ProRail is the railway management company of the Netherlands. It is responsible for the construction, maintenance, management, and safety of the Dutch railway system. ProRail has as a goal an annual five per cent increase in the number of transported passengers up to 2020. This goal cannot be reached with the current way of transporting passengers. There is no room to expand the infrastructure, so the manner in which it is used must change. This means that during the decision making, the impact on individuals connected to the system must be taken into account. ProRail makes these decisions in collaboration with other actors. This means that ProRail is involved in a process of multi-actor decision making. This is an uncertain process. The uncertainty stems from three main causes: substantive uncertainty (ambiguity and lack of information on a project), strategic uncertainty (unpredictable behaviour of other actors), and institutional uncertainty (unknown decision making procedure). Because the Dutch railway network is a socio-technical system, the substantive uncertainty stems from social elements, technical elements, and exogenous elements. As no clear definition has been found in literature, this research proposes a definition for these elements based on the way in which an element can respond. A degree of freedom (DOF) is defined as the possibility to respond in an allowed way that is not included in the task description of the element. Technical elements are designed with zero DOF and social elements with more than zero DOF. Exogenous elements are elements that are not designed in the system but do influence it. In this research the value of the HAZOP methodology for decision makers at ProRail involved in multi-actor decision making is assessed based on literature and a trial application. To make this assessment the causes of uncertainty in decision making in projects initiated by ProRail are investigated. Second, the methods and practices currently used by ProRa
A financial feasibility study into the development of a 'sustainable fuel from digested manure' system for the Salland region within the Province of Overijssel in the Netherlands. This is a specific variant of a green gas hub system, which produces sustainable fuel as end product. To deal with challenges and uncertainties in the development the 'least regret network scenario analysis' methodology is used. Within this scientific paper the application of the 'Least regret network analysis' methodology to the development of a 'Sustainable fuel from digested manure' system is described. Based on this, an evaluation is made about the methodology's usefulness for the development of physical energy infrastructures in general.
Economic importance for the indium mineral has been growing in recent years and indium is usually referred to as one of the technology minerals. High tech industries use indium in their application because of the special properties: transparency and electrical conductivity. Indium demand has more than doubled in the last decade and is expected to grow more in the near future. Indium is not found in its pure state in the nature which makes indium supply complex because it is by-produced from zinc. Indium supply could become problematic in the future because of high demand, the high dependence on zinc supply and the market functioning of indium. The objective of this master study is the exploration under deep uncertainty of plausible futures of a system (indium) that has an important dependence on the function of another bigger system (zinc). To achieve that a simulation model was built to explore uncertainties and to analyse what causes scarcities. The purpose of the model is to generate thousands of plausible futures in order to create and capture behaviours of interest and to explain them. Indium and many other by-produced minerals have similar market conditions and expectations of growing demand hence; the purpose of this study is also to give general insights in by-produced systems and to produce a model that is generic enough to explore other types of by-produced minerals. The methods used in this thesis are System Dynamics (SD) and Exploratory Modelling and Analysis (EMA). These methods are used together as Exploratory Systems Dynamics Modelling and Analysis ESDMA for the building of the simulation model (SD) and the exploration and analysis (ESDMA) of plausible future situations. ESDMA is a new multi-method and a special emphasis is on using and testing developed tools of ESDMA and to give insights into the usefulness of the method for analysing uncertainties of systems under deep uncertainty. The model built for this study is a generic model capturing the dyn
Decision-making for flood defense while facing a considerable change in driving forces demands other methods than the traditional approach of forecasting and optimal policy selection. Exploratory modeling can be a candidate for helping adaptive policymaking to deal with the uncertainties that confront decision-makers. In adaptive policymaking where changes , policies are considered that respond to changes over time. This thesis addresses the question whether exploratory modeling is appropriate to support the design of flood defense strategies and in particular to assess the value of flexibility in such designs. The literature review of this thesis explores the concept of flexibility and shows that exploratory modeling as a method for handling uncertainty can contribute to system control as well as system resilience, and to scientific analysis as well as process management. The case study of this thesis demonstrates the application of exploratory modeling to flood defense strategies. It shows (1) how alternative strategies can be compared and evaluated while considering seven uncertain system parameters, (2) how the relative performance of strategies can be expressed as a regret value, and (3) how these values can be visualized to let decision-makers see how changes in parameters impact on their regret of decision-maker. These 3 goals reached by an exercise of exploratory modeling. Interpretation of computer model of a pre-investment analysis leads to selection of most robust strategy. Beside the help of visualization technique to see and compare performance of different strategies, a tool developed that counts and compare performance of strategies based on their regret value. I call this tool “Regret Frequency Table”. After finding the robust strategy which in this case was “Dike relocation” there was a need to see is this strategy flexible in terms of design to let future developments or not. Therefore based on interviews and gathered information, a decision tree h