Statistics and Operations Research Publications

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Statistics and Operations Research Publications

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    Non‑parametric quantile regression‑based modelling of additive efects to solar irradiation in Southern Africa
    (Scientific Reports, 2024) Masache, A.; Maposa, D.; Mdlongwa, P.; Sigauke, C.
    Modelling of solar irradiation is paramount to renewable energy management. This warrants the inclusion of additive effects to predict solar irradiation. Modelling of additive effects to solar irradiation can improve the forecasting accuracy of prediction frameworks. To help develop the frameworks, this current study modelled the additive effects using non-parametric quantile regression (QR). The approach applies quantile splines to approximate non-parametric components when finding the best relationships between covariates and the response variable. However, some additive effects are perceived as linear. Thus, the study included the partial linearly additive quantile regression model (PLAQR) in the quest to find how best the additive effects can be modelled. As a result, a comparative investigation on the forecasting performances of the PLAQR, an additive quantile regression (AQR) model and the new quantile generalised additive model (QGAM) using out-of-sample and probabilistic forecasting metric evaluations was done. Forecasted density plots, Murphy diagrams and results from the Diebold–Mariano (DM) hypothesis test were also analysed. The density plot, the curves on the Murphy diagram and most metric scores computed for the QGAM were slightly better than for the PLAQR and AQR models. That is, even though the DM test indicates that the PLAQR and AQR models are less accurate than the QGAM, we could not conclude an outright greater forecasting performance of the QGAM than the PLAQR or AQR models. However, in situations of probabilistic forecasting metric preferences, each model can be prioritised to be applied to the metric where it performed slightly the best. The three models performed differently in different locations, but the location was not a significant factor in their performances. In contrast, forecasting horizon and sample size influenced model performance differently in the three additive models. The performance variations also depended on the metric being evaluated. Therefore, the study has established the best forecasting horizons and sample sizes for the different metrics. It was finally concluded that a 20% forecasting horizon and a minimum sample size of 10000 data points are ideal when modelling additive effects of solar irradiation using non-parametric QR.
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    Application of Network Reconstruction Algorithm to Compute Maximum Flow for Water Supply Network: A Case Study of the City of Bulawayo, Zimbabwe
    (Universal Wiser Publisher, 2023-12-13) Tawanda, T.; Kumar, S.; Kumar, S.; Nyamugure, P.
    Determining the maximum flow value in Water Supply Networks (WSN) is a common problem that is being faced by many cities during and after designing of WSN. In this article, the network reconstruction (NR) algorithm is applied to compute the maximum flow value for the city’s WSN based on the actual data. The city of Bulawayo has been selected for the following reasons, availability of research data, the city is facing severe water shedding and several studies have focused only on other issues such as alternative water sources, leakage detection and demand forecasting. The goal of this study is to determine the maximum flow value from the sources to the reservoirs. The results have revealed that the computed maximum flow value is within the estimated range of 110,000 cubic meters to 190,000 cubic meters. Dam sensitivity analysis were considered to determine the sources to give maintenance priority before the rain season. Several recommendations were suggested to improve the water supply situation.
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    Development and performance evaluation of a web-based feature extraction and recognition system for sheet metal bending process planning operations
    (Taylor and Francis, 2021-02-14) Murena, E.; Mpofu, K.; Ncube, A.T.; Makinde, O.; Trimble, J.A.; Wang, X.V.
    Sheet metal bending manufacturing companies require changeable and adaptable process planning systems to shorten the production cycle time and reduce operations costs. This is due to globalisation and the rapid change in market demands for sheet metal products. In light of this, this paper proposes a web-based feature extraction and recognition system. The system would ensure automated planning of various processes used by a bending machine to produce varieties of sheet metal products. The algorithms were implemented using C++ to produce the geometric and feature models used to extract, and recognise the bending features in various CAD files acquired from literature. Five (5) CAD files of various sheet metals were utilised to test the functionality of this system. The results from the feature recognition process have proven to be precisely what the user has designed and saved in the model file. Bend radius and bend angle. Finally, the developed system is able to transform a STEP file into a feature model and display the 3D CAD model within the least time. It is a cost-efficient standalone system that provides data storage, allows collaboration and data sharing. can be used anywhere where there is internet access.
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    Train Schedule Optimization: A Case Study of the National Railways of Zimbabwe
    (Research Academy of Social Sciences, 2014) Nyamugure, P.; Swene, S.D.; Chiyaka, E.T.; Mutasa, F.K.
    The locomotive assignment problem involves assigning a set of locomotives to each train in a pre-planned train schedule so as to provide sufficient power to pull them from their origins to their destinations. An integrated model that determines the set of active and deadheaded locomotives for each train, light travelling locomotives and train-to-train connections is presented. The model explicitly considers consist-busting and consistency. A Mixed Integer Programming (MIP) formulation of the problem that contains about 92 integer variables and 56 constraints is presented in the study. Three models are discussed for assigning locomotives to wagons and coaches and the results are compared amongst the models themselves and compared to the existing scenario at National Railways of Zimbabwe (NRZ). The models generally improve the number of saved locomotives and number of used locomotives. The Locomotive Assignment Model (LAM) solution obtained showed savings of over 70 locomotives, which translates into savings of over one-hundred thousand dollars weekly.
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    Determinants of house prices using spatial analysis: the case for Bulawayo
    (NUST, 2023) Mupondo, N.C.; Ncube, B.; Mupondo, A.; Nemahwe, S.C.
    The factors affecting house prices are crucial to Zimbabwe’s property organisation, and they necessitate an understanding of market trends and patterns in the housing industry. The primary goal of this research is to investigate the correlations between house prices and the factors that influence them to develop a model that can forecast house prices in Bulawayo. This study uses exploratory data analysis and spatial regression approaches to analyse factors affecting house prices in Bulawayo to understand how much housing costs are influenced by the availability of health services and retail stores. How does the distance to schools and the central business district (CBD) affect property prices, as well as the size of the land and the physical environment? To attain these goals, spatial analysis and local regression parameter estimates were used. The study found that many variables have both positive and negative effects on house prices across space and that the spatial lag model is the best fit for predicting house values in Bulawayo.