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The logistics service provider TransMission has been looking for possibilities to deliver with the electric Cargohopper in the inner city of Amsterdam. A freight hub near the city centre of Amsterdam is needed where goods can be transhipped to make this possible. A feasibility study is conducted for different policy scenarios. This has resulted in the choice of a suitable location for the freight hub. Finally, an advice is given to TransMission how the logistic process, human resources and ICT facilities at this freight hub should be organised.

Master thesis project on the application of a systems approach to improve organizational performance. The demand planning process at a pharmaceutical company is analyzed to identify the root causes for the low performance levels at their local sales organizations. These root causes are translated into a redesign of the demand planning system to achieve better results.

Many large airport face problems of congestion in the terminal. Several studies have identified the check-in process as the bottleneck process. However, proposed solutions in literature are limited to changes inside the terminal. In this research, a wider supply chain perspective is applied to the passenger and baggage flows of airports. An off-airport baggage check-in service is developed for the case of Amsterdam Airport Schiphol and KLM Royal Dutch Airlines, with the focus on logistics organization and the business model.

The warehouse in Helmond is almost completely dedicated to Cisco. This company is interested in the emissions caused by the operations they outsource to Kuehne+Nagel Helmond. