In this research, a step by step approach is followed to find the root cause of the lagging adoption of an automated planning system by equipment planners. This is done by exploring the Technology Acceptance Model by Davis, from which a conceptual framework is established. This framework is central to a survey conducted under equipment planners globally. The results of this survey, and follow-up interviews, allow to identify the root cause of the lagging adoption of the new system. Furthermore, these results are used to draft a road map to improve the adoption rate.

The Netherlands faces significant spatial challenges that necessitate major social and spatial interventions, including the need for new housing, renewable energy infrastructure, climate adaptation, agricultural constraints, and biodiversity conservation. This study proposes a framework for combining port functions with water storage in the Netherlands, specifically applied to the Amsterdam Houtrakpolder case. By focusing on the technical feasibility of dual-function solutions and the interests of stakeholders, the framework evaluates the potential integration of water storage systems prioritising water retention. The analysis encompasses four types of port terminals: container, liquid bulk, dry bulk, and offshore wind energy terminals. The study utilises Systems Engineering (SE) to define objectives and constraints, generate conceptual designs, and evaluate alternatives through Multi-Criteria Decision Making (MCDM). The results indicate that the Aquifer Storage Recovery (ASR) system is the preferred dual-function solution for the Houtrakpolder case, aligning with stakeholder interests. However, technical feasibility remains uncertain, suggesting that a systems approach at the main water management and port area levels is recommended for further development.

The transport sector is the Netherlands’ second highest emitting sector, with 19% of the total energy demand. Where other sectors are becoming more sustainable, the transport sector is one of the most difficult to decarbonise despite the many studies in this field. This research aims to gather a list of potential measures and combine them to reach an 80% reduction of emissions in 2040 for the hinterland freight corridor of Rotterdam - Venlo with a high rail freight intensity. A list of potential measures was established with literature studies and expert interviews. With a normative scenario design approach, an iterative process resulted in a list of 8 potential measures with a set of targets for these measures. After computation with the DeCaMod model a reduction of 80% is achieved on the corridor. The results showed that for this corridor, the reduction depends on the availability and adaptability of biodiesels and the electrification of vehicles and vessels. In contrast, measures aiming to improve logistics efficiency do reduce the total energy required but are less effective in reducing carbon dioxide emissions.

In their Distribution Centre (DC) in Maasdam, FrieslandCampina (FC) uses a four-crew shift schedule to prepare all necessary orders for their clients, 24 hours of each Monday to Saturday. Their large automated warehouse is home to 10 000 pallet places, containing fresh dairy products. From here, orders are either prepared as full pallets, machine-picked layers or hand-picked "colli". In the last department especially, personnel cost is high relative to the throughput. Definitive picking deadlines are often ambiguous, posing challenges in job and personnel scheduling. The study goal is twofold. Firstly, to find out whether full knowledge of picking deadlines can contribute to a more efficient job, and so, shift schedule. Secondly, to offer insight for a trade-off between shift types to absorb workload. To reach this study goal, a Shift Minimisation Personnel Task Scheduling Problem (Krishnamoorthy et. al., 2012) and a Bin Packing Problem (Paquay et. al., 2014) were combined and tailored to fit the scheduling problem at FC's DC. In three weekly scenarios, the MILP model scheduled picking jobs in the least expensive shifts through a cost minimisation function. Two model configurations were used, one to prefer the shift between 09:00 and 17:00 (flex), and one to prefer either one of the 06:00-14:00 (morning) or the 14:00-22:00 (afternoon) shifts. Both model configurations inherently avoided the most expensive 22:00-06:00 (night) shift. Main findings include the possibility to absorb workload using the morning and afternoon shift and to avoid the night shift. Additionally, it was confirmed that insight in picking deadlines can contribute to an efficient personnel schedule a great deal.

