For the shared mobility service providers, it is essential to have an economic operation and therefore, it is necessary to maximise their fleet utilisation. Of influence on the latter is the availability of the spare parts. The topics for which spare part forecasting research is already performed are different in several ways compared to the field of free-floating shared vehicles. The many different users with each different driving skill, wear and tear by operation, events as vandalism and traffic accidents and constantly standing out on the street result in sudden failures of parts. Three different forecast models are presented in this paper. The effect of including demand generating factors in the forecast approach is analysed. The methods show that a better forecast performance can be achieved while including demand generating factors into the analysis. The forecast accuracy can be further improved and some suggestions for improvement are given in the paper.

This research lays forward a framework where supplier segmentation, supplier collaboration models, and supplier development strategies are integrated, specifically for remanufacturing. As remanufacturing is gaining popularity this pioneering research may serve as a first step towards more research for companies that are willing to drive remanufacturing across the whole supply chain. This research compares two proven supplier segmentation methods and links it with supplier new collaboration and existing supplier development strategies. As a result of this study, an integrated framework is proposed. For future research, the framework can be applied to other cases or the framework could be extended.

As the margins for improvements in the current freight transport system become limited, researchers address more and more the importance of collaboration between the actors which is crucial for the implementation of new, more efficient transport concepts as synchromodality. In addition, rail is concerned as a sustainable mode of transport that can also achieve economies of scale due to its ability to haul large quantities of goods. This study investigates cooperation policies that affect actors’ behavior to better utilize the rail use and lead to a more efficient system. We propose an innovative approach that combines gaming, simulation and optimization as a mixed method to test and evaluate these policies. The port to hinterland freight transportation system in the range of Port of Rotterdam is used as a case study. First, gaming sessions are organized in order to observe actors’ behavior and collect data. The game that is used was initiated by Port of Rotterdam, especially to identify the problems in this system. Subsequently, by assessing the observed data, a simulation model is developed and different policy scenarios are simulated to quantify their performance. In addition, the optimization model is developed, which sets the upper bound for performance and used as a solid base for comparison between the policy alternatives. Finally, the explanation of the difference between the policies’ and the optimized performance can give an insight on what are the root causes of the inefficiency, what is the best allocation of the resources and where the solutions should be focused.

Due to its archipelagic nature, Indonesia enforces to make substantial investments in its maritime sector, especially port infrastructure, in order to promote trade, economic growth, and ease disparity among Indonesia’s island regions. The challenge, of course, is that Indonesia has many ports to invest in and a limited budget; it must prioritize among various port development projects. The government, therefore, needs to choose wisely in order to avoid wasting money by constructing shipping network plans. One of the most important aspects in shipping network planning in Indonesia is designating which ports are to become international container gateways as these ports will play an important role in connecting Indonesia with foreign markets and therefore have a strong impact on its economy. To improve the current state of policy decision-making for the Indonesian shipping network, we hereby develop adaptive policy decision pathways focused on Indonesian containerized exports. The objective of this study is to build adaptive policy decision pathways that can cope with dynamic future uncertainties and results to help the government develop a robust network investment plan that helps the Indonesian government achieve its objective of improving connectivity and value-added exports through lower logistics costs. The main general finding is that flow pattern is very sensitive to demand volumes. Moreover, Minimum Cost-Flow (MCF) model used in this study is able to identify the flow pattern so that is very useful to analyze the potential of gateways. By applying this approach, we can analyze how the flow pattern changes from period to period and therefore may lead to different policy decisions. Having this approach in shipping network planning will help policymaker to have several predictive pathways and thus can support policy decision-making.

Aircraft component maintenance, repair, and overhaul (MRO) outsourcing is a rather new phenomenon. Nowadays, component support solutions that are flexible with predictable cost are pursued. In other words, the time to restore and cost of MRO are aimed to be minimised. This research project is collaboration with one of the worlds leaders in aircraft MRO: KLM Engineering & Maintenance. Notable is the unbalanced allocation of jobs to shops due to obsolete contractual agreements, whereas the flexibility to deviate slightly from these agreements is available. Several task to shop assignment policies are developed and evaluated using discrete event simulation. The policies are an extension of the classic assignment problem and are aspired to reduce either the time to restore a component, the cost of MRO, and seek for a solution that includes both attributes. The result of the deployed experimental design is a non-dominating set of solutions where the trade-off between cost and time is clearly recognised. A policy where both cost and time are considered, with emphasis on time, results in a reduction of both performance indicators. However, other policies perform considerably better in terms of either cost or time. The established Pareto front provides a proper basis for further optimisation and depicts the choices for the MRO provider clearly.

Efficient distribution is crucial in many industries today, especially in retail, where customer demand grows rapidly. The rise of online shopping and automation has led to a shift towards using Central Distribution Centers (CDCs). Retailers are part of this trend, which requires them to rethink how they handle their distribution. This study focuses on two main challenges: first, changing the distribution system to adapt to new CDCs, and second, dealing with the computational complexities when optimizing the distribution on a large scale. The aim is to design a large distribution system for the allocation of stores and the routing of vehicles. In this research, we introduce new elements to the mathematical model, which are mostly focused on the routing of vehicles. We also improve the computation by implementing a start solution, adjusting the parameters of the Gurobi optimization tool and including a callback function which bounds the run time. This model will determine the routing decisions for demand from several DCs to the stores and find the optimal vehicle routes. Through a case study at a company in the Netherlands, this paper provides insights into the changing world of distribution systems. It offers practical solutions for businesses trying to make their distribution processes better. The analysis revealed that consolidating demand at CDCs can lead to significant cost improvements while altering transport costs between CDCs and RDCs and adjusting order volumes can influence delivery strategies. Despite the absence of direct relationships between routing decisions, distances, and volumes, a logistic regression model provides guidance.

In the contemporary global environment, supply chains are increasingly vulnerable to disruptions, with environmental and supplier disturbances posing frequent challenges. This research aimed to understand the influence of a systematic supplier selection approach, combined with an effective risk mitigation strategy, on supply chain performance amidst supply-side disruption risks. We identified a significant gap in current supply chain management: the lack of a structured supplier selection methodology, which can lead to inefficiencies and performance declines. To address this, our study employed the best worst method (BWM) for systematic supplier assessment and ranking. Simultaneously, a discrete-event simulation model on the Simio platform was utilized to emulate supply chain dynamics, factoring in various suppliers and potential disruptions. Our findings, contextualized within the polymer industry, highlighted Material Quality, Supply Reliability, Price, and Lead Time as the most critical criteria for supplier selection. The BWM rankings correlated strongly with actual supply chain outcomes, emphasizing its effectiveness. In terms of risk mitigation, flexibility-oriented sourcing strategies were superior to redundancy-oriented ones, demonstrating better management of supply-side disruptions and reduced safety stock levels. These results have both societal and scientific implications. Societally, a methodical supplier selection process ensures resilient supply chains, essential for sectors like healthcare and food, and promotes global collaboration and innovation through diversified supplier sourcing. Scientifically, our research validates the BWM's utility for supplier selection, challenging conventional beliefs linking higher costs to superior risk mitigation. A key insight was the growing importance of flexibility in modern supply chain risk management. For the polymer sector, we recommend a BWM-based structured supplier selection process. By partnering with top-ranked suppliers and diversifying geographically, businesses can reduce safety stock levels. Our simulation model provides a practical tool for decision-makers, facilitating scenario testing to identify optimal strategies. However, our study's limitations must be acknowledged. Tailored primarily for the polymer industry, the specific assumptions underpinning our simulation may not be universally relevant. Thus, while our insights are valuable, their applicability might be limited by data constraints and the polymer sector's distinct attributes. In conclusion, a methodical supplier selection, paired with a flexibility-oriented sourcing approach, optimizes supply chain performance in the face of supply-side disruptions. Enhancing these elements can guide businesses in developing agile supply chains, ensuring cost-effectiveness and improved performance.

