Doctoral Degrees (Logistics)
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- ItemOrder picking optimisation on a unidirectional cyclical picking line(Stellenbosch : Stellenbosch University, 2020-12) Hofmann, Flora; Visagie, Stephan E.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics.ENGLISH SUMMARY : The order picking system in a company's distribution centre is the biggest contributor to the operational cost within the DC. Optimisation should thus aim at running this activity as effciently as possible. The order picking process consists of three main activities, namely walking to the stock, picking stock in fullment of a customer order and handling the picked stock for further processing. While the total amount of work for the picking and handling activities remain constant, the minimisation of walking distance becomes the main objective when minimising the total picking effort. The minimisation of walking distance can be translated into a reduced overall picking time which can lead to a decrease in the total cost of operating the picking system. The main objective of this dissertation is to optimise the order picking system on a unidirectional cyclical picking line. Order batching is introduced to the picking system, since it is an effective methodology that minimises walking distance in operations research literature. Order batching has been introduced to the standard single block parallel-aisle warehouse layout, but not to the specic layout of a unidirectional cyclical picking line. Additionally, the unidirectional cyclical picking line can offer two conguration options that change the physical set up and thereby inffuence the way in which pickers walk during the order picking process. Order batching is introduced to the unidirectional cyclical picking line through picking location based order-to-route closeness metrics. These metrics are further extended by taking the characteristics of the layout into account. The distribution centre of a prominent South African retailer provides real life test instances. Introducing the layout specic stops non-identical spans metric in combination with the greedy smallest entry heuristic results in a reduction of 48:3% in walking distance. Order batching increases the pick density which may lead to higher levels in picker congestion. In a discrete event simulation, the reduction of the overall picking time through a decrease in walking distance is thus conrmed. On tested sample picking waves, the overall picking time can be reduced by up to 21% per wave. A good number of pickers in the picking system is dependent on the pick density. The pick density, amongst other explanatory variables, can also be used to predict the reduction in picking time. The effects of different structural options of the unidirectional cyclical picking line, namely the U- and Z-conguration, are investigated. This results in four decision tiers that have to be addressed while optimising the order picking system. The rst decision tier assigns stock to picking lines, the second arranges stock around a picking line, the third chooses the conguration and the last sequences the orders to be picked. Order batching is added as an additional layer. An increase in pick density benets the reduction of walking distance throughout the decision tiers and supports the choice of the U-conguration after evaluating different test instances. The total completion time of a picking wave can thus be reduced by up to 28% when compared to benchmark instances. The dissertation is concluded by suggesting further research directions.
- ItemApplication of long short-term memory artificial neural networks to forecast water supply and demand in the Lake Chad Basin(Stellenbosch : Stellenbosch University, 2020-12) Fouotsa Manfouo, Noe Careme; Potgieter, Linke; Nel, Johanna H.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics.ENGLISH ABSTRACT: The implementation of effective water resources management in developing countries in general and in the Lake Chad Basin in particular, is hindered by the absence of reliable information on both the net water supply, as well as on the agricultural water demand. The main purpose of this research is to provide a methodology to determine and forecast total water supply and water demand in the context of scarce data for water resources management. In order to develop a forecasting methodology, a literature survey is first performed to understand the current environment and methodology of water resources management in the Lake Chad Basin, to highlight the main problems faced within the context, and to identify the opportunity for applied research. As part of this investigation, different stakeholders were visited during a field trip to the Lake Chad Basin. The main water users identified in the Lake Chad Basin do not have historical data on agricultural water demands, making it difficult to understand current water demand requirements or estimate future demand in the Lake Chad Basin. Literature available on the Lake Chad Basin were also considered. A hydrological model was developed in 2011 by Bader, Lemoalle, and Leblanc and reported on in the paper Modèle hydrologique du Lac Tchad [16]. The model provides information on the lake storage for the period 1956 to 2011, however, it does not consider upstream diversion. Therefore, the output of the model does not allow an exhaustive estimation of water supply in the Lake Chad Basin. In addition, the model is data intensive and uses variables that are neither easy to obtain, nor straightforward to compute, and requires expert hydrological knowledge to extend the use of the model for future water supply estimation beyond 2011. Moreover, there are currently no model developed for estimating water demand in the Lake Chad Basin. Long short-term memory is an artificial recurrent neural network that have been shown to perform exceptionally well in the context of time series forecasting, due