Megaprojects frequently face cost overruns and schedule delays, a pattern known as the iron law of megaprojects, which persists partly because traditional determinis tic or probabilistic planning methods are inadequate for managing deep uncertainty. This form of uncertainty arises when the probability, timing, or impact of key events cannot be reliably estimated, often leading to unrealistic schedules and ineffective risk responses. This study investigates the question: How can Exploratory Modelling and Analysis and Dynamic Adaptive Policy Pathways be applied to improve schedule robustness in infrastructure construction projects? The research focuses on the Schiphol bridge reconstruction, which is part of the Veenix A9 BaHo project and is conducted in collaboration with Count & Cooper. A Discrete Event Simulation (DES) model is built in SimPy and structured using a task dependency graph de rived from the project’s original schedule via NetworkX. The model is sampled 10,000 times under baseline conditions using Latin Hypercube Sampling. In scenario discovery, Patient Rule Induction Method (PRIM) is used in combination with a scaling function identifying six high-impact scenarios, which serve both as inputs for robust policy search and as Adaptation Tipping Points (ATP) for the Dynamic Adaptive Policy Pathways (DAPP) schedule. Robust mitigation strategies are derived using a Multi-Objective Evolutionary Algorithm under the Multi-Objective Robust Decision Making framework, with a second PRIM experiment selecting four final robust policies. These policies correspond to at least one of the high-impact scenarios and form the backbone of a conditional DAPP schedule. The DAPP schedule is evaluated against a static baseline using 5,000 DES simulations with identical uncertainty sampling. In 20 comparative runs, it reduced project duration by an average of 67 days and cost by approximately e97.5 million on the entire project schedule. All three robust policies include the measures new design, overtime labour, and electric machinery, suggesting that a focused subset of actions can improve resilience even when the future is highly uncertain. Unlike prior Decision Making under Deep Uncertainty (DMDU) applications, which often focus on long-term or high-level strategic planning, this study embeds adaptive logic within a highly granular, task-level construction schedule based on real project data. This approach raises methodological challenges in how adaptation tipping points are defined, triggered, and monitored within network-based simulation. The findings demonstrate not only the feasibility of combining Exploratory Modelling and Analysis (EMA) and DAPP in operational construction settings but also the need for further research into real-time scenario recognition and policy switching mechanisms under uncertainty.

This research examines how dynamic façade variables influence the embodied and operational carbon of mid- to high-rise residences during the early design phase, addressing growing environmental concerns from urban densification. By analysing façade design and parametric architectural strategies, the study aims to identify sustainable solutions that minimize environmental impact while complying with regulatory standards.

A combination of literature review and computational simulations was used to evaluate different façade typologies. The literature review identified common façade systems and their carbon footprints, while a case study applied this knowledge to a realistic scenario. Using parametric modelling tools such as Grasshopper, energy simulations were conducted to assess carbon impacts. An optimization process then identified the most sustainable façade configurations, highlighting key trends and design considerations.

The findings reveal that material selection, façade design, and energy efficiency significantly impact the total carbon footprint of buildings. Among the façade types analysed, aluminium unitized façades have the highest embodied carbon emissions due to the carbon-intensive nature of aluminium production. In contrast, prefabricated timber façades have the lowest embodied emissions, benefiting from a lower carbon footprint and carbon sequestration potential. Concrete façades fall in between, with their high weight contributing to greater embodied carbon despite lower emissions per kilogram. The relationship between window-to-wall ratio (WWR) and embodied carbon varies by material; a higher WWR increases emissions for aluminium and timber façades, whereas for concrete façades, it reduces embodied carbon as glass replaces carbon-intensive concrete elements.

Operational carbon emissions are highly dependent on façade orientation. North-facing façades require the most heating due to limited solar exposure, while south-facing façades benefit from passive solar heating but require more cooling. The most effective way to reduce operational carbon is by improving glazing insulation (lowering U-values), especially in colder orientations. Increasing the Rc-value of insulation has only a minor effect when WWR is high, as window heat transfer dominates. With an assumed 2% annual improvement in energy efficiency and grid decarbonization over a 75-year lifespan, operational carbon emissions are expected to decrease by 50%, making embodied carbon an increasingly dominant factor.