The ever increasing need of developing efficient solutions for the transport of cargo, able to reduce transport costs while complying to green policies has promoted the use of multimodal transport during the last decades. Multimodal transport consists of the use of at least two modes of transportation for the transport of cargo from origin to destination. Governments and transport authorities (network planners) seek to make the optimal investment decisions when choosing where to allocate their budgets. To accomplish this they need to predict the response of the formulated policies on the cargo flow distributions over a multimodal network. Cargo flows over a multimodal network are in turn dependent on the behavior of water and land transport companies (network users), aiming in minimizing their own total costs of transportation. This research has developed a Decision Support Tool that is able to model cargo flows over a multimodal network, and predict the changes of cargo flows as a response to network planners policies. The research has used the freight transport network of Brazil as a testing ground for the model, and focused on improvements on the railway infrastructure. The results of the model suggest that the establishment of new railway connections and higher railway operating speeds can reduce the total costs and environmental footprint of transportation over a multi modal network. Finally, according to the model assumptions, it was concluded network users have a preference towards faster railway services.

Dutch municipalities face the waste management problem of assets which are reaching their end of life cycle. This is why, they investigate ways to effectively tackle this issue, by simultaneously complying with the goal imposed by the Dutch government about 100% circular construction sector by 2050. As a preparatory step in the transition towards Circular Economy (CE) in the Netherlands by 2050 and the forecasted changing regulations in waste management, a material hub is deemed as a solution to the waste management concern on the level of municipalities. For this reason, the Dutch public authorities are searching a unified framework to evaluate the impact of the material hub as a circular waste management strategy. The main objective of this study is to create a decision-making tool from the municipalities’ perspective to explore the circumstances under which the material hub can contribute to circularity objectives in waste management domain, given the increased cost incurred, and assess the future feasibility of the material hub. In order to achieve the above-stated objective, this research, first, introduces the concepts of circular waste management practices in the Netherlands and the material hubs based on the academic evidence and exploratory discussions with relevant professionals. Moreover, it entails literature review of recent publications relevant to waste management models and specifically Reverse Logistics (RL) models. Second, this study describes the conceptual framework of the defined problem: • by categorising all the construction and demolition waste (CDW) into fifteen material clusters that reflect sufficiently the various waste streams in municipalities • by translating circularity in this context • by configuring a RL supply chain which is universally applicable for the fifteen material clusters. Third, the methodology used to provide a solution to the postulated problem is formulated together with simplifications for transforming the actual problem into optimization model. Fourth, the real-life problem is simulated in a mathematical model, which is then tested in the municipality of Haarlem. Fifth, the data for this case, which is either gathered or generated and inserted as model inputs, is summarised. In data generation, various scenarios of supply-demand ratio of returned materials are used. Sixth, the model results are evaluated in order to determine whether the material hub can lead to economical and circular objectives. Finally, conclusions are drawn about circumstances under which the material hub is financially viable investment for the Haarlem municipality. The cost effectiveness of the material hub is determined by two criteria. The first one is that investment in the material hub as waste management practice could evoke cost savings for the municipality in comparison with the current strategy. This is achieved by giving a new purpose to returned materials and avoiding buying all materials needed in new construction projects. The second financial criterion is the required storage capacity of the material hub. The outputs of the model for different scenarios lead to the conclusion that these two financial criteria can be fulfilled when the supply is higher than demand for returned materials. More specifically, it is concluded that in scenarios with supply-demand ratio of 2:1 and 3:1, cost savings can reach 2.5-3.6% and 10.8-12% respectively compared to the current situation. In parallel, circular objectives are realised within the aforementioned scenarios. It is identified that the optimal objective values (minimum cost, maximum circularity and minimum CO2 emissions) are not obtained simultaneously in one scenario. Subsequently, the optimal solution is determined depending on the focus of the decision-makers in the municipality. Furthermore, it should be commented that even though financial and circular goals are accomplished under the specific circumstances, the cost savings are deemed relatively low considering the initial effort and time of building a material hub. Another implication from the model results is that only limited quantity of returned materials is actually stored at the material hub. This is in contradiction with the intended basic function of the material hub as storage facility.