to its ability to incorporate lags of unknown duration in the network structure. Despite the good track record of this methodology in forecasting time series, it is not widely used in the literature for water supply and demand estimation. In this dissertation, multivariate time series forecasting with long short-term memory is investigated as an alternative methodology for different aspects of water supply and demand estimation. Pearson correlation, random forest, extra trees classifiers and principal component analysis are investigated as input selection approaches to increase prediction accuracy. For water supply estimation, a lake storage forecasting model as well as a streamflow forecasting model are developed. Results indicate that long short-term memory can be used to predict Lake Chad Basin storage, with better performances than the state of the art results, obtained from artificial neural networks and support vector regression. The multivariate approach indicates that atmospheric data are both good and easily obtainable data for lake storage forecasting. The input variables, selected with both the principal component analysis and random forest approach are recommended for streamflow forecasting in the Lake Chad Basin. Random forest occupies the second position, by producing better predictions in the Ndjamena gauging station. A long-term temperature forecasting model as well as a precipitation forecasting model were developed and the outputs were used as input in the CROPWAT software to determine the irrigation water requirement per hectare per crop type. A comparison between the widely used statistical downscaling model and the forecasting models for long-term temperatures and precipitations developed in this research indicate better accuracy using the multivariate long short-term memory approach. Both the root mean square error and the mean absolute percentage error used to check the performances of the models indicate commendable accuracy. Four population dynamics models, namely the malthusian growth model; the logistic growth models with both constant and dynamic rates, as well a logistic growth model with dynamic rate and species interaction, are developed to estimate the size of land used for both crop and livestock, and to finally predict the total agricultural water demand in the Lake Chad Basin. The models are parameterised using long short-term memory. A case by case investigation of prediction performances across the three countries indicates that the malthusian growth approach produces better performances in 9 cases, the logistic growth model with constant rate performs better in 4 cases, and the logistic growth model with dynamic rate performs better in 7 cases. The malthusian approach is more suitable for variables with unstable trends, the logistic model with constant rate is more suitable for variables with almost concave or convex shapes and the logistic growth with dynamic rate is the most useful long-term crop land-use and livestock population forecasting. Finally, the best performing models for crop land-use and livestock population are downscaled to main water users level, in order to estimate total water demand per crop type and per livestock type. The investigation of the four population dynamics models, on both the crop land-use and livestock population dynamics, the characterisation of the competition type between species in the Lake Chad Basin case study as well as the estimation of water demand at water users’ level is a new contribution to literature.
- ItemA systems perspective of basic education in South Africa(Stellenbosch : Stellenbosch University, 2020-03) Venter, Lieschen; Visagie, Stephan E.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics.ENGLISH SUMMARY : South Africa has one of the highest measures of economic inequality and one of the worst performing basic education systems in the world. The majority of learners from poorer communities are attending dysfunctional schools, while a minority of learners from richer communities are achieving adequately in a functional system. The economic disparity creates an intuition that allocating more funds can solve low academic performance, but this approach has yielded little return for a number of years. In this dissertation the impact of school leadership on learners' academic performance is considered in the South African context. School management is a systemic concept and elements thereof cannot be analysed in isolation. A series of system dynamics simulation models is developed to understand the effect of various school management interventions on communities, teachers, resources, and learners within the basic education system. The School Effectiveness Model simulates the South African basic education system and reveals that improvement interventions must be made early, continously and in multiple areas for them to be effective. The Teacher Effectiveness Model simulates the career progression of Western Cape public teachers and reveals that the number of the teachers appointed in a primary school has a greater impact on their effectiveness than the quality of the teachers appointed. The Early Childhood Development Model simulates the preschool career of Western Cape children and reveals that improving the quality of Early Childhood Development programmes has a greater impact on their primary school readiness than increasing the number of children enrolled into programmes. The Primary School Model simulates the progression of learners from Grade 1 to Grade 7 in the Western Cape and reveals that improving learners' social circumstance at home has a greater impact on their academic performance than improving their classroom experience. Finally, the expanded School Effectiveness Model brings all the models together to reveal that a combination of interventions is needed to decrease the academic performance gap between poorer and richer communities within the Western Cape.