Considering both embodied and operational emissions, timber façades emerge as the most sustainable option, particularly when paired with optimized glazing and insulation values. Aluminium façades have the highest total carbon footprint, with embodied emissions accounting for nearly half of the total impact even in efficient configurations. Concrete façades present a unique trend, where reducing WWR can sometimes increase total emissions due to the high embodied carbon of concrete relative to glazing. These results emphasize the need for an integrated approach to façade design, balancing material selection, insulation levels, glazing performance, and orientation to minimize total carbon impact.

This study acknowledges several limitations, including reliance on a single simulation program, uncertainties in future energy grid decarbonization, and a limited range of material and façade options. Future research should explore additional materials, occupant behaviour models, and renewable energy integration to enhance sustainability assessments. Further validation using multiple simulation methods, diverse climate models, and broader material databases would improve reliability and deepen understanding of façade performance across different environmental contexts.

With decision-making becoming increasingly data-driven in early design stages due to global environmental challenges, and qualitative metrics remaining crucial in facade design due to their huge architectural impact, performance-driven design exploration frameworks are emerging as powerful tools to explore vast design spaces based on geometry typology and approximated performance. However, those used in facade design based solely on Self-Organising Maps (SOM), face significant challenges in computational efficiency. While more advanced frameworks used in the AEC sector combining SOM and Multi-Layer Perceptrons (MLP) address this, they still face limitations in prediction accuracy, convergence speed, interpretability, reliability, and usability, reducing their effectiveness in decision-making. This thesis aimed to overcome these limitations, by developing a novel framework integrating SOM and Kolmogorov-Arnold Networks (KAN), applied in the design process of an aluminium-based biocomposite curtain wall facade. The results demonstrate that substituting heavy performance simulations with fast approximations using KAN leads to significant enhancements in computational efficiency. In addition, comparative analysis revealed that KAN outperforms MLP in prediction accuracy on highly-complex performance metrics, with much faster convergence and smaller architectures. Furthermore, KAN proved to be faster and more intuitive to train, as well as more consistent in predictions. Moreover, KAN enhanced interpretability between geometry and performance, enabling designers to focus on relevant design variables and adjust them strategically toward optimal performance, providing an integrated solution with transparent decision-making and faster processing compared to traditional sensitivity analysis tools. Finally, a novel approach to design exploration has enabled the integration of less-geometry related design variables, enhancing optimisation capabilities, as well as proven to balance human-AI interaction more efficiently than traditional frameworks, making design exploration more interactive, thereby more effective and intuitive. Ultimately, the SOM-KAN framework has proven to advance the facade design process by facilitating more efficient decision-making in early design stages, leading to superior architectural and sustainable solutions.

The evaluation process of tender bids in area development by Ontwikkelingsmaatschappij het Nieuwe Westland (ONW) is addressed, which currently uses non-quantified selection criteria to find developers, one of them being 'Vision of Sustainability'. With sustainability gaining importance, this research aims to enhance the tender process by quantifying sustainability criteria and incorporating them into a preference-based decision-making model. The research question guiding this thesis is: ‘How can sub-criteria of sustainability in area development be quantified and implemented into a preference-based decision-making model?’

The quantification of sustainability sub-criteria was performed using the DCBA method, which defines forty sustainability characteristics in area development. Each characteristic is assessed across four scenarios (D, C, B, and A), ranging from least to most sustainable. The sustainability scenarios were quantified based on costs and revenue across different variables (house types). For this thesis, a selection of these characteristics was elaborated as a prototype. The Preferendus Tool, combining Preference Function Modelling and Tetra software, was used to create a model that optimises housing differentiation and ranks tender bids based on stakeholder preferences.

User experiments revealed that while the model's complexity requires technical support, it holds value primarily as an assessment tool, to rank different developer bids, rather than a design tool. The model's potential was hindered by variability in optimisation results, suggesting the need for further mathematical refinement. Incorporating sustainability into an optimisation model shows promise for enhancing the evaluation of sustainability in area development tenders. Future recommendations include incorporating updated sustainability criteria, expanding user experiments with actual stakeholders, and iteratively improving the model.

In conclusion, the thesis demonstrates how sustainability sub-criteria can be quantified and integrated into a preference-based decision-making model, providing a structured approach to optimise and evaluate sustainability in area development. The findings support the development of a model that can potentially improve the current tender evaluation process by offering a quantifiable assessment of sustainability.