Accurately forecasting charging demand is a major factor in energy providers’ ability to meet said demand. A number of methods have been proposed in the literature. This master thesis aims to provide a framework for energy providers to forecast charging demand due to increased EV adoption. Specifically, this research examines the effects that rental EV adoption has on charging demand at Icelandic energy provider ON Power’s fast charging stations. Further, it establishes how increased technological developments and infrastructure spending can be balanced to avoid unnecessary costs, and if ON Power’s charging infrastructure is prepared to meet the electricity demand that a fully electrified rental car fleet would bring. To answer these questions, an agent-based modeling framework was constructed to simulate charging demand in ON Power’s network. The model simulates all tourists that use rental cars in driving the Icelandic ring road as if the entire fleet consisted of EVs. A case study is conducted where the Icelandic ring road is split into nine zones, and six scenarios that each reflect different technological developments are simulated using the modeling framework. The research findings indicate a significant change in charging demand. Firstly, rental EVs affect seasonal charging demand with more than half of the demand occurring in the summer months of June-August. Secondly, geographical charging demand shifts from mostly being in the greater capital area, to mostly being outside of it. Thirdly, a sufficient charging infrastructure at Keflavík Airport is a major factor in the electrification of the Icelandic rental car fleet. The research findings further indicate that with the right strategy, it is well within reach for ON Power to handle the rental EV charging demand. A strategy is proposed for ON power to avoid unnecessary spending while still meeting charging demand, which considers current demand, future demand, and how new charging station locations should be determined.

The last decade saw the rise of e-commerce trade and the shift of the manufacturing industry to the emerging economies, China first of all. In this context, the European Customs Authorities experienced an explosion of small parcels coming from e-commerce websites, often from China, and faced difficulties to detect fiscal frauds and security threats using their conventional risk management systems. To address this problem, the European project PROFILE brings together the customs administrations of Netherlands, Belgium, Sweden, Norway, and Estonia, aiming to provide the EU with a shared platform for: (1) accurately assessing customs risks; (2) optimizing operation and logistics by integrating multiple sources of information; (3) developing a shared data platform to share customs risk management (CRM) practices. As part of this project, the Dutch Customs Administration (DCA) and International Business Machines (IBM) Corporation are collaborating to deploy the cutting-edge technologies of artificial intelligence to automatically cross-check the customs declarations coming from Chinese e- commerce against online information. Through a Design Science approach, I carried out this research for the Delft University of Technology, written in collaboration with IBM Netherlands, aiming to deliver a preparatory study for the developing team before the PROFILE project begins. This includes knowledge brokering between the Dutch Customs Administration and IBM Netherlands so that a more precise problem scope can be defined, and the requirements elicited. In particular, this research focuses on the first part of the project: the development of an adaptive web-crawler for e-commerce, able to compare the declarations documents against online information. According to the Dutch Customs Administration, the web-crawling system should gather the description of the goods from declarations, search the product on the web, find its price of sale on the e-commerce platforms, compare it with the value declared in the declaration, and return a risk indicator of green/red flag to the targeting officer. The design process of this system follows approaches coming from the systems engineering discipline, starting with the requirement analysis, addressing them with the state-of-the-art big data analytics, and finally deriving the logical components of the system, whose design is presented through a logical architecture. First, the application domain is investigated. When goods entry the Netherlands need an entry declaration. These goods arrive at the harbor of Rotterdam or airport of Schiphol, where some of these are imported into the country and become import/export, and others stop temporarily as transit waiting to be shipped somewhere else. The Dutch Customs Administration monitors these processes through risk management systems aiming to stop non-compliant goods. This research describes these practices, with a higher focus on the e-commerce risk targeting. About the e- commerce world, a study of the e-commerce processes behind an online purchase is also carried out through a real purchase on Chinese e-commerce. This was used to observe how the Chinese sender described the item, and how the Dutch Customs assessed the risk and decided on the duties to be paid. This led to reflect on the possible frauds scenarios and how to address them. Finally, the Dutch Customs also reported that the products descriptions are often vague and ambiguous, and a more accurate formulation of the problem is described. Secondly, an in-depth literature on the fields of web-crawling and big data analytics techniques is carried out. The possible technologies that could be useful to address the requirements and the problem formulation are investigated. Starting with an analysis of the existing literature on the field of big data analytics, this research also covers the recent trends of machine learning and artificial intelligence. To avoid reporting a too big literature, the topics reported have been accurately chosen, for instance describing only the techniques for web analytics and text analytics. This literature on big data analytics is further broken in two sub-topics, one more theoretical, which classifies the types of analytics methods and defines the technology of machine learning and natural language processing, including the last paradigms of deep learning and reinforcement learning, and one more practical, where guidelines for the design, development, and implementation of machine learning techniques are proposed. It is here that a theoretical framework to systematically reflect on the challenges of the field of big data analytics has been identified. This framework is then used to systematically collect the main technological challenges of the use case under analysis and translate them into non-functional requirements. Finally, the last part of the literature describes what a web-crawler is and what web- crawling/web-craping means. This later extends to the concepts of focused web-crawling and smart, intelligent, adaptive web-crawling, where machine learning techniques are deployed to improve performance. The literature concludes by providing related works of machine learning techniques implemented in smart web-crawling of the e-commerce websites and stating the knowledge gap that needs to be bridged to address the use case under analysis. After the application domain and the literature review, the knowledge from these previous phases combines in a continuous iterative process according to the design science methodology (Hevner, 2014). Through unstructured interviews with the DCA and IBM experts, the requirements elicitation is carried out. The approach by Armstrong and Sage (2000) deriving from the field of systems engineering is used. The main objective of the system to be developed is broken down into a series of sub-activities that must be carefully structured to formulate the requirements. About the non-functional requirements, instead of reflecting on the different domains – technological, environment, law compliance, etc. – as it is proposed by the same systems engineering approach mentioned earlier, this research uses the framework identified in the literature review about the main challenges of big data project (Sivarajah, 2016). To derive the components of the architecture from the requirements and customer needs, the methodology proposed by Suh (1998) called Axiomatic Design has been used, mapping the requirements into architectural components in a rigorous manner. In this way, the design domains proposed by this methodology – customer, functional, physical and process domains – are taken as the reference point for the design process: first, the business needs are identified, then these are translated into requirements, which are mapped into design features. The process domain is left out of this research and will be addressed by the IBM development team in Ireland. The design cycle leads to the design of a web-crawling system represented through a service- oriented architecture (SOA). Its block diagram and black-box description of each application service are provided. Furthermore, the architecture functionality is described with an architecture walk-through and a sequence diagram in the unified modeling language (UML). The result is an innovative real-time web-crawling system to identify the value of a given product on the e-commerce websites. It deploys natural language process models to filter the non-relevant search results, and other machine learning models to best matching the remaining relevant results with a given item description. The design and architecture description of this innovative web-crawling system is the main artifact of this research, while the mixed methodology of systems engineering methodologies and big data frameworks is another important scientific contribution.

This study addresses the truck rescheduling problem as the consequence of uncertain arrival time. It proposes an integrated system of predictive model powered by machine learning algorithm and exact optimization model such that it is distinct from most existing literatures in this domain. The uncertainty of truck arrival time is captured as presence probability by the developed predictive model. Subsequently, a Mixed-Integer Quadratic Programming (MIQP) is built to solve the Probabilistic Slot Rescheduling Problem (P-SRP) in which the rescheduling is subject to expected value constraint that incorporates the presence probability of incoming trucks. The objective is to minimize the expected cost of rescheduling that would lead to more efficient and robust operation. In regard to the predictive model, evaluation according to the standardized KPI shows the ANN is the best algorithm to fit the input historical data with overall F1 score of 73%. Moreover, adding real-time elements enhances the prediction result by 20%. In regard to optimization model, the P-SRP model results on expected cost that is 42% lower than the P-SRP model. Numerical experiment based on multiple scenarios indicates that the proposed solution yields optimal added values in situation characterized by high number of reschedule being required, large scale of operation in terms of quantity of loading bay and utilization rate, the priority is to maintain initial schedule, and there is no stand-in loading bay available. Lastly, it is found that increasing operational efficiency comes at the expense of the schedule robustness, although the value is negligible. This study is limited to the conceptual setting and the use of synthetic data; therefore it should be extended to include empirical result.

