The use of analytical hierarchy process in spatial decision support system for land use management

Geographic information systems give us the possibility to analyze, produce, and edit geographic information. Furthermore, these systems fall short on the analysis and support of complex spatial problems. Therefore, when a spatial problem, like land use management, requires a multi-criteria perspective, multi-criteria decision analysis is placed into spatial decision support systems. The analytic hierarchy process is one of many multi-criteria decision analysis methods that can be used to support these complex problems. Using its capabilities we try to develop a spatial decision support system, to help land use management. Land use management can undertake a broad spectrum of spatial decision problems. The developed decision support system had to accept as input, various formats and types of data, raster or vector format, and the vector could be polygon line or point type. The support system was designed to perform its analysis for the Zambezi river Valley in Mozambique, the study area. The possible solutions for the emerging problems had to cover the entire region. This required the system to process large sets of data, and constantly adjust to new problems’ needs. The developed decision support system, is able to process thousands of alternatives using the analytical hierarchy process, and produce an output suitability map for the problems faced.

Management consulting labs Galp loyalty

The main objective of this project was to investigate methods to create a new loyalty approach for Galp Energia in order to improve customer retention and reduce churn, related with the expansion of hypermarket chains’ in the Fuel Retail Market and the country’s economical situation. The team carried out on-spot surveys and focus groups, researched loyalty programs’ best practices, analyzed peers practices and the company’s past performance in order to find important customer insights. These were used to develop the final recommendations resulting in a new paradigm to the group’s loyalty approach alongside incremental improvements to the current loyalty solutions.

Numerical simulation of blast effects on fibre grout RC panels

The main purpose of the present dissertation is the simulation of the response of fibre grout strengthened RC panels when subjected to blast effects using the Applied Element Method, in order to validate and verify its applicability. Therefore, four experimental models, three of which were strengthened with a cement-based grout, each reinforced by one type of steel reinforcement, were tested against blast effects. After the calibration of the experimental set-up, it was possible to obtain and compare the response to the blast effects of the model without strengthening (reference model), and a fibre grout strengthened RC panel (strengthened model). Afterwards, a numerical model of the reference model was created in the commercial software Extreme Loading for Structures, which is based on the Applied Element Method, and calibrated to the obtained experimental results, namely to the residual displacement obtained by the experimental monitoring system. With the calibration verified, it is possible to assume that the numerical model correctly predicts the response of fibre grout RC panels when subjected to blast effects. In order to verify this assumption, the strengthened model was modelled and subjected to the blast effects of the corresponding experimental set-up. The comparison between the residual and maximum displacements and the bottom surface’s cracking obtained in the experimental and the numerical tests yields a difference of 4 % for the maximum displacements of the reference model, and a difference of 4 and 10 % for the residual and maximum displacements of the strengthened model, respectively. Additionally, the cracking on the bottom surface of the models was similar in both methods. Therefore, one can conclude that the Applied ElementMethod can correctly predict and simulate the response of fibre grout strengthened RC panels when subjected to blast effects.

An essay on agile project management practices

The main goals for the current dissertation is to research on how practices and concepts from Agile Project Management can be applied in a non-IT context and to discover which aspects should be considered when deciding if whether an Agile approach should be implemented or not. Previous studies reflect on the adoption for the identified context. However, the recognition of these practices and concepts by the Project Management field of studies still remains unresolved. The adoption of Agile Project Management emerges as a manifestation against traditional approaches, mainly due to their inability of accepting requirements’ changes. Therefore, these practices and concepts can be considered in order to reduce the risks concerning the increase of competition and innovation – which does not apply to the IT sector solely. The current study reviews the literature on Agile Project Management and its adoption across different sectors in order to assess which practices and concepts can be applied on a non-IT context. Nine different methods are reviewed, where two of these show a higher relevance – Scrum and Extreme Programming. The identified practices and concepts can be separated into four different groups: Cultural and Organizational Structures, Process, Practices, and Artefacts. A framework based on the work by Boehm & Turner in 2004 is developed in order to support the decision of adopting agile methods. A survey intended for project managers was carried in order to assess the implementation of the identified practices and concepts and to evaluate which variables have the highest importance on the developed decision support framework. It is concluded that New Product Development is the project type with the highest potential to implement an agile approach and that the Project Final Product’s Innovativeness, Competitiveness, and the Project Member’s Experience and Autonomy are the most important aspects to consider an implementation of an Agile approach.

Mapping the Pasture Steppe in Bayankhongor, Mongolia: comparison of classification methods, using Landsat-8 and geophysical data

Grasslands in semi-arid regions, like Mongolian steppes, are facing desertification and degradation processes, due to climate change. Mongolia’s main economic activity consists on an extensive livestock production and, therefore, it is a concerning matter for the decision makers. Remote sensing and Geographic Information Systems provide the tools for advanced ecosystem management and have been widely used for monitoring and management of pasture resources. This study investigates which is the higher thematic detail that is possible to achieve through remote sensing, to map the steppe vegetation, using medium resolution earth observation imagery in three districts (soums) of Mongolia: Dzag, Buutsagaan and Khureemaral. After considering different thematic levels of detail for classifying the steppe vegetation, the existent pasture types within the steppe were chosen to be mapped. In order to investigate which combination of data sets yields the best results and which classification algorithm is more suitable for incorporating these data sets, a comparison between different classification methods were tested for the study area. Sixteen classifications were performed using different combinations of estimators, Landsat-8 (spectral bands and Landsat-8 NDVI-derived) and geophysical data (elevation, mean annual precipitation and mean annual temperature) using two classification algorithms, maximum likelihood and decision tree. Results showed that the best performing model was the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), using the decision tree. For maximum likelihood, the model that incorporated Landsat-8 bands with mean annual precipitation (Model 5) and the one that incorporated Landsat-8 bands with mean annual precipitation and mean annual temperature (Model 13), achieved the higher accuracies for this algorithm. The decision tree models consistently outperformed the maximum likelihood ones.