Jenny P. Holloway, H. Ittmann, Nontombeko Dudeni-Tlhone, P. Schmitz
Elections draw enormous interest worldwide, especially if these involve major countries, and there is much speculation in the media as to possible outcomes from these elections. In many of these recent elections, such as the UK and USA, however, forecasts from market surveys, electoral polls, scientific forecasting models and even exit polls, obtained from voters as they leave the voting stations, failed to predict the correct outcome. Election night forecasts, which endeavour to forecast the ultimate result before the final outcome is known using early results, were also carried out, with some more accurate than others.After successfully predicting most of the metropolitan region results correctly in the South African local 2016 municipal elections, using an election night forecasting model developed for South Africa (SA), the question of adapting the model to work outside of SA on a different electoral system was raised. The focus of this paper is to describe the results obtained for the 2016 USA presidential election, on election night, using an adapted version of the SA model. This paper also addresses the applicability of the model assumptions as well as the data issues involved in forecasting outside of South Africa. It is shown that even with many hurdles experienced in the process the model performed relatively well.
{"title":"From SA to the USA: Election forecasting","authors":"Jenny P. Holloway, H. Ittmann, Nontombeko Dudeni-Tlhone, P. Schmitz","doi":"10.5784/34-2-581","DOIUrl":"https://doi.org/10.5784/34-2-581","url":null,"abstract":"Elections draw enormous interest worldwide, especially if these involve major countries, and there is much speculation in the media as to possible outcomes from these elections. In many of these recent elections, such as the UK and USA, however, forecasts from market surveys, electoral polls, scientific forecasting models and even exit polls, obtained from voters as they leave the voting stations, failed to predict the correct outcome. Election night forecasts, which endeavour to forecast the ultimate result before the final outcome is known using early results, were also carried out, with some more accurate than others.After successfully predicting most of the metropolitan region results correctly in the South African local 2016 municipal elections, using an election night forecasting model developed for South Africa (SA), the question of adapting the model to work outside of SA on a different electoral system was raised. The focus of this paper is to describe the results obtained for the 2016 USA presidential election, on election night, using an adapted version of the SA model. This paper also addresses the applicability of the model assumptions as well as the data issues involved in forecasting outside of South Africa. It is shown that even with many hurdles experienced in the process the model performed relatively well.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88546737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The availability of irrigation water greatly impacts on the profitability of the agricultural sector in South Africa and is largely determined by prudent decisions related to water release strategies at open-air irrigation reservoirs. The selection of such release strategies is difficult, since the objectives that should be pursued are not generally agreed upon and unpredictable weather patterns cause reservoir in flows to vary substantially between hydrological years. In this paper, a decision support system is proposed for the selection of suitable water release strategies. The system is based on a mathematical model which generates a probability distribution of the reservoir volume at the end of a hydrological year based on historical reservoir in flows. A release strategy is then computed which centres the expected hydrological year-end reservoir volume on some user-specified target value subject to user-specified weight factors representing demand satisfaction importance during the various decision periods of the hydrological year. The probability of water shortage for a given year-end transition volume may be determined by the decision support system, which allows for the computation of acceptable trade-off decisions between the fullment of current demand and the future repeatability of a release strategy. The system is implemented as a computerised concept demonstrator which is validated in a special case study involving Keerom Dam, an open-air reservoir in the Nuy agricultural district near Worcester in the South African Western Cape. The system's strategy suggestions are compared to historically employed strategies and the suggested strategies are found to fare better in maintaining reservoir storage levels whilst still fulfillling irrigation demands. Keywords: Decision support, reservoir releases
{"title":"Decision support for open-air irrigation reservoir control","authors":"J. D. van der Walt, Jhj van Vuuren","doi":"10.5784/34-1-563","DOIUrl":"https://doi.org/10.5784/34-1-563","url":null,"abstract":"The availability of irrigation water greatly impacts on the profitability of the agricultural sector in South Africa and is largely determined by prudent decisions related to water release strategies at open-air irrigation reservoirs. The selection of such release strategies is difficult, since the objectives that should be pursued are not generally agreed upon and unpredictable weather patterns cause reservoir in flows to vary substantially between hydrological years. In this paper, a decision support system is proposed for the selection of suitable water release strategies. The system is based on a mathematical model which generates a probability distribution of the reservoir volume at the end of a hydrological year based on historical reservoir in flows. A release strategy is then computed which centres the expected hydrological year-end