Masters Degrees (Industrial Engineering)

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Browsing Masters Degrees (Industrial Engineering) by browse.metadata.advisor "Bekker, J. F."

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    Development of an architecture and web-based demonstrator for tourist itinerary planning
    (Stellenbosch : Stellenbosch University, 2022-11) Swanepoel, Nita; Bekker, J. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: The international Travel and Tourism sector has shown significant growth in recent years and is an exceptionally lucrative industry. It is continuously expanding, improving business processes and incorporating technological advancements that create an abundance of travel opportunities. An area of the industry that has gained more attraction recently is trip planning, as it is arguably one of the most important aspects of a tourist’s journey regardless of their background or income level. With the wide variety of travel websites, online booking platforms and tourist recommendations available on the internet and social media, the task of creating an itinerary can be daunting. Every prospective tourist has different needs and preferences and, therefore, the itinerary planning process requires careful deliberation and comparison of options in order to make suitable and realistic choices. The researcher surmised that there is a need for a personalised itinerary planner which allows the user to specify the budget. To address this need, a research study was conducted in which engineering technologies, information systems, computer programming and web development were combined to develop a new and original tourist itinerary planning (TIP) demonstrator. A literature study revealed that two essential aspects of trip planning, namely personalisation and budgeting have, to some extent, been neglected in previous works. Therefore, the TIP demonstrator presented in this study is focused on providing tourists with an itinerary that satisfactorily fulfils their travel preferences and helps them stay within their desired budget. The TIP demonstrator also offers a unique interactive process of trip planning where the itinerary is constructed with continuous input from the user. This is realised using a TIP architecture, algorithm, database and browser user interface that collectively facilitate the itinerary planning process. After development and implementation, the TIP demonstrator has been tested on different scenarios imitating real-life travel planning behaviours and preferences. The evaluation of the TIP demonstrator showed that it is viable to develop a fully deployable system of this nature and that there exists a business case for it. This study showed, once again, that industrial engineering principles can be applied and integrated systems developed in the services sector.
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    Estimation methods for date palm yield : a feasibility study
    (Stellenbosch : Stellenbosch University, 2021-03) Heyns, Karlien; Bekker, J. F.; Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
    ENGLISH ABSTRACT: With a growing population and a need for food security, crop yield prediction is vital; not only is it used by exporters and importers, but also by the farmer who needs to plan marketing strategies and determine prices. Methods on crop yield prediction are more abundant for annual plants than for perennials. Very few reliable crop yield prediction models have been developed on the date palm, which is grown in arid regions with plentiful water available. Date fruit is a nutritious food which is produced in many countries and consumed widely around the world. Farming with date palms is a complex process with a large variety of factors affecting the annual yield. This study investigated the feasibility of predicting date yield using data collected by a research partner producing date fruit. Data on some farming practices as well as weather conditions was collected from 2010 onwards, at different levels of detail. Machine learning techniques were considered for prediction of yield; however, four applica- ble linear regression techniques were identified and could be used with the available data for feature selection. The dataset has many features, but dominant features were extracted from the data. Some of the feature selection methods used were a correlation technique, stepwise regression and regularisation. These features were further used to develop regres- sion models. It was found that some weather features were important, as well as features describing the date bunch mass. The latter were observed by sampling bunches from trees in different orchards. Linear regression models were developed on orchard level and on farm level, i.e., for the farm as a whole, and the best-performing linear regression models were selected (while avoiding overfitting). The yield predictions following from these models were compared to the actual annual yield recorded, as well as the estimated yield determined by a rather pragmatic yield prediction method devised by the research partner. The selected models produced a 4% prediction error while the farm method gives a 7% error. The proposed models reduced the prediction error and eliminate the need for laborious sampling work done to support the farm prediction model. The study found that certain data that is collected is not needed by the proposed linear regression models. The study was done from an industrial engineering perspective, and a systematic process was followed to critically assess the data available. This was done to keep complexity of the models at a level suitable for reasonable and accurate yield prediction, and to eliminate some unnecessary data collection labour on the farm.