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KLM Cargo would like to improve short-term resource scheduling at their World Port cargo handling terminal, in order to increase both quality and efficiency of their service. Forecasts of air cargo demand could support decisions on manpower deployment. This thesis contains a forecast comparison study for short-term forecasts of the amount of departing cargo from World Port in kilograms per day, based on historical booking data. A single regression forecast model on pre-departure booking levels came up as top-performing model, capable of predicting cargo demand per day with only 3% mean absolute percentage error. This thesis also examines departure behaviour over the course of each day of the week. Furthermore, arrival behaviour is analysed, which is defined as the connection between the departure flow and several arrival flows at the terminal. The main conclusions out of the behavior analyses is that cargo departure is unevenly spread out over each day and that cargo is delivered at World Port relatively far in advance of planned departure.

Technical, social and environmental developments are driving energy system stakeholders to reexamine how their energy needs are met. The conventional method of building large centralized generators to meet a community's energy needs is being questioned as smaller energy sources become more efficient and economical. These smaller sources, commonly referred to as distributed generators (DG), have the potential to transform the structure in which energy is produced, distributed and consumed in a community. The research presented here looks at how the decisions and interactions among diverse household energy consumers shape their community's energy system evolution after DG is introduced. To gain a deeper understanding of this transition a model is designed to explore the relationship that social and environmental factors have on the deployment of DG. The development of the model is approached from the perspective of design science research which forms the structure of this report. Agent-based modeling is the methodology used in the analysis since the nature of the system under study evolves through the interactions among diverse consumers. The model is positioned to be able to explore how factors like demand, product preference and pollution concerns can impact the market share of DG in the next 20 years. It does this by comparing the diffusion of DG technologies in competition with electricity supplied by conventional sources on the grid. The input parameters for the model are throughly grounded in the latest research on energy systems to give relevancy to the results. The consumer agents in the model each have attributes that concern how they satisfy four different needs related to cost, environment, social identity and technology preference. The decision making of an agent is governed by their relative uncertainty surrounding how their energy source is helping to meet these needs. The results demonstrate that there is a relationship between social and environmental criteria on the deployment of DG. This is done by using the model to perform what-if evaluations of energy system developments across uncertain futures and to explore the drivers behind the changes. Factors like peer group size are shown to influence rates of adoption. Consumers with strong product preferences or environmental concerns are found to be unswayed by price concerns or the decisions of their peers in choosing their energy sources. The simulations show the differences in energy source choices across a range of energy price and economic development scenarios while accounting for a consumers' economic, environmental and social network situations and product preferences.