Operations research applications to tourism

Swart, William; Var, Turgut; Gearing, Charles E.

doi:10.1016/0160-7383(78)90321-3

Cited by 21 publications

(7 citation statements)

References 14 publications

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“…Such an index has been developed by Swart et al (1978). Weights are given to a number of different attributes of the destination and an index is then devised.…”

Section: The Gravity Modelmentioning

confidence: 99%

“…The concept and measurement of an attractivity variable depends on the development of an attractivity index. Such an index has been developed by Swart et al (1978). Weights are given to a number of different attributes of the destination and an index is then devised.…”

Section: The Gravity Modelmentioning

confidence: 99%

“…Many taxonomies have been used to classify forecasting methods; (Archer, 1976(Archer, ,1980 Quandt, 1970; Van Doorn, 1982; Baron, 1979; Vanhove, 1980;Swart et al, 1978). This article consolidates the various approaches to forecasting tourism and aggregates them by classifying them into three categories: time series models, econometric causal models (including the gravity model) and expert-opinion methods.…”

Section: Introductionmentioning

confidence: 97%

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Tourism forecasting: A review of empirical research

Sheldon

Var

1985

Journal of Forecasting

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Many publications on tourism forecasting have appeared during the past twenty years. The purpose of this article is to organize and summarize that scattered literature. General conclusions are also drawn from the studies to help those wishing to develop tourism forecasts of their own. The forecasting techniques discussed include time series models, econometric causal models, the gravity model and expert-opinion techniques. The major conclusions are that time series models are the simplest and least costly (and therefore most appropriate for practitioners); the gravity model is best suited to handle international tourism flows (and will be most useful to governments and tourism agencies); and expert-opinion methods are useful when data are unavailable. Further research is needed on the use of economic indicators in tourism forecasting, on the development of attractivity and emissiveness indexes for use in gravity and econometric models and on empirical comparisons among the different methods. KEY WORDSTourism Forecasting Econometric models Gravity models Delphi Time series Tourism is one of the worlds largest industries. The tourism product, however, is perishable in nature, and its demand is vulnerable to many external factors. These two features make the forecasting of tourism flows essential.Three levels of organizations require tourism forecasts-the concerned government agencies, national and regional tourism organizations and the individual suppliers of tourist and travel facilities. This article surveys methods used for forecasting tourism, and suggests the most appropriate forecasting methods for governments, regional tourism organizations and private companies.Many taxonomies have been used to classify forecasting methods; (Archer, 1976(Archer, ,1980 Quandt, 1970; Van Doorn, 1982; Baron, 1979; Vanhove, 1980;Swart et al., 1978). This article consolidates the various approaches to forecasting tourism and aggregates them by classifying them into three categories: time series models, econometric causal models (including the gravity model) and expert-opinion methods. A discussion on each of these methods follows.

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“…Such an index has been developed by Swart et al (1978). Weights are given to a number of different attributes of the destination and an index is then devised.…”

Section: The Gravity Modelmentioning

confidence: 99%

Section: The Gravity Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 97%

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Tourism forecasting: A review of empirical research

Sheldon

Var

1985

Journal of Forecasting

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“…They focus on the impact of alternative cropping plans on economic (cash expenditure, production achievement, profit) and land-oriented (land utilization, productive resource) goals. Similar approaches based on FGP applications in India and Iran can be found in Sharma et al (2007) Since the late 1970s the potential usage of operational research techniques to study tourism problems, like the determination of tourist flows or the evaluation of the impact of tourism activities, has been very well known (Swart et al, 1978). However, applications of GP techniques in this field are quite limited, and they can be grouped in two main areas: tourism planning and sustainable tourism.…”

Section: Applications In Strategic Managementmentioning

confidence: 86%

Multi-criteria decision analysis with goal programming in engineering, management and social sciences: a state-of-the art review

2015

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Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (Manag Sci 2:138-151, 1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences. Forthcoming in Annals of Operations ResearchAbstract Goal programming (GP) is an important class of multi-criteria decision models widely used to analyze and solve applied problems involving conflicting objectives. Originally introduced in the 1950s by Charnes et al. (1955) the popularity and applications of GP has increased immensely due to the mathematical simplicity and modeling elegance. Over the recent decades algorithmic developments and computational improvements have greatly contributed to the diverse applications and several variants of GP models. In this paper we present a state of the art literature review on GP applications in three selected (prominent and popular) areas, namely engineering, management and social sciences.

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“…All causal factors are considered in the aggregate. It isassumed that the net result'of these variables is what has caused whatever trends, seasonalities, or cyclical behavior may exist in the data, and that an extrapolation pf the trend, seasonal, or cyclical pattern will yield an appropriate forecast (Swart, Var and Gearing 1978). Baron (1972, f973) describes the use of a time series analysis program "X-11," developed by Shiskin at U S .…”

Section: Time Seriesmentioning

confidence: 99%