reservoir volume on some user-specified target value subject to user-specified weight factors representing demand satisfaction importance during the various decision periods of the hydrological year. The probability of water shortage for a given year-end transition volume may be determined by the decision support system, which allows for the computation of acceptable trade-off decisions between the fullment of current demand and the future repeatability of a release strategy. The system is implemented as a computerised concept demonstrator which is validated in a special case study involving Keerom Dam, an open-air reservoir in the Nuy agricultural district near Worcester in the South African Western Cape. The system's strategy suggestions are compared to historically employed strategies and the suggested strategies are found to fare better in maintaining reservoir storage levels whilst still fulfillling irrigation demands. Keywords: Decision support, reservoir releases","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"319 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80193245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Sasol Coal Value Chain is a complex system consisting of blending, stacking and reclaiming of no fewer than six different coal sources with vastly different coal qualities. The amount and quality of the gas produced from coal depend crucially on the quality of the coal reclaimed from the coal stacking yards. In this paper the development of a real time coal quality simulation model using information from an online X-Ray Fluorescence analyser, integrated with various data sources from the Coal Supply Facility, is presented. The integration of different data sources is discussed to create a centralised and standardised data framework for input to the simulation model. The simulation of a heap profile of the coal quality for each heap stacked, together with the quality of the reclaimed coal, is discussed in detail. It is shown how the generated information from the model is utilised in the development of a reclaiming strategy.
{"title":"Simulation of a coal stacking process using an online X-Ray Fluorescence analyser","authors":"R. Rossouw, R. Coetzer, N. J. Roux","doi":"10.5784/34-1-575","DOIUrl":"https://doi.org/10.5784/34-1-575","url":null,"abstract":"The Sasol Coal Value Chain is a complex system consisting of blending, stacking and reclaiming of no fewer than six different coal sources with vastly different coal qualities. The amount and quality of the gas produced from coal depend crucially on the quality of the coal reclaimed from the coal stacking yards. In this paper the development of a real time coal quality simulation model using information from an online X-Ray Fluorescence analyser, integrated with various data sources from the Coal Supply Facility, is presented. The integration of different data sources is discussed to create a centralised and standardised data framework for input to the simulation model. The simulation of a heap profile of the coal quality for each heap stacked, together with the quality of the reclaimed coal, is discussed in detail. It is shown how the generated information from the model is utilised in the development of a reclaiming strategy.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"44 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79953316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The fast changing nature of the educational environment and the subsequent increase in the volumes of generated learner data, have found existing data analysis techniques lacking in certain fields. These techniques form part of the analysis and reporting phases of learning analytics and need to adapt to accommodate the changing face of education. In this paper, a set of interrelated algorithmic solutions that utilise mathematical programming models to generate and provide learning feedback in the form of academic performance status reports, is presented. Three existing mathematical models, more specifically the benchMark program, an outputs-only data envelopment analysis and a traditional analytic hierarchy process were evaluated for providing the information required to assist students in improving their academic achievement. The requirements include providing students with their current academic performance status, setting interim improvement goals and calculating improvement targets towards reaching those goals. The evaluated models did not address the requirements satisfactorily. The solution proposed in this paper consists of an algorithm that implements a linear programming model to generate performance status reports based on the current assessment scores of a group of students in a module. The output is used in a second algorithm that utilises the remaining improvement opportunities available to generate a participation future time perspective. The resulting schedule together with each individual student's current assessment scores, is used to calculate discrete improvement goals for each student as well as targets towards reaching those goals. A third algorithm provides a lecturer with some insight into the mastering of module content. Keywords: Analytic hierarchy process, data envelopment analysis, educational feedback, learning analytics, linear programming, non-linear programming