Long truck queues and congestion around terminals is a common sight, however they come with many negative externalities for all the stakeholders involved. Truck Appointment System (TAS) is the most commonly used system to face these problems, but it still has some drawbacks and limitations. Consequently, in this research an extension of the typical TAS is proposed in order to improve its performance. The main components of this system are the use of truck dependent time-windows, the utilization of real-time information and the adaptive trucks rescheduling model. The duration of the arrival time-windows is longer than the actual service times, allowing overlap between time-windows. Thus, the actual service sequence might be different from the reserved one. To determine the actual loading sequence an Optimization model is developed, which is run periodically while utilizing real-time truck information. A chemical plant is used as a case study in this research. The performance of the proposed TAS is assessed with the use of a Simulation model. The outcomes of this research suggest that the a less strict TAS can significantly improve the system’s performance, especially trucks’ waiting times. Also, the system’s resilience against disruptions and the plant’s environmental footprint are improved, while queues are reduced.

This master thesis explores how ports, such as Hamburg, are adapting their logistics to enhance supply chain efficiency, with a specific focus on the evolving global first-mile fresh fruit reefer (GFFR) cold chain. It discusses the use of value stream mapping (VSM) from an inside-out perspective to analyze operations, challenges, and opportunities for improvement. Through VSM and opportunity analysis, it identifies key issues and opportunities, stressing the need to improve customs scanning and establish a food cluster network at the Port of Hamburg. In summary, this study underscores the significance of effectively managing GFFR cold chain relationships, with proposed improvements addressing weaknesses and capitalizing on opportunities for competitiveness.

Demand forecasting plays a critical role in organizational planning, encompassing inventory management, capacity allocation, and financial decision-making. However, achieving accurate forecasts can be challenging, particularly in industries characterized by high demand volatility, such as semiconductor assembly equipment manufacturing, exemplified by Besi. Leveraging machine learning (ML) techniques presents a promising solution for effectively forecasting the seasonal and cyclical demand fluctuations experienced by Besi.

Besi, full name BE Semiconductor Industries N.V., is an multinational semiconductor equipment manufacturer which originates from The Netherlands. The company was founded in 1995 by Richard Blickman and now has operations in, among other countries, China, Switzerland and Malaysia. Besi develops leading edge assembly processes and equipment for leadframe, substrate and wafer level packaging applications in a wide range of end-user markets including electronics, mobile internet, cloud server, computing, automotive, industrial, LED and solar energy.
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This paper investigates the challenges organizations face when implementing ML models into their demand forecasting processes, aiming to design a framework and implementation plan to guide organizations in adopting ML techniques. The research methodology employed is design science research (DSR), which focuses on developing and validating new designs within existing systems. The paper follows the iterative steps of DSR, including problem identification and motivation, objective definition, design and development, demonstration, evaluation, and communication. This iterative process facilitates collaboration with literature and industry experts to design a practical solution.

The study draws on literature research and exploratory discussions with Besi employees, emphasizing five key areas for investigation: the current forecasting process, existing forecasting techniques, organizational requirements, limitations, and input-output considerations. The findings highlight that Besi, similar to other organizations, employs a multi-layered forecasting process, with the most effective layer for implementing improvements and ML models being the initial forecast. Additionally, Besi predominantly relies on judgment-based forecasting techniques, making the implementation of a neutral ML tool necessary to create a hybrid forecasting system that mitigates human bias. Besi possesses the necessary prerequisites for effective ML techniques, such as clean and abundant data, but lacks the expertise required to construct and implement accurate models. Furthermore, Besi desires a neutral model that counters human bias and inputs historical monthly sales data, with the output expressed as total monthly sales.

To facilitate successful implementation within Besi's forecasting chain, several aspects are explored: the process framework (including integration, monitoring, updating, forecasting, and communication), peripheral considerations (e.g., legal and end-user trust), and the dashboard. The process framework is designed based on the existing forecasting process at Besi, incorporating the ML model, a dashboard, and revised information flows. The steps align with literature recommendations for ML-based forecasting, indicating that initial implementation is expected to face minimal resistance, monitoring is an ongoing task for the forecaster, updating involves improving the model and frequent training with new data, forecasting remains with the same personnel but incorporates additional information sources, and communication remains unchanged.

Peripheral matters, such as regulations and end-user trust, are limited in their impact, with research indicating that no laws impede ML model implementation, while a dashboard can enhance trust among direct users. Gradual implementation in phases, where the model does not hold authoritative power, facilitates organizational acceptance.

