Master of Science in Transport & Logistic Management

Permanent URI for this collectionhttp://192.248.9.226/handle/123/12719

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    item: Thesis-Abstract
    Strategic clustering of foundation suppliers of Sri Lankan tea and their impact on the macro supply network
    (2015-01-17) Thambipillai, TP; Styger, L; Kumarage, AS
    The tea sector is important to the Sri Lanka economy and offers employment opportunity to those who live close to tea cluster areas. Tea small holders account almost 76 % of total tea estates in Sri Lanka but their output is comparatively low with respect to other tea estates in Sri Lanka. By comparing other tea exporting countries, it is found that Sri Lanka is far behind in tea production and has lost its position in global market of tea. This is mainly because of the industry not being very profitable and the state assisting by subsidies for the survival of industry. Due to the high cost of production, tea estates and tea small holders are minimising the cost on welfare facilities of workers, replanting and technology improvement. Sri Lanka tea clusters are located in Kandy, Dimbulla, Bandarawela, Nuwara Eliya, Galle and Ratnapura. It is important to understand the drive performance indicators of tea estates and the constraints along the growth in order to formulate remedial actions. Market stability, human resources, technological capacity, finance, infrastructure, safety, administration and environment are the main factors that are affecting the performance of clusters. A Shortfall from any factor can weigh down the overall performance of tea clusters of Sri Lanka. Recommendations provided for the problems may significantly reduce the risk in driving performance criteria and improve the efficiency of tea cluster. Assistance from the State could further improve the overall facility that may improve the quality of finished tea.
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    item: Thesis-Abstract
    An investigation into the risk of outsourcing of knowledge rich, supplu critical elements within supply networks:a Sri Lnankan context
    (2014-10-31) Layangani, LDCS; Styger, L
    Outsourcing of supply network elements have been increased by companies as a potential solution to aid increasing competitive advantage. Companies are more dependent on their service providers, if the majority of their product or service elements are outsourced. This typically increases supply risk profiles. Outsourcing of knowledge rich, supply critical elements in supply networks creates substantial impact on a companies’ network. Companies with high risk profiles can typically exhibit a knock on effect in revenue. It is essential for manufacturing companies to analyze the risks linked with outsourcing of knowledge rich, supply critical elements. This research study examines the probability of risk event occurrences associated with outsourcing of knowledge rich, supply critical elements on the long term sustainability of the network. A list of knowledge rich, supply critical elements in supply networks were determined to align to the industry study carried out. The methodology consists of the development of evaluating value at risk probabilities for listed supply network elements, through the formation of Bayesian networks and with use of basic probability concepts. Transferring of knowledge is determined as an uncertain process in outsourcing. Outsourcing of knowledge rich, supply critical elements was found to be limited within the Sri Lankan manufacturing sector. Probabilities of impact on monitory values relative to outsourcing in a company were investigated and it was determined that the impact on the long term sustainability of the network was substantial. The methodology directs manufacturers to make decisions on outsourcing of knowledge rich, supply critical elements and implement risk mitigation plans to maintain competitive advantage within their supply network.
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    item: Thesis-Abstract
    Methodology to Assess the Reliability of Transport Networks under Disaster Conditions
    (2014-08-19) Adikariwattage, VV; Bandara, JMSJ
    Transportation research and development covers a multitude of topics regarding all areas in transportation. Transportation reliability and vulnerability studies are a new area that has started to draw a lot of attention particularly about its possible applications to help disaster management practices. But unfortunately transportation network risk and vulnerability assessment has not received due recognition so far when formulating preparedness policies in disaster management operations. There are various types of studies such as environmental impact assessment, cost benefit studies for transportation infrastructure where a wide variety of features are looked at, but risk and vulnerability analysis of the transportation network has not yet been considered with much importance. One major reason for this can be highlighted as the lack of established terminology and associated means of analysis that can be specifically adopted for the purpose. And further more it is difficult to draw a firm consensus on available methods due to various disparities among the concepts proposed. The aim of this research is to develop a methodology to evaluate the state of transportation networks in terms of accessibility and connectivity under disaster situations. A new methodology is proposed based on concepts of both vulnerability and reliability assessment of transportation networks. The proposed method expresses the state of the network using an index defined as the Preparedness Index that is used as a measurement of the state of the network against possible threats and degradation due to damage. The proposed preparedness index has two components, one to assess the quality or the effectiveness of the connection in terms of distance covered, travel time or LOS provided, and the other component to assess the probability of maintaining the connection that takes in to account the prevailing uncertainty in the network. With the proposed concept it was possible to achieve a good balance in the measurement regarding the state of the network without any one component, either network structural aspects or predictability and probability aspects dominating the analysis. Therefore this proposed index has the potential to over come some of the draw backs identified with conventional methods.
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    item: Thesis-Abstract
    Identifying the travel patterns of on-demand taxi trips through inferred trip purposes
    (2022) Dhananjaya DD; Sivakumar T
    Activity-based modeling has become the backbone behind transportation planning, and trip purposes that can be inferred from large GPS datasets of different travel means are paving the path to augmenting its accuracy. In this context, the trip purpose inference problem has emerged since the GPS is unable to capture the trip purposes explicitly. This problem has not been thoroughly addressed in developing countries despite the fact that the applicability of a trip purpose inference model extremely depends on the land use context. Hence, this study attempted to ameliorate an accurate model (base model) for a chosen study area in Colombo District, Sri Lanka using the on-demand taxi trips data. Point of Interest (POI) data is often accompanied by the purpose inference models as it provides a complete insight into the land use around origin and destinations. In the pursuit of the main objective of the study, machine-learning-based text classification was tested to improve the number of informative POIs and its outcome indicated that the Support Vector Machine (SVM) classifier can be utilized effectively and efficiently. The designed trip purpose inference model referring to a base model is proposed as a three-layer trip purpose inference framework in which a method to impute purpose based on trip regularities and the method to identify residential trips was included as two layers before using the base model. The validity of the proposed model was evaluated with the assistance of household travel survey data and with respect to the purpose proportions and division level R-square. Furthermore, travel patterns of the on-demand taxi trips were assessed in terms of temporal regularity, trip lengths, and spatial dynamics. It is recommended to conduct further studies to assess the applicability of other parameters such as trip origin context and trip time with the assistance of unsupervised learning methods.