Urban transshipment network is a transshipment solution for last-mile distribution in dense metropolitan regions. In this network, a set of micro hubs located in the urban areas are used for deconsolidating shipments and transferring them from high capacity vehicles to light freight vehicles that eventually deliver to customer locations. Due to the shorter distances between the micro hubs and final customers, these networks can be combined with light electric freight vehicles with limited driving ranges to deliver shipments from micro hubs networks. Thus, logistics service providers can potentially adopt these networks for complying with new low emission zone policies that restrict diesel cargo vehicles to enter dense urban regions. However, before deciding to adopt these new networks over existing conventional ones, it is imperative to assess the performance of these networks towards achieving overall business objectives. Although optimization models can be used for the evaluation process, the existing models that can be used for urban transshipment networks are computationally complex, causing their limited application to real-life contexts. Thus, this research presents a new continuous approximation-based model in the form of a support system for LSP to evaluate the performance of urban transshipment networks in a simpler aggregate manner. The application of the support system to a synthetic case study revealed that the model was able to provide a feasible solution that was adequate for strategic evaluation and performance of urban transshipment networks was contingent on the density of customer points in LEZ

The world is experiencing a new technological revolution that can have profound implications for the nature of work. Currently, attention is particularly focused on the implication of the increased adoption of industrial robots. Multiple studies predict that especially developing countries are at risk to be negatively affected by robotization in the near future, in ways such as unemployment and greater income inequality. To a large extent, developing countries became integrated in the world economy through their involvement in global value chains (GVCs). The establishment of GVCs, in turn, were a direct result of an upward trend in offshoring. However, because both offshoring and industrial robots primarily affect routine and labor-intensive tasks, the labor-cost advantage of developing countries is quickly eroding and puts at risk their strong export position. Furthermore, in recent year world trade is indeed observed to slow down, which has further fueled concerns. This thesis aims to quantify how much the adoption of industrial robots in developed countries contributed to the decline in world trade experienced in recent years. We do this by regressing a measure of offshoring on the density of industrial robots and other control variables. Offshoring is quantified in terms of the offshoring index, which is constructed using two datasets by the Organization for Economic Co-operation and Development (OECD). To get data on the adoption of industrial robots, we use a dataset that has been made available by the International Federation of Robotics (IFR). The final unbalanced panel dataset consist of 29 countries, 15 industries and spans the period 1993-2015. The other factors that will be controlled for in the models include: labor intensity, wages, year-dummies, country-trends and industry-trends. Furthermore, we estimate the models using fixed-effects. Our regression estimates provide evidence for a negative and statistically significant relationship between the adoption of industrial robots and offshoring intensity. We estimate that if the density of industrial robots increases by 10% in OECD countries, then offshoring decreases by 0.29%. Furthermore, we investigate if the effects are particularly strong for certain industries. We find that the effect between robotization and offshoring particularly holds true for industries that have already robotized the most in relative terms. We conclude by discussing several policies that can help developing countries to tackle the disruptive effects of automation and safeguard their future economic development.

Real investment within non-financial corporations has been steadily decreasing in the last fifty years, as increased payout to shareholders is seen as one of the main causes . Chetty & Saez (2006) argue that taxes on profits, dividend payout and capital gain also have a negative impact on payout, and thus real investment in non-financial firms. However, it is unknown if corporate governance can explain this relationship between taxes and real investment. An agent-based financial market was built to research which types of corporate governance can explain the effect of taxes on the real investment rate. The results show that a corporate governance based on maximizing shareholder value and minimizing interest payments can explain the effect of taxes on the real investment rate. To further define the effect of corporate governance on the real investment rate, an agent-based macroeconomic model can be built that would incorporate more feedback loops of investment and payout and endogenous growth of earnings of firms.

In the past years there has been increasing awareness about the benefits of collecting and using more sensor data for businesses. This has led firms to look for data outside of their boundaries and use some data commercialization mechanisms such as data brokers, and open or privately-owned data marketplace. However, these exchanges solutions are controlled by companies which have a commercial interest that differs from users, leading to lack of transparency and lack of protection of data, loss of data ownership by the provider and no guarantee of fair pricing. These centralized data exchanges call into question the willingness of both data providers and data users to share data. As an alternative, blockchain technology can be used to reduce the control and interference of any firm, leading to a more peer-to-peer and transparent data marketplace. To improve coordination between stakeholders and to enhance a more automated marketplace, the system should be context-aware. The main contribution of this thesis is a proposition of blockchain-based components integrated within a context-aware decentralized data marketplace. Other parts of the system are highlighted, as they need to be subject to more research in order to achieve a fully functional and complete system. Finally, guidelines are suggested for generalization to other types of data and ecosystems.