Based on insights from all sources, a step-wise plan for ML model implementation is proposed. The initial phase involves assembling the necessary infrastructure, data, and stakeholders, followed by creating and implementing a minimum viable product as a confirmation tool alongside the existing forecasting process. The minimal viable product is a functional model that provides usable accuracy and lists expected monthly sales based solely on historical data and observed fluctuations.

The second and final step focuses on refining the model and presenting its findings through a dashboard, incorporating information from other relevant sources to support the forecaster in making informed forecasts. This phase also enables improved communication with other relevant departments. Ultimately, the forecast incorporates real-time sales data for increased accuracy.

Feedback from Besi representatives indicates that this implementation approach is suitable for their business. The framework and steps presented in this study have been generalized to benefit other organizations, making an academic contribution in the field of ML-based demand forecasting. This contribution stems from the building upon the theory by Caniato in implementing a quantitative forecasting method. The new findings show that there are multiple ways to implement a quantitative method and that, according to this paper, the implementation is best cut up into two phases for smooth transition and maximum acceptance.

The global transport sector currently contributes 23% of carbon dioxide emissions, a figure set to double by 2050 without mitigation efforts. While Scope 3 emissions, notably in container transport, are challenging to address, they constitute over half of all emissions. This study explores cargo owners' perspectives on Scope 3 emissions and identifies opportunities for mitigation. With impending Scope 3 reporting obligations in 2024, the shipping sector lacks a clear path for emissions reduction. The research aims to bridge this gap by examining how carbon emission information in port environments can enhance the sustainability of logistics processes. The study combines supply chain performance theory, decision-making processes, and decarbonization of logistics into a conceptual framework. A multiple case study, incorporating diverse cases based on product and market segments, validates the framework. Interviews with logistical decision-makers and experts reveal challenges such as lack of data availability, data quality, responsibility, and intrinsic motivation. The study identifies legislative pressure, consumer and shareholder influence, and comparison functions as potential drivers for prioritizing carbon emissions in decision making. Improvements suggested include enhanced data quality through responsibility agreements, standardized emission tools with mode comparison functions, and the facilitation role of port authorities. Technological development is seen as crucial for increasing sustainable options, with legislative and stakeholder pressures necessary for cargo owners to prioritize carbon emissions in decision making. The study concludes that emission information tools, supported by port authorities, can contribute to a more sustainable approach in logistics decision making.

This study aims to investigate and develop the efficiency of a decentralised dynamic system for last-mile parcel distribution. A better understanding of the efficiency, and thus the feasibility of this decentralised logistics method, contributes to a better understanding of the potential of these systems as alternatives to the current centrally organised system. The main research question is: To what extent can the efficiency of a decentralised dynamic parcel distribution method be improved by applying different heuristics and adapting the simulation environment with congestion and a mixed fleet? To answer this question, a review is made of existing literature on last-mile logistics, self-organisation in logistics and auction methods in logistics. The current model is evaluated, benchmarked and validated. Model improvements are defined and applied in an experimental setting using a small dataset, after which the extended model is applied to a real-scale case dataset based on 729 delivery addresses in Delft. The applied modification strategies are random insertion, parcel swapping and k-means clustering. To account for the behaviour of the model under different conditions, the method was tested for a heterogeneous fleet of vans and cargo bikes and different levels of congestion. In general, the heuristics were able to improve the performance of the model, but the improvements in travel distance were minimal. When the combination of the best performing heuristics is applied to the case data, a 4.6% improvement in vehicle travel distance can be achieved compared to the original method, but still with high computation times. Comparison with an external centrally organised OR solver shows that although the decentralised method can be improved, it does not perform well, as the OR solver can find a better solution in a fraction of the time needed for the improved decentralised auction method. This study provides a proof of concept of the method and a more efficient model has been constructed. However, given the limitations of the method, further research should carefully consider whether it makes sense to continue with this particular method.