{"title":"Mathematical modelling for academic performance status reports in learning analytics","authors":"A. V. D. Merwe, H. Kruger, J. Toit","doi":"10.5784/34-1-582","DOIUrl":"https://doi.org/10.5784/34-1-582","url":null,"abstract":"The fast changing nature of the educational environment and the subsequent increase in the volumes of generated learner data, have found existing data analysis techniques lacking in certain fields. These techniques form part of the analysis and reporting phases of learning analytics and need to adapt to accommodate the changing face of education. In this paper, a set of interrelated algorithmic solutions that utilise mathematical programming models to generate and provide learning feedback in the form of academic performance status reports, is presented. Three existing mathematical models, more specifically the benchMark program, an outputs-only data envelopment analysis and a traditional analytic hierarchy process were evaluated for providing the information required to assist students in improving their academic achievement. The requirements include providing students with their current academic performance status, setting interim improvement goals and calculating improvement targets towards reaching those goals. The evaluated models did not address the requirements satisfactorily. The solution proposed in this paper consists of an algorithm that implements a linear programming model to generate performance status reports based on the current assessment scores of a group of students in a module. The output is used in a second algorithm that utilises the remaining improvement opportunities available to generate a participation future time perspective. The resulting schedule together with each individual student's current assessment scores, is used to calculate discrete improvement goals for each student as well as targets towards reaching those goals. A third algorithm provides a lecturer with some insight into the mastering of module content. Keywords: Analytic hierarchy process, data envelopment analysis, educational feedback, learning analytics, linear programming, non-linear programming","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78350224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial to Volume 33(2)","authors":"S. E. Visagie","doi":"10.5784/33-2-583","DOIUrl":"https://doi.org/10.5784/33-2-583","url":null,"abstract":"","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"193 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-12-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74781135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
V. Yadavalli, K. Jeganathan, T. Venkatesan, S. Padmasekaran, S. J. Kingsly
A continuous review (s; S) inventory system at a service facility with nite homogeneous sources of demands and retrial is analysed. The lifetime of each item is assumed to be exponential. Before items are delivered to the customers, some basic service on the item must be performed. It is known as a regular or main service. The service may get interrupted according to a Poisson process and it restarts after an exponentially distributed time. If the server is idle at the time of arrival of a customer and the inventory level is positive, then the service begins immediately. After the completion of regular service, a customer may either abandon the system forever or demand for a second service from the same server, which is multi-optional. If any arriving customer nds that the server is busy or inventory level is zero, he/she either enters into the orbit with probability p or balks (does not enter) with probability 1 - p. The stationary distribution of the number of customers in the system, server status and the inventory level is obtained by the matrix method. The Laplace-Stieltjes transform of the waiting time of the tagged customer is derived. Various system performance measures are derived and the total expected cost rate is computed under a suitable cost structure. A numerical illustration is given. Key words : (s; S) policy, service interruption, nite source, retrial, repair, essential and optional service.
{"title":"A retrial queueing-inventory system with J-additional options for service and nite source","authors":"V. Yadavalli, K. Jeganathan, T. Venkatesan, S. Padmasekaran, S. J. Kingsly","doi":"10.5784/33-2-566","DOIUrl":"https://doi.org/10.5784/33-2-566","url":null,"abstract":"A continuous review (s; S) inventory system at a service facility with nite homogeneous sources of demands and retrial is analysed. The lifetime of each item is assumed to be exponential. Before items are delivered to the customers, some basic service on the item must be performed. It is known as a regular or main service. The service may get interrupted according to a Poisson process and it restarts after an exponentially distributed time. If the server is idle at the time of arrival of a customer and the inventory level is positive, then the service begins immediately. After the completion of regular service, a customer may either abandon the system forever or demand for a second service from the same server, which is multi-optional. If any arriving customer nds that the server is busy or inventory level is zero, he/she either enters into the orbit with probability p or balks (does not enter) with probability 1 - p. The stationary distribution of the number of customers in the system, server status and the inventory level is obtained by the matrix method. The Laplace-Stieltjes transform of the waiting time of the tagged customer is derived. Various system performance measures are derived and the total expected cost rate is computed under a suitable cost structure. A numerical illustration is given. Key words : (s; S) policy, service interruption, nite source, retrial, repair, essential and optional service.","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"4 1","pages":"105-135"},"PeriodicalIF":0.0,"publicationDate":"2017-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79997209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is important that rebases are available on standby at strategic locations in a nature conservation area from where wildre ignition points can be reached rapidly and such res brought under control before they spread. Two facility location models are proposed in this paper which may form the basis for decision support when deciding on the locations of such rebases in a nature conservation area. Both of these models are multi-objective in nature. They are able to produce solutions that embody trade-o decisions between minimising the