A simulation model for lean, agile, resilient and green supply chain management: practices and interoperability assessment

Dissertação para obtenção do Grau de Mestre em Engenharia e Gestão Industrial

The effects of land-use development policies on forest management

This paper develops a model of a forest owner operating in an open-city environment, where the rent for developed land is increasing concave in nearby preserved open space and is rising over time reflecting an upward trend in households’ income. Thus, our model creates the possibility of switching from forestry to residential use at some point in the future. In addition it allows the optimal harvest length to vary over time even if stumpage prices and regeneration costs remain constant. Within this framework we examine how adjacent preserved open space and alternative development constraints affect the private landowner´s decisions. We find that in the presence of rising income, preserved open space hastens regeneration and conversion cuts but leads to lower density development of nearby unzoned parcels due to indirect dynamic effects. We also find that both a binding development moratorium and a binding minimum-lot-size policy can postpone regeneration and conversion cut dates and thus help to protect open space even if only temporarily. However, the policies do not have the same effects on development density of converted forestland. While the former leads to high-density development, the latter encourages low-density development.

Modelling lean and green supply chain

Dissertação para obtenção do Grau de Doutor em Engenharia Industrial

Taxes and labor supply: Portugal, Europe, and the United States (Conference version)

I relate hours worked with taxes on consumption and labor. I propose a model and compare its predictions for Portugal, France, Spain, United Kingdom and United States. Hours per worker in Portugal decreased from 35.1 in 1986 to 32.6 in 2001. With only the parameters and the taxes for Portugal, the model predicts the hours worked in 2001 with an error of only 12 minutes from the actual hours. Across countries, most predictions differ from the data by one hour or less. The model is able to explain the trend in hours with only the changes in taxes.

Taxes and labor supply: Portugal, Europe, and the United States

I relate hours worked with taxes on consumption and labor for Portugal, France, Spain, United Kingdom and United States. From 1986 to 2001, hours per worker in Portugal decreased from 35.1 to 32.6. With the parameters for Portugal, the model predicts hours worked in 2001 with an error of only 12 minutes from the actual hours. Across countries, most predictions differ from the data by one hour or less. The model is not sensible to special assumptions on the parameters. I calculate the long run effects of taxes on consumption, hours, capital and welfare for Portugal. I extend the model to discuss implications for Social Security. I discuss the steady state and the transition from a pay-as-you-go to a fully funded system.

Analysis of urban land use and land cover changes: a case of study in Bahir Dar, Ethiopia

Dissertation submitted in partial fulfillment of the requirements for the Degree of Master of Science in Geospatial Technologies

How to improve supply chain performance of a specialized retailer: Managing landed costs of Zippy’s global supply chain

A Work Project, presented as part of the requirements for the Award of a Masters Degree in Management from the NOVA – School of Business and Economics

Exploratory analysis of OpenStreetMap for land use classification

In the last years, volunteers have been contributing massively to what we know nowadays as Volunteered Geographic Information. This huge amount of data might be hiding a vast geographical richness and therefore research needs to be conducted to explore their potential and use it in the solution of real world problems. In this study we conduct an exploratory analysis of data from the OpenStreetMap initiative. Using the Corine Land Cover database as reference and continental Portugal as the study area, we establish a possible correspondence between both classification nomenclatures, evaluate the quality of OpenStreetMap polygon features classification against Corine Land Cover classes from level 1 nomenclature, and analyze the spatial distribution of OpenStreetMap classes over continental Portugal. A global classification accuracy around 76% and interesting coverage areas’ values are remarkable and promising results that encourages us for future research on this topic.

Predicting the impacts of climate change on the distribution and conservation of endemic forest land snails of Madeira Island

Climate change is emerging as one of the major threats to natural communities of the world’s ecosystems; and biodiversity hotspots, such as Madeira Island, might face a challenging future in the conservation of endangered land snails’ species. With this thesis, progresses have been made in order to properly understand the impact of climate on these vulnerable taxa; and species distribution models coupled with GIS and climate change scenarios have become crucial to understand the relations between species distribution and environmental conditions, identifying threats and determining biodiversity vulnerability. With the use of MaxEnt, important changes in the species suitable areas were obtained. Laurel forest species, highly dependent on precipitation and relative humidity, may face major losses on their future suitable areas, leading to the possible extinction of several endangered species, such as Leiostyla heterodon. Despite the complexity of the biological systems, the intrinsic uncertainty of species distribution models and the lack of information about land snails’ functional traits, this analysis contributed to a pioneer study on the impacts of climate change on endemic species of Madeira Island. The future inclusion of predictions of the effect of climate change on species distribution as part of IUCN assessments could contribute to species prioritizing, promoting specific management actions and maximizing conservation investment.