Intralogistics is a crucial part of the supply chain and is continuously influenced by the drivers such as globalisation, mass customisation, and shorter product life cycles. The challenges brought about by the pandemic have accelerated the ’shifting pattern’ towards automation in transport & logistics. With the push towards higher robustness and efficiency, self-organisation is the next step to incorporate flexibility, reconfigurability, scalability, re-usability, adaptivity, and energy-efficiency in the material handling system. This aims to reduce human fatigue and inaccuracy in in-house logistics and also improve the response time. However, the feasibility of such systems in real-world applications often remains unclear owing to the complex emergent system characteristics as a result of local interactions between multiple agents and the lack of extensive operational studies. This research attempts to develop an intuitive, adaptable and scalable model to study the emergent behaviour of a system of self-organising robots for in-house material movement. By considering the industry expectations and feedback in the research and evaluation, the model also functions as a decision support tool for business cases, providing a prima facie impression on the system performance.

The focus of this thesis is to introduce the application of blockchain to address the challenges associated with Port terminal operations. The research aims to investigate the interactions between different processes such as communication, planning, and transportation and associated challenges at a small-to-medium size logistics hub. Combi terminal Twente (CTT), situated at XL Business Park (Port of Twente) is considered as an ideal representative of such a small- to-medium size logistics hub, to carry out this research. To understand the intensity of changes that could be bought into the system when a disruptive technology like blockchain is implemented, different levels of digitalization is studied. The study also emphasizes identifying enabling technologies that can enhance the functions of blockchain applications and implementing automation. Furthermore, the study compares the current business process with an improved blockchain-based process by employing BPMN. The contribution made in the study can possibly help the researchers and the developers to introduce Proof of concepts and different business models. During the course of analysis, six main areas were identified where blockchain technology can be employed; secured communication to secure release reference number, container sorting, planning routes and congestion, trade documentation, certification & maintenance of the assets, and fleet management. the research contribution is as following; First, from a practical point of view, analysis facilitated the recognition of bottlenecks in the current inter-organizational processes, where the unloading process is studied and depicted in the report. Second, the proposed categorization of blockchain solutions may help understand the different uses of blockchain in Port terminal operations. Third, from a Business Process Management point of view, the improved business process extends the knowledge in BPMN, and the domain of blockchain-based information systems and the findings are validated through interviewing experts. The alternative solutions were considered to critically evaluate the technologies success rate. Also, the significance of the applications on the KPI's specific to terminal operations is investigated. Finally, based on the findings and the interview, a roadmap for future implementation is determined to develop proof-of-concept leveraging opportunities offered by blockchain in the future.