cost of locating rebases and maximising the coverage of key areas in a conservation area. These trade-os may be based on a variety of coverage importance criteria, such as aiming to cover terrain portions exhibiting a steep ground slope, terrain portions that experience a high annual mean wind speed, or terrain portions in which many wildres have ignited in the past. The coverage criteria are typically case-specic and may therefore be specied by the decision maker. Both models, as well as their approximate solution methodology, are implemented in the form of a computerised decision support system in order to render them accessible to non-mathematically inclined decision makers. The decision support system is validated by applying it to a special case study involving Table Mountain National Park, a nature conservation area in the Western Cape, South Africa. Key words: Firebase location, nature conservation area, coverage criteria
{"title":"A decision support system for rebase location in a nature conservation area","authors":"R. Reed, Jh van Vuuren","doi":"10.5784/33-2-562","DOIUrl":"https://doi.org/10.5784/33-2-562","url":null,"abstract":"It is important that rebases are available on standby at strategic locations in a nature conservation area from where wildre ignition points can be reached rapidly and such res brought under control before they spread. Two facility location models are proposed in this paper which may form the basis for decision support when deciding on the locations of such rebases in a nature conservation area. Both of these models are multi-objective in nature. They are able to produce solutions that embody trade-o decisions between minimising the cost of locating rebases and maximising the coverage of key areas in a conservation area. These trade-os may be based on a variety of coverage importance criteria, such as aiming to cover terrain portions exhibiting a steep ground slope, terrain portions that experience a high annual mean wind speed, or terrain portions in which many wildres have ignited in the past. The coverage criteria are typically case-specic and may therefore be specied by the decision maker. Both models, as well as their approximate solution methodology, are implemented in the form of a computerised decision support system in order to render them accessible to non-mathematically inclined decision makers. The decision support system is validated by applying it to a special case study involving Table Mountain National Park, a nature conservation area in the Western Cape, South Africa. Key words: Firebase location, nature conservation area, coverage criteria","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"1 1","pages":"57-84"},"PeriodicalIF":0.0,"publicationDate":"2017-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83676597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Segmentation (or partitioning) of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main streams of segmentation and examples exist where the application of these techniques improved the performance of predictive models. Both these streams focus, however, on a single aspect (i.e. either target separation or independent variable distribution) and combining them may deliver better results in some instances. In this paper a semi-supervised segmentation algorithm is presented, which is based on k-means clustering and which applies information value for the purpose of informing the segmentation process. Simulated data are used to identify a few key characteristics that may cause one segmentation technique to outperform another. In the empirical study the newly proposed semi-supervised segmentation algorithm outperforms both an unsupervised and a supervised segmentation technique, when compared by using the Gini coecient as performance measure of the resulting predictive models. Key words : Banking, clustering, multivariate statistics, data mining
{"title":"A semi-supervised segmentation algorithm as applied to k-means using information value","authors":"D. G. Breed, T. Verster, S. Terblanche","doi":"10.5784/33-2-568","DOIUrl":"https://doi.org/10.5784/33-2-568","url":null,"abstract":"Segmentation (or partitioning) of data for the purpose of enhancing predictive modelling is a well-established practice in the banking industry. Unsupervised and supervised approaches are the two main streams of segmentation and examples exist where the application of these techniques improved the performance of predictive models. Both these streams focus, however, on a single aspect (i.e. either target separation or independent variable distribution) and combining them may deliver better results in some instances. In this paper a semi-supervised segmentation algorithm is presented, which is based on k-means clustering and which applies information value for the purpose of informing the segmentation process. Simulated data are used to identify a few key characteristics that may cause one segmentation technique to outperform another. In the empirical study the newly proposed semi-supervised segmentation algorithm outperforms both an unsupervised and a supervised segmentation technique, when compared by using the Gini coecient as performance measure of the resulting predictive models. Key words : Banking, clustering, multivariate statistics, data mining","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"3 1","pages":"85-103"},"PeriodicalIF":0.0,"publicationDate":"2017-12-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89714108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Editorial to Volume 33(1)","authors":"S. E. Visagie","doi":"10.5784/33-1-577","DOIUrl":"https://doi.org/10.5784/33-1-577","url":null,"abstract":"","PeriodicalId":30587,"journal":{"name":"ORiON","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2017-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83084732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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