Resilience of logistic service providers facing a port strike: a case study

The purpose of this thesis is to study the impact of a port strike on companies that perform as logistic service providers in a supply chain (SC), here denominated 3PL (third-party logistic providers). These companies are highly dependent on ports to perform their activity, since they provide international services. Consequently, a disruption in a port can seriously impair their business. A stevedores’ strike is one of the possible disruptions that can affect ports. This study aims to analyze the negative effects caused by this disruption, and what strategies 3PLs may implement in order to keep their performance levels stable and have a quick recovery time. Within this objective, the first step will be to establish a theoretical context about the maritime port’s sector and 3PLs in a SC context, to then expand the concept of a resilient SC, and finally to develop a theoretical framework in order to better contextualize the case study. Subsequently, the impact of a port strike will be quantified by using a case study comprising three companies, covering the areas of land and sea distribution and port operations. Information from primary sources was assembled in two phases: first via e-mail and, in a second phase, through a personal interview. The information from secondary sources was obtained through television news, internet and conferences, enabling its cross-analysis. Finally, by analyzing the collected data, it will be possible to draw conclusions about the measures carried out by each company to minimize the negative effects of the strike, thus contributing to a more resilient SC. As a conclusion, a stevedores’ strike will create a snow-ball of negative effects in the SC, degrading all relevant KPIs (key performance indicators) of the 3PLs under study. No mitigation and contingency strategies available proved really effective to reduce the negative effects of a port strike disruption.

Influence of Lean and Green on supply chain performance: an interpretive structural modelling model

This work aims to identify and rank a set of Lean and Green practices and supply chain performance measures on which managers should focus to achieve competitiveness and improve the performance of automotive supply chains. The identification of the contextual relationships among the suggested practices and measures, was performed through literature review. Their ranking was done by interviews with professionals from the automotive industry and academics with wide knowledge on the subject. The methodology of interpretive structural modelling (ISM) is a useful methodology to identify inter relationships among Lean and Green practices and supply chain performance measures and to support the evaluation of automotive supply chain performance. Using the ISM methodology, the variables under study were clustered according to their driving power and dependence power. The ISM methodology was proposed to be used in this work. The model intends to provide a better understanding of the variables that have more influence (driving variables), the others and those which are most influenced (dependent variables) by others. The information provided by this model is strategic for managers who can use it to identify which variables they should focus on in order to have competitive supply chains.

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.

Analysis of panoramio photo tags in order to extract land use information

In the recent past, hardly anyone could predict this course of GIS development. GIS is moving from desktop to cloud. Web 2.0 enabled people to input data into web. These data are becoming increasingly geolocated. Big amounts of data formed something that is called "Big Data". Scientists still don't know how to deal with it completely. Different Data Mining tools are used for trying to extract some useful information from this Big Data. In our study, we also deal with one part of these data - User Generated Geographic Content (UGGC). The Panoramio initiative allows people to upload photos and describe them with tags. These photos are geolocated, which means that they have exact location on the Earth's surface according to a certain spatial reference system. By using Data Mining tools, we are trying to answer if it is possible to extract land use information from Panoramio photo tags. Also, we tried to answer to what extent this information could be accurate. At the end, we compared different Data Mining methods in order to distinguish which one has the most suited performances for this kind of data, which is text. Our answers are quite encouraging. With more than 70% of accuracy, we proved that extracting land use information is possible to some extent. Also, we found Memory Based Reasoning (MBR) method the most suitable method for this kind of data in all cases.

Land cover mapping analysis and urban growth modeling using remote sensing techniques

The rapid growth of big cities has been noticed since 1950s when the majority of world population turned to live in urban areas rather than villages, seeking better job opportunities and higher quality of services and lifestyle circumstances. This demographic transition from rural to urban is expected to have a continuous increase. Governments, especially in less developed countries, are going to face more challenges in different sectors, raising the essence of understanding the spatial pattern of the growth for an effective urban planning. The study aimed to detect, analyse and model the urban growth in Greater Cairo Region (GCR) as one of the fast growing mega cities in the world using remote sensing data. Knowing the current and estimated urbanization situation in GCR will help decision makers in Egypt to adjust their plans and develop new ones. These plans should focus on resources reallocation to overcome the problems arising in the future and to achieve a sustainable development of urban areas, especially after the high percentage of illegal settlements which took place in the last decades. The study focused on a period of 30 years; from 1984 to 2014, and the major transitions to urban were modelled to predict the future scenarios in 2025. Three satellite images of different time stamps (1984, 2003 and 2014) were classified using Support Vector Machines (SVM) classifier, then the land cover changes were detected by applying a high level mapping technique. Later the results were analyzed for higher accurate estimations of the urban growth in the future in 2025 using Land Change Modeler (LCM) embedded in IDRISI software. Moreover, the spatial and temporal urban growth patterns were analyzed using statistical metrics developed in FRAGSTATS software. The study resulted in an overall classification accuracy of 96%, 97.3% and 96.3% for 1984, 2003 and 2014’s map, respectively. Between 1984 and 2003, 19 179 hectares of vegetation and 21 417 hectares of desert changed to urban, while from 2003 to 2014, the transitions to urban from both land cover classes were found to be 16 486 and 31 045 hectares, respectively. The model results indicated that 14% of the vegetation and 4% of the desert in 2014 will turn into urban in 2025, representing 16 512 and 24 687 hectares, respectively.

Efficiency optimization of the transportation supply chain activity: the maritime option for Volkswagen Autoeuropa

Field Lab of Entrepreneurial Innovative Ventures