In recent decades, logistic markets havebeen changing. Ecommerce is growing steadily, and the recent Covid19 pandemicgave an extra boost to that. Besides, customers are seeking more flexibility inthe logistic services. Parcel delivery is changing, yet this leads to variousnegative externalities including congestion, air, and noise pollution. Theseeffects let companies seek innovative solutions for their parcel transport. Oneof these innovations is ’crowdshipping’,parcel delivery done by the crowd instead of conventional delivery companies.By making use of existing passenger transport rather than a speciallydispatched driver to ship parcels, parcel shipping should be economically and environmentallymore sustainable. Crowdshipping is a service that has shown potential in pilotsand smallscale researches. However, strategic analyses of the impact ofcrowdshipping on all actors in the transport systems are still lacking. Thegoal of this research is to explore the interactions between travellers and parcelshipments in various strategic crowdshipping contexts and assess their impacton transport use. To achieve that, the following main research question will beanswered: ’How could sustainable crowdshipping impact freightand passenger transport use?’. A simulation model is built using agentbased modelling to explorebehaviour and simulate possible effects. This model consists of interactionsbetween four agents in the system; the customers,crowdshipping platform, travellers and occasional carriers. First, thecustomers place their orders at the platform. When travellers make their trip,they could consider carrying a parcel along their way. They notice their plannedtrip to the platform, which will calculate the optimal parcels for them. Thetravellers could opt for one of the parcels and turn into occasional carriers.The impact of crowdshipping is assessed by calculating the detour theoccasional carriers travel to deliver their parcels. Other outcomes are the providedcompensation and percentage of matched parcels, to determine the viability ofthe platform. The spatial demarcation of the simulation is most of the provinceof South Holland in the Netherlands. In this study area, 2.3 million peoplereside who order over 220,000 parcels each day. Furthermore, 4.1 million tripsare made daily by car and bicycle. Taking the willingness of both customers andtravellers into account, 13,000 parcels and 750,000 traveller trips enter themodel. Four experiments are performed to inspect system behaviour in variouscontexts. The results show that implementing crowdshipping in this study areacould be viable. The average provided compensation is lower than the priceconsignors currently pay for conventional delivery. Besides, the delivery degreeseems acceptable to get a decent level of service. Through crowdshipping, thetravelled distance in the passenger transport system will increase because ofthe detours taken by occasional carriers. This increase subsequently leads to adecrease in freight transport distance through a decreased demand inconventional parcel demand. However, the passenger transport increase exceeds thefreight transport decrease. The crowdshipping platform could limit the takendetours by making strategic choices in their implementation. This might be atthe expense of their delivery degree. It is advised for the public authority toset boundaries for the platform and stimulate strategic matching choices basedon these possible externalities. When interpreting these results, cautionshould be taken. The approach has some shortcomings regarding the spatialdistribution of detours, costs of platform’s viabilityand first leg distances for parcel transport. Furthermore, limitations could befound in the assumptions made for the simulation model. This includes theabstraction that travellers do not deviate from their planned trips andmodalities, and travellers could only carry one parcel. Another simplificationis made in the matching strategy by the platform which might have led tosuboptimal drivers for the parcels. Other limitations are caused by flawed datause. Travellers’ and customers’willingness could therefore be unreliable. Also, pedestrian and publictransport travellers are not considered due to data deficiency. Furtherresearch could be done in three ways within this field of study. First, moredata can be gathered to solve the abovementioned limitations. This includesdata on preferential routes for occasional carriers and willingness data fortravellers in all modes. Secondly, other conceptual choices can be made tooptimise the detours per parcel with forecasted travellers, or to conceptualisethe collaboration between conventional and crowdshipping delivery. Finally,research can be done to study intervention methods and corresponding legalpossibilities for the public administration to limit travellers’ detours.

Urban freight logistics currently has to deal with multiple unsustainabilites. Physical Internet characteristics can be promising to make urban freight logistics more sustainable. It was researched if this can be the case and, what opportunities and barriers there are belonging to this change. With Q-methodology different stakeholder perspectives were revealed. This resulted in four different perspectives from which three had a positive attitude towards PI characteristics. One perspective was more moderate in relation to this and stated that a lot is possible already without changes happening. Opportunities and barriers are defined and it was concluded that there is currently no real need to change. Because an increase of national coordinated regulation was also assessed positively a policy framework was created that states individual and collaborative actions for stakeholders. With this 'an environment where efficiency pays off' should be created.

Growing demand for railway-based transport stresses railway infrastructure on safety, punctuality, and robustness. The Port of Rotterdam and ProRail desire a process of executing simulation model supported capacity studies wherein inputs, methods and outputs are coordinated upfront. However, interorganisational capacity studies are currently conducted ad hoc in lengthy processes, in which there is disagreement about inputs, methods, and outputs of the process. They can be considered misaligned as the internal capacity management processes do not fully fit each organisation’s objectives, while also not being sufficiently adaptive towards the dynamic railway capacity context. Alignment is the result of coordination activities between collaborating organisations. An aligned rail freight capacity management process is necessary for the successful matching of demand and supply for rail freight transport services, and can be supported by simulations of the railway capacity. Thus, the question arises: How to improve the alignment of collaborating organisations on quantitative metrics for railway freight capacity in the Port of Rotterdam with the use of meso-level simulation models?

This research presents a process design for an interorganisational capacity planning process that has the potential to improve alignment between the collaborating organisations. The principle-based design method presents a novel approach to addressing alignment problems in the domain of decision-model supported capacity planning collaboration between networked organisations. The process design is formulated through a design science method, wherein specific coordination challenges are matched to literature-derived principles regarding technical and interorganisational coordination of capacity planning processes. The design is evaluated against stakeholder defined requirements and through discussion of the proof of concept: an executed capacity study using the formulated design.