Purpose - This development project is focused on the combination of project scheduling and risk mitigation for large infrastructure projects. The aim of this project is to develop a model that can automatically restructure the network schedule to enable that delay is mitigated as much as possible while the effects on multiple project criteria are minimized. This model will be developed as an alternative perspective on the original Mitigation Controller.
Approach - Simulation, optimization and multi criteria evaluation are combined to reflect reality and the human goal oriented behavior during the decision making process in large infrastructure projects. Including optimization in a simulation process ensures that uncertainties and risks are reflected in optimization outcomes. Embedding a multi criteria evaluation in the optimization establishes the reflection of multiple project criteria in the model. One of the largest ongoing infrastructure projects in the Netherlands is used as a case study to validate the model and establish its practical relevance.
Findings - The verification and validation on the applied case study showed the theoretical and practical feasibility of the developed model. The probability to achieve a certain project target duration increases significantly for the highest aggregated preference of the applied mitigation strategy.
Originality - The proposed development proposes an alternative perspective on the Mitigation Controller as it examines the optimal network logic rather than crashing activities to minimize project delays. Not only cost, but also multiple other project criteria can be taken into account in the optimization, such as traffic hindrance or stakeholder effects. On this wise Monte Carlo Simulation (MCS) is not only combined with optimization, but also with Multi Criteria Analysis. And lastly, a Genetic Algorithm is applied to minimize the project delay for the least effects on project criteria in each run in the MCS.
Future work - To improve the body of scientific knowledge on this topic, further research on how to combine the model on network restructuring and the original Mitigation Controller on activity crashing into one overall automated computer tool would be of interest. This tool can then propose the best mitigation strategies both on the structure of the network as on the lead times of activities to mitigate project delays, which would be a valuable addition for project control in the construction industry.

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.

The growing need for sustainability and circularity in the construction industry has given rise to the demand for proper sustainability valorisation and evaluation methods. Investors and project owners need sound rationale for their returns on investment made into sustainability – in financial, environmental and social benefits achieved. Promoting sustainable building methods and the use of sustainable products and materials demands a tool that can justify the investment into them. This thesis addresses this challenge and provides a decision making tool that can evaluate and compare the true lifecycle values of construction projects, in order to allow investors and project owners to compare between design options and select the most optimum choice based on their preferences. Thus the model gives a direct relation between the investment and total returns, and acts as a guide for investors on the selection of design and material choices for the needed level of sustainability in their projects. Termed as the “Total Value for Society” model, it uses Multi-criteria Decision Analysis methodology to pick the construction project with the most “true lifecycle value” out of multiple options of the same functional equivalence. The tool is applied to a test case provided by Heijmans. The results of this model are verified and validated through stakeholder interviews. A sensitivity analysis confirms the robustness of the model. An expert review performed using stakeholder interviews validates the usefulness of the model and its impact on the design selection process. The validation shows that the model’s usefulness to owners/investors, contractors and designers indeed matches with their expected uses of the model and fulfills its needs as established by the same stakeholders earlier. Finally, the report concludes by answering to the main development statement - The Total Value for Society Model, by comparing design options based on their lifecycle performances on financial, environmental and social sustainability aspects, and showing the most optimum option for the given preferences on the relative weights of criteria, can enable investors to evaluate and compare the true lifecycle values of construction projects, and doing so, guide their investment decisions. The thesis however is just one step towards rationalising investment decisions regarding sustainable construction, and it further provides new avenues for future research and developments.

