Thesis (MCom)--Stellenbosch University, 2014. / ENGLISH ABSTRACT: The South African fresh fruit export industry is concerned about fruit and financial losses due to temperature breaks within the fresh fruit export cold chain. The Port of Cape Town plays a crucial role in the export process as the majority of fruit reefer containers that are exported through Cape Town are handled by the Cape Town Container Terminal. This study focuses on the container terminal leg of the fresh fruit export process. Observations made in the Cape Town Container Terminal, at shipping lines and exporting companies show that certain procedures are not always followed in the Cape Town Container Terminal and that congestion and ineffective working methods are causing breaks within the fresh fruit export cold chain. Temperature and time data received from Transnet Port Terminals, shipping lines and exporting companies were analysed for the container terminal segment of the export process. From the data analysis it is clear that there are a large number of breaks originating within the container terminal and that the port is not operating efficiently. The study identifies areas of improvement and makes recommendations on improving some of the issues discussed.

Thesis (MBA)--University of Stellenbosch, 2010. / According to the 6th State of Logistics Survey for South Africa (2009: 5), logistics costs for 2008 reached R339 billion, equivalent to 14.7% of GDP. Transport represents 50.4% of these logistics costs compared to a world average of 39%. This major gap between South Africa and the world average shows that there are inefficiencies in this domain that need improvement. This report focuses on the issues faced by the port of Cape Town, the benefits that could be generated by the implementation of an inland port in the Cape Town area and the key success factors for this implementation. The Centre for Supply Chain Management of the University of Stellenbosch created a forecast model for South Africa and expects a demand of over 2.4 million Twenty-foot Equivalent units (TEUs) in 2039 for the port of Cape Town with a current throughput of 740,000. In 2012, this capacity will reach 1.4 million TEUs thanks to an on-going project that includes the widening of berth, investment in equipment, training of operators and a better utilization of the available storage space. The port therefore has to find a solution to increase a throughput on the long term. Along with capacity, the port is facing other issues such as low productivity, poor infrastructure and congestion in the port area that causes increased delivery time and trucking costs. The inland port has to bring solutions to these issues. Capacity must be addressed with a large piece of land that can accommodate growing volumes and also large investment in equipment and training to increase the productivity and therefore the throughput of the supply chain. In order to decrease congestion in the port area, the inland port has to be located out of the city in an area that can sustain growing traffic. Belcon is a Transnet Freight Rail facility located in Bellville that can offer sufficient storage capacity in a low traffic area. Investments must be made in order to increase its throughput but it has the potential to absorb a large part of the flows going through the port of Cape Town and a management with the will to develop the inland port concept. At the same time, this inland port is an opportunity to develop intermodal transport for a more sustainable transport system in South Africa. Belcon being a TFR terminal, it is the best location to implement this inland port and offer a competitive intermodal solution for the stakeholders of the South African transport industry.

This thesis focuses on decision support for regional LTL carriers. The basic operating characteristics of regional LTL carriers are similar to those of national LTL carriers, i.e., they operate linehaul networks with satellites, breakbulks, and relays to consolidate freight so as to be able to cost-effectively serve their customers. However, there are also key differences. Most importantly, because the area covered by a regional carrier is smaller, a regional carrier handles less freight (sometimes significantly less) and therefore typically has fewer consolidation opportunities, which results in higher handling and transportation costs per unit of freight. Consequently, competing with national carriers on price is difficult. Therefore, to gain or maintain market share, regional carriers have to provide better service. To be able to provide better service, regional carriers have to be more dynamic, e.g., they have to be able to deviate from their load plan when appropriate, which creates challenges for decision makers. Regional carriers deliver about 60% of their shipments within a day and almost all of their shipments within two days. Furthermore, most drivers get back to their domicile at the end of each day. Therefore, the focus of the thesis is the development of effective and efficient decision models supporting daily operations of regional LTL carriers which provide excellent service at low cost. This thesis presents an effective solution approach based on two optimization models: a dynamic load planning model and a driver assignment model. The dynamic load planning model consists of two parts: an integer program to generate the best paths for daily origin-destination freight volumes and an integer program to pack freight into trailers and trailers into loads, and to determine dispatch times for these loads. Techniques to efficiently solve these integer program solution are discussed in detail. The driver assignment model is solved in multiple stages, each stage requiring the solution of a set packing models in which columns represent driver duties. Each stages determines admissible driver duties. The quality and efficiency of the solution approach are demonstrated through a computational study with real-life data from one of the largest regional LTL carriers in the country. An important "technique" for reducing driver requirements is the use of meet-and-turn operations. A basic meet-and-turn operation involves two drivers meeting at a location in between terminals and exchange trucks. A parking lot or a rest area suffices as a meet-and-turn location. This ensures that drivers return to the terminal where they started. More sophisticated meet-and-turn operations also exist, often called drop and hook operations. In this case, drivers do not exchange trucks, but one of their trailers. The motivation in this case is not to get drivers back to their domicile, but to reduce load- miles. The thesis presents analytical results quantifying the maximum benefits of using meet and turn operations and optimization techniques for identifying profitable meet-and-turn opportunities.

Thesis (MComm (Logistics))--Stellenbosch University, 2008. / One of the results of globalisation is that supply chains are getting longer, in both time and distance. For example, the local bookstore around the corner now competes with the bookstore in the USA. Logistics ties together geographically distant sources and markets. The implications of this are that there is a greater need for efficiency in specifically transportation and distribution networks. The integration of transport infrastructure into a logistics hub is seen as an enabler of distribution on a global basis. A solution to overcome the above complications is the development of logistics hubs as a means to simplify supply chain processes. Logistics hubs are generally defined as integrated centres for transhipment, storage, collection and distribution of goods (Jorgenson, 2007). Universally logistics hubs have intermodal or multi-modal solutions to abridge transportation difficulties and creating seamless movement of goods and in doing so optimising general operations. Freight shipments now have the ability to be consolidated at a central point and distributed from that point to its final destination. This creates added value for freight products. Customers now receive products at the right time, at the right place and in the right quantity, but with the benefit of paying less as a result of economies of scale created by the value added at the logistics hub. Logistics hubs are very well established internationally and many examples exist where these have been successfully built and implemented. This does not suggest that there is one specific recipe to success. Each region has its own demands which need to be satisfied. There are however numerous common characteristics which were identified during the course of the study. It is important for South Africa to integrate existing transport infrastructure to optimise logistics in the country and in doing so, ascertaining itself as a regional logistics hub.

O estudo tem como finalidade tratar a alocação de recursos no nível operacional com a presença de incertezas. Para isso, foi proposta uma abordagem de otimização usando métodos heurísticos. As soluções de problemas de produção e logística, comumente abordadas em pesquisa operacional, exploram diversos parâmetros dentre os quais o presente estudo considera três como de incerteza: demanda, tempo de execução e indisponibilidade de recursos. Para tal finalidade foi escolhido como estudo de caso a resolução de um problema de logística. O problema consiste na minimização dos custos de operação, na seleção de veículos em uma frota heterogênea, consolidação das cargas para cada cliente e na seleção do tipo de frete utilizado. Quanto ao tipo de frete, são considerados dois, os quais se diferenciam quanto aos ativos envolvidos na produção e ao tipo de prestação de serviço, sendo eles: i) frota da empresa com serviço terceirizado; ii) frota e serviços totalmente terceirizados. O problema original foi decomposto em duas etapas: i) Compartimentalizador e ii) Alocador. As duas etapas são solucionadas via a abordagem de Busca Tabu, sendo que a primeira etapa (Compartimentalizador) gera uma lista dos carregamentos factíveis que atenda pedidos de até três clientes distintos. O Alocador se utiliza da lista dos carregamentos factíveis para definir como e quando cada pedido será atendido. Os resultados indicam a viabilidade da adoção desta abordagem para a solução de problemas reais. / The study aims to address the allocation of resources at the operational level under uncertainties. For this reason, it was proposed an optimization approach based on heuristic methods. The resolutions of production and logistics problems, commonly addressed in operational research, explore various parameters among which the present study considers three variables of uncertainty: demand, operation time and resources availability. For this purpose a logistics problem was chosen as study of case. The problem consists in minimizing cost operation, selection of vehicles in a heterogeneous fleet, consolidation of loads for each client and selecting the type of freight payables. Regarding of freight payables types, there are centered in two different tariffs, mainly due to assets and service negotiation, such as: i) fleet controlled by company and service outsource; ii) fleet and service completely outsource. The resolution of the original problem was broke down in two steps: i) Compartmentalizer and ii) Allocator. Both steps are solved through Tabu Search approach; the first step (Compartmentalizer) generates a list of feasible shipments to fulfill orders up to three different customers. The second step, the allocator uses the list of feasible shipments to define how and when each request will be supplied. The results aim the feasibility of assumes this approach in order to solve real problems.

O estudo tem como finalidade tratar a alocação de recursos no nível operacional com a presença de incertezas. Para isso, foi proposta uma abordagem de otimização usando métodos heurísticos. As soluções de problemas de produção e logística, comumente abordadas em pesquisa operacional, exploram diversos parâmetros dentre os quais o presente estudo considera três como de incerteza: demanda, tempo de execução e indisponibilidade de recursos. Para tal finalidade foi escolhido como estudo de caso a resolução de um problema de logística. O problema consiste na minimização dos custos de operação, na seleção de veículos em uma frota heterogênea, consolidação das cargas para cada cliente e na seleção do tipo de frete utilizado. Quanto ao tipo de frete, são considerados dois, os quais se diferenciam quanto aos ativos envolvidos na produção e ao tipo de prestação de serviço, sendo eles: i) frota da empresa com serviço terceirizado; ii) frota e serviços totalmente terceirizados. O problema original foi decomposto em duas etapas: i) Compartimentalizador e ii) Alocador. As duas etapas são solucionadas via a abordagem de Busca Tabu, sendo que a primeira etapa (Compartimentalizador) gera uma lista dos carregamentos factíveis que atenda pedidos de até três clientes distintos. O Alocador se utiliza da lista dos carregamentos factíveis para definir como e quando cada pedido será atendido. Os resultados indicam a viabilidade da adoção desta abordagem para a solução de problemas reais. / The study aims to address the allocation of resources at the operational level under uncertainties. For this reason, it was proposed an optimization approach based on heuristic methods. The resolutions of production and logistics problems, commonly addressed in operational research, explore various parameters among which the present study considers three variables of uncertainty: demand, operation time and resources availability. For this purpose a logistics problem was chosen as study of case. The problem consists in minimizing cost operation, selection of vehicles in a heterogeneous fleet, consolidation of loads for each client and selecting the type of freight payables. Regarding of freight payables types, there are centered in two different tariffs, mainly due to assets and service negotiation, such as: i) fleet controlled by company and service outsource; ii) fleet and service completely outsource. The resolution of the original problem was broke down in two steps: i) Compartmentalizer and ii) Allocator. Both steps are solved through Tabu Search approach; the first step (Compartmentalizer) generates a list of feasible shipments to fulfill orders up to three different customers. The second step, the allocator uses the list of feasible shipments to define how and when each request will be supplied. The results aim the feasibility of assumes this approach in order to solve real problems.