tourist arrivals to Balearic Islands using genetic programming

Modeling the effects of physician‐directed promotion using genetic algorithm‐partial least squares

Supply chain optimization modeling in uncertain environment with prediction mechanism

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