In general, costs are an important aspect long before the construction of a civil project starts. In the conceptual phase, which is the first phase of a civil construction project, little cost information is available, and the cost estimate is at that stage of project development less accurate than when the project is almost completed. When a cost estimate is made with a conventional method, such as SSK, in the conceptual phase of a civil construction project, the Association for the Advancement of Cost Engineering, AACE, argues that the error of a cost estimate compared to the real price can be 15-50%. The lower accuracy in the conceptual phase is caused by several obstacles: a lack of estimating experience, lack of information, and a method that cannot calculate an accurate cost estimate. Besides, the conventional approach is time-consuming. Machine learning models can overcome these obstacles. This research is initiated by a practical problem within the company Witteveen+Bos. Witteveen+Bos uses a conventional approach for cost estimates and wants to improve the accuracy and calculation time of cost estimates of road bridges in the conceptual phase of a project. This research aims to improve the cost estimates of bridges in the conceptual phase of bridge projects by use of the machine learning model called FALCON. This model is already successful applied in the field of cost estimates for construction project types. Improvement that should be realized in the reduction of the calculation time from hours to minutes. Besides the research aims to reduce the maximum deviation of estimates for road bridge projects to 30%. The FALCON model is not the only model to solve an estimation problem. Standard models, suitable for estimation problems, are multiple linear regression, K nearest neighbors (KNN), and decision trees. The results show that the average absolute deviation is 34% for the KNN model, 36% for the decision tree regression model, 57% for the multiple linear regression model, and 24% for the FALCON model. All 4 models are able to calculate results within a couple of minutes. FALCON is the most accurate model The error of the individual predictions of the FALCON model roughly corresponds to the bandwidth of the AACE (15-50%) for the conceptual phase of a project. The validation of the FALCON model is realized through interviews with cost estimators of Witteveen+Bos. These interviews showed that the realized results meet the expectations of the interviewees. Besides, the interviewees are willing to implement the model in practice. In the end, it must be concluded that the FALCON model can calculate a cost estimate, of bridge projects in the conceptual phase, more quickly, and with a comparable accuracy level as with conventional methods that are used today. For future research, it is recommended to use a larger dataset that has less variation. The used dataset consists of 39 bridge projects. Besides, it is recommended to use international standards for data structuring instead of national standards.

Currently, Ordinary Portland cement (OPC) concrete accounts for the world's 8-10% anthropogenic carbon/ greenhouse (GHG) emissions, where 90-95% of the emissions are due to the production of OPC. A 200% increase in OPC demand is projected by 2050 from 2010 levels. Therefore, it is urgent to reduce these emissions arising from its production. Alkali-activated concrete (AAC) has gained significant attention from researchers worldwide for being a sustainable alternative to OPC concrete. AAC completely substitutes Portland cement with industrial by-products, such as BFS (blast furnace slag) and FA (fly ash). An alkaline activator is used to activate the BFS or FA to form a hardened binder. Despite comparable or even better technical performance than OPC concrete, worldwide usage of AAC is not yet observed. The challenges for the commercial scalability of AAC have rather increased with the onset of coal phase-out law in many parts of the world. This limits the availability of FA and thus forcing researchers to find alternative binders to make AAC, such as using BFS as the sole binder. Therefore, while other technologies develop and blast furnaces are phased-out, it is essential to effectively use the available BFS from the steel industries to produce alkali-activated slag concrete (AASC). Although various studies proved that AASC has significantly higher compressive strength than OPC concrete, numerous researchers have reported poor workability and rapid-setting. AASC also has higher drying & autogenous shrinkage. However, despite having a higher autogenous shrinkage, AASC shows moderate-low cracking potential compared to OPC concrete. A self-compacting AASC is expected to follow the same behaviour. However, very few studies have focused on the self-compacting behaviour of AASC, which, therefore, limits AASC for various structural applications where an SCC is required. In order to upscale the commercialization, a comprehensive study is needed that provides detailed insights on the development of a SCAASC (without FA) with an appropriate mixture-design and optimization procedure, desirable fresh properties, hardened properties, sustainability analysis and commercial viability. Hence, the main research question is: Is it possible to develop a self-compacting alkali-activated concrete made from 100% BFS as a binder and currently available raw materials without using any FA? If so, What is the optimal mixture design that results in desirable fresh and hardened properties suitable for real structural applications? Is the developed concrete sustainable and ready for commercialization? And if not, what are the commercial barriers? Attempts are made to answer these questions through various laboratory experiments and stakeholder interviews. The final mixture design is developed in a 5 step optimization process. It involves studying the effect of a particle packing based mixture design method, curing temperature, silica modulus (Ms) content and different admixtures on the workability and compressive strength (1, 7 and 28 days) development of the SCAASC. In this process, an optimal mixture design with desired workability and strength properties is developed and an optimal curing and mixing regime is also suggested. This developed SCAASC is further tested for all the SCC like fresh properties. Various hardened properties such as compressive and splitting tensile strength, elastic modulus, Poisson’s ratio, bond strength to reinforcement, free drying and autogenous shrinkage are also investigated at different ages for different curing regimes. The cracking potential is also investigated for the first time using a TSTM (Temperature stress testing machine). Furthermore, the environmental footprint and material costs are evaluated through an LCA (life-cycle analysis). Lastly, stakeholder interviews were conducted with an international and local construction company to validate the developed SCAASC and exploit its commercial viability. From the experiment results and conclusions drawn from the interviews, the developed SCAASC is commercially scalable for its desirable fresh and hardened properties. It can be used in various industrial applications where an SCC is used. The 80% less CO2 emissions from a reference OPC concrete further bolster its market acceptance. However, high market costs and lack of available standards are still the major challenges.

