Negotiations in urban development projects can take a long time, going back and forward to the designer many times, even leading to stalemate situations. Requirements can be set intuitively, without knowing what the side effects of the constraints will be for the urban development. On top of that, approval of all stakeholders is needed to be able to implement the design. An integral process of decision-making, with cooperation, open communication and considering everyone’s opinion, seems to be challenging when it comes to urban development projects.

Current decision-making methods, such as Linear Programming, are lacking in terms of cooperation between parties, only finding a feasible design for a group of stakeholders and satisfying one party. Considering preferences helps overcome this problem. In this thesis, an a priori design optimization approach (PDOA) has been developed as decision support system, applied and tested on a real-life urban planning case, Waelpolder.

The PDOA returns a quantitative design (Program of Requirements) for the to be developed area which is considering preferences and wishes of stakeholders a priori, before a design is generated. By basing the design on preferences of stakeholders, intuitively determined requirements are avoided and designs will be more compliant to stakeholders’ needs. The PDOA generates a design by optimizing on overall preference. So, finding a feasible solution is guaranteed, on top of that the approach is able to find a most desired group solution.

The Preferendus (based on Tetra) method, where stakeholders do not make the same amount of compromises in a design, is preferred to use in the PDOA rather than the goal attainment method, where stakeholders do the same amount of compromises. This is mainly because the Preferendus returns more extreme designs and can better show the impact of adjustments in requirements in comparison to the more moderate designs generated with goal attainment. In addition, the stakeholders of Waelpolder indicate that it is realistic that one party should do more compromises than another party. Therefore, the Preferendus is better able to support decision-making and open up discussions.

The PDOA allows a new direction in decision-making, where negotiations become an open glass box rather than a black box. Openness & transparency, which is one of the fundamentals of the PDOA, takes away strategic play between stakeholders. Stakeholders are forced to express what they want upfront, before any design is created.

The approach supports negotiations in decision-making as it provides insights in a rapid and simple manner. The tool allows requirement modification, thereby showing the impact of certain requirements and presenting alternative possibilities. In addition, long negotiation processes where stakeholders need to get aligned are avoided because of the required openness and adaptability of the tool.One of the stakeholders within Waelpolder is willing to implement the PDOA within its organization as slip school model in urban development projects, to investigate the effects of adjusting requirements. Due to the iterative value of the approach, the stakeholders are positive about using the approach in other area developments, in an early stage.

The construction and infrastructure industry has witnessed an increase in the need for an optimized mitigation strategy to combat schedule and cost overruns amid the rise in market competitiveness and more strict timelines and budgets. Moreover, projects nowadays are more complex in terms their scope and requirements which ultimately drives more innovative and efficient solutions to enhance project risk mitigation. Typically, Monte Carlo (MC) simulations with different combinations of mitigation measures are performed until a random set of measures is chosen to satisfy the targeted budget. Several attempts aimed at optimizing around different objective functions to obtain more efficient strategies that would demonstrate the dynamics faced on the construction sites(Safaeian et al., 2022). The main flaw of all attempts were the absence of the goal-oriented control behavior of the project manager who would only opt to the optimal mitigation strategy given a risk event and cost overrun. The closest attempt was demonstrated in the objective functions of MitC developed to keep projects within schedule given a set of optimized mitigation strategies (Kammouh et al., 2021). However, the aforementioned tool solely addresses the selection of mitigation measures in the case of project delays affecting a strict delivery date with a trade-off restricted to one criterion: cost. The development in this document demonstrates a tool with broader functionalities that aims at selecting an optimized strategy to keep the project within budget yet with further additional features that extend its usefulness to a multi-criteria approach that involves time delays, environmental impacts as well as noise disturbance. The usefulness of the Mitigation controller for cost is demonstrated using a real case of a construction project in the Netherlands with the analysis performed on the go yielding significant negative impact savings relative to current approaches used to maintain the project’s budget.