- ItemRoads infrastructure funding and financing for Namibia : a case study of the national road network(Stellenbosch : Stellenbosch University, 2020-03) Petrus, Helvi Ndilimeke; Krygsman, Stephan C.; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics.ENGLISH SUMMARY : Namibia established a second-generation road fund with the aim of implementing a user-pays system by ensuring that road users pay for their consumption of road services. The national road network covers approximately 48 399 km and serves a vehicle population of approximately 371 281. Somewhat implicit in these road funds is the confidence that the Road User Charge System (RUCS) will deliver sufficient funding for the road sector. This exploratory case study investigated the funding and financing of the national road network in Namibia. The national road network constituted the population from which the hypothetical road samples were drawn. Several data-collection methods were employed, including document analysis and secondary data analysis. The research employed the Highway Development and Management (HDM-4) model to estimate the external costs of road use. This research evaluated the relationship between the road-generated revenue (RGR) and its allocation towards the national road network expenditure and related these to international standards. The findings indicate that the Road Fund Administration (RFA) possesses high transparency in allocating RGR towards the preservation of the road network. This places Namibia among countries with high dedication of 80% and above towards road expenditure, together with the United States of America (USA) and Switzerland when compared to international standards. While revenue generated from road users are highly allocated to the preservation of the road network (0.96 ratio), a wide gap remains between the required funds and resources available for road expenditure. Financing for road expenditure was found to be a dilemma facing many developing countries, where revenue from road users does not cover the total road costs due to limited capacity and economics of use. Additional funding sources are therefore required to fund these deficits. The research also demonstrated the applicability of the Highway Development and Management (HDM-4) model, to determine the Marginal External Costs (MEC) of road use. The results indicate that heavy vehicles impose the highest costs in terms of infrastructure damage and environmental costs when using the network. When applying marginal costing, the results indicate that heavy vehicles contribute approximately 98% (district road), 97% (main road), and approximately 98% (trunk road) in terms of external costs when using the respective network. Overall, light vehicles contribute the most to congestion and accidents costs when using the national road network. Although the results presented the national road network to be congestion free, relatively low congestion was traced on the trunk road, thus increasing the overall cost contribution for light vehicles from 2% (district road) and 3% (main road) to approximately 19% when using the truck road network. The findings indicate that motorists impose some externalities when using the road network and it would make economic sense to internalise such costs to road users. The research further assessed the implications of setting Road User Charges (RUC) at the Short-Run Marginal Costs (SRMC) of road use. The results indicate that setting RUC equal to correct prices leads to an estimated road funding deficit of N$5 062 746 on the sampled trunk road. These findings indicate that a marginal pricing approach in the Namibian context (expansive road network serving few users) might not necessarily raise the revenue required for the investment and maintenance of the network. This situation calls for an alternative approach to marginal pricing. In exploring the second-best RUC suitable to the Namibian funding circumstances, this study explored what Namibia could learn from other countries with expansive road networks such as Australia and New Zealand. The findings presented the efforts Namibia that has made in terms of policy formulation and noteworthy institutional frameworks, which have made Namibia the leading country in sub-Saharan Africa in terms of road-quality rankings. However, Namibia needs to embrace technologies towards charging vehicles per kilometre. The existing Mass Distance Charges (MDC) attempted to solve the challenges associated with charging heavy vehicles according to distance travelled; however, the current MDC is a blunt instrument that does not adjust charges according to weight, time, and location. Reforming the current system with the focus on distinguishing suitable charges for light and heavy vehicles to account for their use of the road network per vehicle per kilometre according to time and location is something that Namibia could learn from Australia and New Zealand. Collaborating efforts from both the public and private sectors could be another step toward a road financing solution. The contribution of this study revolves around adding to the existing knowledge relating to financing expansive road networks that serve a small vehicle population by assessing the RUCS with a particular focus on the user-pays principle and by estimating the MEC of road use by utilising the HDM-4 model.
- ItemA framework for sustainable road freight decarbonisation in South Africa(Stellenbosch : Stellenbosch University, 2019-12) Terblanche, Lee-Anne; Havenga, Jan H.; Goedhals-Gerber, Leila Louise; Stellenbosch University. Faculty of Economic and Management Sciences. Dept. of Logistics. Logistics.ENGLISH ABSTRACT: Local and international governments are becoming more aware of carbon emission outputs and the damage that these emissions are inflicting on the environment. The definite impact that carbon emissions have on global warming has received public and media attention, which has placed high carbon-intensive companies and products under surveillance. Following electricity, road freight in South Africa is the second-biggest generator of carbon emissions. Thus, this research problem identified the need to decrease South Africa’s road freight emissions through the use of a road freight decarbonisation framework. The research problem entailed applying a framework to South Africa and expanding the structure to include the unique challenges in the South African road freight industry. A mixed methods research design was conducted to include both qualitative and quantitative data for the road freight industry in South Africa. The first objective of the research was to establish which road freight decarbonisation strategy, framework, system or tool would suit South Africa that also provides a holistic approach to decarbonising road freight activities. A literature review and criteria analysis concluded that the McKinnon road freight decarbonisation framework would be best suited to adapt and expand within the South African context. The literature review was followed by qualitative and quantitative data gathering to establish how industry professionals perceived the McKinnon framework, and to determine what further inputs into the framework could be provided. The data gathering consisted of personal interviews with industry professionals and data questionnaires that were sent out to working professionals in the road freight industry. After South African challenges were established and added to the chosen framework, the South African challenges for road freight carbon emission were quantified, through data questionnaires, to determine what total impact these challenges have on total road freight emissions and to quantify the carbon variables on a national basis. The outcome of the research provided the first South African decarbonisation framework, which highlights the road freight challenges South African companies are facing daily. The study identifies what the current main road freight carbon-intensive challenges are in South Africa, so that South Africa can focus on these costly and highly intensive emission influences and be aware that the problems are not isolated events, but can affect all road freight companies in South Africa. Keywords: Road freight, decarbonisation, carbon emissions, challenges