In order to tackle the problem of supply chain managers not being ready to share data, the thesis first identifies the root causes of the reluctance of supply chain managers towards sharing data. This reluctance of the supply chain managers is caused by many reasons. Firstly, they fear the misuse of their sensitive information. Information regarding one supply chain firm can be used by other firms in a way that undermines the original firm. The competitors can use this information to win the race over the original firm. There is not enough trust between supply chain firms and hence, they fear giving their information to each other. Lastly, the data sharing techniques would require investment in terms of infrastructure of data sharing technologies. The reluctance, caused by the aforementioned reasons, is a reason for the absence of data sharing in supply chains. By overcoming this reluctance and sharing data in a supply chain, efficiencies can be increased and optimal potentials can be achieved. Therefore, this reluctance is an obstacle that needs to be overcome.

This research investigates the diffusion of Shore Power in ports. Shipping Companies and ports are dependend on the strategies of each other. Using Game Theory and Agent Based Modelling, the network and parties are simulated.

The rise of e-commerce makes shopping easier than ever. It also puts pressure on logistic service providers, their employees, traffic and the environment. Parcel deliveries to service points or parcel lockers could release some of this pressure. However, service points are only scarcely used by Dutch consumers, while parcel lockers still lack a dense network. In addition, it is unclear how Dutch consumers can be persuaded to use these options more, and what kind of travel mode they will use for their parcel pick-ups. Two stated choice experiments were constructed to find out which factors influence Dutch consumer preferences for different delivery methods as well as the choice for a travel mode to pick-up a parcel. The results indicate that delivery prices and delivery moments are important factors that influence consumer choices for a delivery option. Several background characteristics, like age and current e-shopping behaviour, also have an influence. For the pick-up mode choices, distance and weight were most important. Here current travel behaviour plays a large role as well. There is potential in setting prices and delivery moments such that the self-pickup methods are used more. Building a dense parcel locker network can further accelerate this, and will also favour pick-up modes like walking and cycling. Future research should, therefore, focus on the complex situation in the Dutch parcel market, in order to analyse how more collaboration between the different parties in this sector can be improved such that prices, delivery moment and distances to pick-up points favour pick-up methods more.

Many innovations have never or only in limited form been adopted. Therefore, throughout the last decades, academics have been focusing on the success and failure factors (SFF) of the adoption of innovations. This success depends on the objectives of the engaged stakeholders. However, no comprehensive literature review exist that focused on the purpose of the SFF frameworks and the engagement in the innovation process of these frameworks. Therefore, the papers related to SFF frameworks and the adoption of innovations have been reviewed on these knowledge gaps. This review showed that no framework had been developed that engaged stakeholders for using weighted success and failure factors to propose and evaluate solutions to enhance the viability of innovations. Therefore, a multi-criteria approach called the Multi-Actor Multi-Success-and-Failure-Factors Multi-Criteria Analysis (MAMSFFMCA) has been developed and evaluated on a case about online grocery shopping in the Netherlands. This method has been a useful tool to engage stakeholders and experts in efficiently identifying success and failure factors of online grocery shopping, to propose alternatives and to evaluate them. The MAMSFFMCA method can be further developed by providing more guidelines and rule of thumbs that can be used for the data gathering, the selection procedures and the evaluations of every step.

The brownfield container terminal at the Maasvlakte aims at realizing a zero-emission vehicle fleet to strive for its aim to be zero-emission by 2040. Therefore, the research question is as follows: “What are the consequences for costs and operational performance of implementing mature renewable energy carriers for AGVs at the container terminals of ECT before 2040?” Literature research is carried out to find the definition for energy carriers and to find out what possible energy carriers exist to transition a fossil fuel-based vehicle fleet into a zero-emission vehicle fleet. The list of possible energy carriers is reduced by a screening, consequently, resulting in the lithium-ion battery and hydrogen as possible candidates for the vehicle fleet of ECT. Consequently, discrete event simulation is carried out to find the operational performances of these two energy carriers. Afterward, a cost calculation is done and a possible phased transition is shown.