Why do engineers often design what people don't want? And why do people often want solutions that are not feasible? This is because the current design and decision support optimisation methodologies are one-sided and ignore or fail to capture the dynamic interaction between people's preferences (desirability) and technical assets' performance (feasibility). Furthermore, most methodologies contain fundamental problems and often cannot achieve a single best design solution. Moreover, the offshore & dredging engineering (ODE) industry can significantly benefit from multi-objective design/decision optimisation as projects become larger, more stakeholders are involved in concurrent design/decision-making, and new technologies emerge.

To solve the shortcomings given the current momentum within the ODE industry, a truly integrative Open Systems Design (Odesys) methodology is proposed in this thesis. For this purpose, a new multi-objective optimisation method IMAP is introduced which is operationalised in a Python-based software tool called Preferendus, including a new inter-generational Genetic Algorithm (GA) solver.

The application and added value of the Preferendus/IMAP are validated for two demonstrators within the maritime contractor Boskalis.

The first validation case concerns planning and design optimisation in the development of offshore floating wind farms, focusing on scheduling and mooring design. For this purpose, an integration was made with the wind turbine simulation tool OpenFAST. The new Preferendus/IMAP significantly improved the overall tender performance by: 1) providing initial design approaches within a few hours, where currently tender teams spend days working on design alternatives that, in hindsight, were suboptimal; 2) removing tender team bias from the design process and finding design solutions that were otherwise unfairly disregarded; 3) open glass-box modelling support for concurrent design between the asset owner and the contractor.

The second validation case is a decision support optimisation application of a dredging production in which multiple vessels jointly execute a dredging project. Due to different types of disturbances, these vessels often have to wait for each other, reducing the overall efficiency of the project. Current expert-based optimisation approaches are limited in their ability to adjust best-for-project. The new Preferendus/IMAP shows a clear improvement and finds solutions that significantly reduce waiting time while simultaneously achieving high production levels. Moreover, the Preferendus/IMAP outperforms single-sided optimisation on production alone by achieving similar high production levels while also improving other objectives such as CO2 emissions and vessel efficiency.

Steps for further development include: 1) improving OpenFAST integration and modelling; 2) addition of fatigue loading in the anchor design; 3) improving discrete event simulation scheduling modelling; 4) improving runtime by exploring other algorithms and/or programming improvements.

This research describes a way to determine the reliability of coastal pump stations - from which their environment is influences by climate change – in time. Last decade, the challenge within the hydraulic infrastructure network shifted the focus from expanding the infrastructure inventory towards the maintenance and management of the existing network. The reason for this tendency is that the predominant part of the Dutch infrastructure inventory is approaching its end-of-life while their functions are still required. As a consequence, the asset owner is confronted with the challenge to choose the suitable maintenance action in order to extent the assets life-cycle.

Infrastructure projects in the construction industry are complex. This complexity leads to unavoidable changes in scope. When scope changes are not formalized and managed correctly, it is a risk contributing to project failure. A scope change that is not formalized is scope creep. This research looks at the causes and effects of scope creep in both literature and practice. Furthermore, a case study is conducted to recommend a course of action to anticipate scope creep and its effects.

Internal and external uncertainties like structural integrity, load, demand, weather conditions and spatial planning have an impact on Infrastructure assets. Incorporating uncertainties and flexibility to decisions by means of more information becoming available, adds value to new investments, life time extended maintenance and replacements. The traditional way to evaluate such projects, the Life Cycle Cost Analysis (LCCA) based on traditional Net Present Value (NPV) techniques fails to incorporate flexibility, and hence ignores extra value from expected future information. Decision Tree Analysis (DTA) and Monte Carlo Analysis (MCA) can actually allow for valuing flexibility in investments.
If investments are subject to non-diversifiable uncertainties, investors should be compensated for associated risks by using time dependent risk adjusted discount rates for valuation practices. A discussion is taking place on difficulties that occur if this principle is applied to infrastructure investments. Conducted literature research shows that current techniques to correct for non-diversifiable cannot be applied directly to most engineering valuation problems.
A MCA on investment alternatives regarding to the replacement of a bridge in the municipality of Amsterdam demonstrates how expected investment values can be calculated taking along multiple uncertainties and flexibility. Although the value of flexibility is always equal or greater than zero, case study results show that incorporating uncertainty and flexibility in the analysis can also affect the NPV negatively.