Construction projects are notorious for going over budget. It is important for a project manager to have as much grip on the financial situation of the project as possible. In order to deal with the uncertainty that is prevalent in a construction project, a project manager should be able to steer project cost back towards budget.
To provide project managers with clear insight on the mitigation strategy that would be advised to be used. An adaptation of the mitigation controller (Kammouh et al, 2021) was developed in order to provide insight into mitigating project cost, as opposed to the original focus on project planning.
By adding goal focussed optimisation to a Monte Carlo Simulation, the human element of the corrective behaviour that is shown by a project manager is taken into account. This removes the fundamental modelling error that is present within traditional probabilistic approaches.
Through a real-life case from an infrastructural project in the centre of the Netherlands, its usefulness was shown. The Cost Mitigation Controller identified the most effective mitigation strategy for the projects current situation. The results from this study were evaluated using an experiment that was done with practitioners that work in project management roles. As a result it was shown that the Cost Mitigation Controller outperformed all project managers that were involved in the experiment. The probability of staying within budget for the Cost Mitigation Controller (74.6%) was around 4% higher than those of the three top performing project managers (70.3%).

Currently, the inland shipping industry is not sufficiently incentivised to invest in sustainable technologies in order to reduce their emissions. The industry itself wants to be more sustainable, but there is insufficient financial room to invest in alternative technologies. Besides, public authorities try to achieve a reduction using command-and-control policy instruments. Therefore, this study aims to gain insights into the relationship between policy instruments and incentivising technical alternatives. In this study, emission-reducing strategies have been identified using a mixed-integer linear programming (MILP) model based on the fleet owned by the Port of Rotterdam. As a result, this study has identified two effective strategies – environmentally differentiated port dues and environmental fees. The effectiveness of the environmentally differentiated port dues can be allocated to the degree of pollution control. The implication of an environmental fee on carbon dioxide (CO2) emissions, has led to a reduction in either greenhouse gas and pollutant emission.

Nowadays, the term "automation" is gaining extensive interest in both industrial and business fields. There is also a trend of achieving deeper automation in the business processes, which is a part of Business Process Automation (BPA). Among various BPA technologies, Robotic Process Automation (RPA) has attracted increasing research attention. RPA is an emerging form of business process automation technology. The practice of performing RPA results in the deployment of attended or unattended software agents to an organization's environment. ChatBots, as the outcome of the development of artificial intelligence (AI) technology, has been regarded as a potential and powerful tool to achieve such cognitive automation in service-oriented business processes. However, limited knowledge on the design road-map and approaches of ChatBot implementation in the Business process hinders the ChatBots further deployment into company practical operation. Herein, the research is focused on the question: How should ChatBots be implemented in service-oriented processes? Chapter Introduction contains the introduction of this study, including the background, problem statement, research question, and thesis structure.
Chapter Literature Review describes the background of business process management (BPM), ChatBot technology, and the current state of ChatBot-implementation in business processes. BPM holds great potential for significant cost saving and productivity improvement, and the lifecycle of BPM serves as one of the guidelines for ChatBot-implementation methodology designed in the next chapter. Chapter Methodology Design introduces the requirements, design strategy, description approach, and validation and evaluation of designing a ChatBot implementation methodology. The requirements of methodology design are described according to the limitation of the current ChatBot-implementation methodology. Then the methodology design strategy is proposed. The methodology can be developed based on the BPM lifecycle, which ensures the business processes covered by the BPM lead to consistently positive outcomes and delivers maximum value to the organization, together with the Quark ChatBot implementation methodology. Following is the approach to describe a methodology. It is crucial to depict the methodology with clear logic. The analytical framework of Sol can be regarded as a suitable approach to describe the ChatBot implementation methodology. The framework consists of three parts namely way of thinking, working, and modeling. Methodology validation and evaluation are described at last. To check the validity and evaluate the methodology, it is necessary to use a case study to execute the methodology. By employing the method of case study, the methodology can be practically performed to automate a process by implement ChatBots. Accordingly, the validity of methodology can be examined, and the limitation of the methodology can also be explored, laying the foundation for the further improvement of the methodology. Chapter ChatBot-implementation Methodology designs the ChatBot-implementation methodology according to the design strategy and description approach in Chapter Methodology Design. It follows the analytical framework of Sol (1982). Chapter Methodology Evaluation evaluates the ChatBot-implementation methodology. The Call Management process of Philips IT Global Services was chosen as an example case to apply the ChatBot-implementation methodology to obtain an automated process.
In Chapter Conclusion, conclusions are drawn. Research findings answer the previously mentioned research questions. The major contribution of this research is establishing a theoretical ChatBot-implementation methodology for service-oriented process automation. Although, there still are some limitations of the work. For further research on ChatBot-implemented process automation, efforts can be focused on the executable process model, the business process model towards the ChatBot model, the extension of methodology application, the multi-process ChatBot implementation methodology, and ChatBot-based business process management.