In order for public transportation to remain an attractive travel option, its reliability must be improved. To enable this, extensive knowledge on the passenger delay phenomenon is necessary. This study explores the possibility of using empirical data of passenger delay to determine the relationships between stations through a Bayesian Network approach. This approach is able to uncover any dependencies, regardless of known or unknown causes. The results from the Bayesian Network can be used to calculate a set of newly defined informativity indicators. These indicators give information on a station’s capacity of providing information on the delay state of the rest of the network. Among the possible applications of these indicators are: providing accurate information to passengers and the operator on delays, and aiding in determining the most effective areas for delay mitigation measures. To increase the usefulness of these indicators, they are compared to centrality indicators, so that even for networks with little available data, the informativity indicators can still be approximated. It was found that the centrality and informativity indicators correlate to some extent, meaning the informativity indicators could be very meaningful and relevant in further gaining knowledge on and improving public transport reliability.

Due to the complexity of aircraft spare part management, the aviation supply chain and ecosystem are not deemed as transparent as desired. As a result, participants face additional cost of communication and compliancy as proprietary component data is not made immediately accessible. To solve these problems, Maintenance, Repair and Overhaul (MRO) providers currently consider to use Blockchain to track and trace aircraft spare parts. Guided by Business Model Stress Test, this thesis evaluates the robustness of MRO business models when they use Blockchain as an aircraft spare part track and trace capability. Blockchain as an aircraft spare part track and capability is not only strategically relevant for cost-conscious innovating MROs, it also does not impose a risk to the robustness of their business model in most scenarios. MROs should engage in a regulatory-backed Blockchain consortium through which they share limited component data throughout the entire industry. If this is not possible, they can retreat from the consortium under the notion that their business model is threatened. Even though Blockchain could improve aviation supply chain and ecosystem transparency, the feasibility of the consortium is questionable. To remedy this concern, future research should focus on identifying strategic incentives for Original Equipment Manufacturers and aircraft operators to participate in the Blockchain consortium.

The aim of this thesis is to investigate how the collection of industrial waste can be managed with different types of sub-contraction. Literature review was conducted in order to obtain knowledge about how this type of problems can be modelled along with the types of sub-contraction and how the logistical performance could be assessed. Interviews were performed to get a better understanding of the decision making for this type of problems. Thereafter, two mathematical models were formulated in order to model the decisions at tactical and operational level. In order to get more insights the developed models were implemented with real-life data. Following, further testings were performed to determine the efficiency of the developed models and to obtain managerial insights. A key conclusion was that among the three types of sub-contraction that were examined, the results obtained from the route based and the tour based type give the most realistic solutions. Overall, the results indicate that the introduction of sub-contraction in waste collection brings considerable cost-savings.

Aircraft LRU (Line Replaceable Unit) MROs (Maintenance, Repair Organization) struggle to meet service levels. To stay competitive, literature describes two strategies; increasing contracted fleet size and not reaching projected repair turn around times. Besides these two strategies, literature does not describe the use of triple echelon order fulfillment for global contracts but it is recommended Sprong (2019) Munsters (2019). This research aims to find relations between the service level and the two strategies from the perspective of expansion rules and operations. A discrete time-step simulation is proposed because the systems component availability changes over time and because of the stochastic behavior of repair time and component failure. The found relations are firstly that send from base operational procedures and secondly that mbk stock influence the theoretic repair time performance. The second relation is that when adding more fleet units (apply a bigger pool), the current performance will deteriorate.