An improvement study to the pre-processing process of Damen Shipyards Galati (DSGa) is performed since benchmark analysis shows that the current pre-processing process underperforms, compared to industry peers. The objective is to increase DSGa’s throughput and decrease space utilisation, such that pre-processing facilities S1 and S1a can be merged into S1a and DSGa meets the industry ‘standard’. Lean Manufacturing shows the potential to enable improvement and is adopted to initiate process redesign. The Lean principles drive the approach of this study, which studies the ‘current state’, defines improvement strategies and implements the strategies in ‘future states’. Finally quantitative analysis shows that the objective is met.

With the rapid development of real estate market, due to trends as the change in the way of working and automation/digitalisation of working processes, flexibility is one of the most important tasks for owner/investors of office buildings to invest in. Currently, 60% of the vacant offices are structural vacant. Flexibility could be used as an instrument to prevent future vacant office space. The flexibility to change characteristics of a building during its life cycle is called the adaptive capacity of a building. Adaptability can be regarded as way to make the real estate supply more dynamic and better able to cope with demand dynamics (static supply and dynamic demand) and in this way extend the functional lifespan of buildings. The problem is that real estate investors are hardly interested in investing in office buildings to increase the adaptive capacity. Thereby, is the most used financial method the DCF method which is currently insufficiently used to simulate the future uncertainty in an investment in flexibility on the longer term. This aim of this research is to illustrate how to cope with the future uncertainty in a new financial method. By using a stochastic approach with the Decision Tree Analysis the investors could be stimulated to invest in the adaptive capacity of office buildings and increase the adaptive capacity of a building to respond if the functional demand changes.

Purpose – One of the long-standing issues in the field of real estate management is the alignment of demand and supply. When building retrofit is initiated to comply with changing demands, the decision-maker has to deal with several, often competing, criteria. Smart systems have the potential to contribute to the process of optimally attuning the building with the changing demands, in other words, adding value to the building. To date, plenty of research exist on predicting the added value based on quantitative variables but fail to incorporate quantitative variables. The purpose of this paper is to provide a decision model that is capable of predicting the added value of smart systems in a building. It offers real estate managers a Preference Function Modelling (PFM) process, structure and model to approach a multi-criteria problem.

Design/methodology/approach – The PFM approach, an evaluation operational research methodology, is designed to help decision makers to choose the most preferred alternative from a set of already existing alternatives. A decision model is made in Excel that enables the construction of measurement scales to which linear algebra and calculus are applicable. A second research technique being used is the Lagrange curve. The model is tested on a single building in the portfolio of Schiphol Real Estate. Findings – The PFM model enables the decision-maker to establish a list of overall preferences scores of all relevant alternatives. The alternative with the highest overall preference score is predicted to add the most value to the building. The pilot study reveals that the procedure resulted in a result but a certain level of uncertainty exists. The cause of the uncertainty seems to be a lack of knowledge about the alternatives. Research limitations/implications – The Lagrange curve is a suitable technique for changing values of a variable. However, the curve tends to overshoot. Practical implications – The practical applicability depends on the selection of alternatives and knowledge available. If the alternatives are not known a priori, the PFM approach cannot be used and Preference Based Design (PBD) is more appropriate. If the knowledge available is low and difficult to improve the input is subject to uncertainty. The output may not project the actual situation. Originality/value – The research is based on the PAS approach from Arkesteijn et al. (2017). The PAS approach is a PBD methodology and is adapted to a PFM methodology in order to fit the intended purpose of the research. The PFM methodology takes both scaling and the Lagrange curve into account and along with an iterative procedure the decision-making process is well supported. The model provides a custom-made outcome, which is of high value for the decision-maker in the SHG case.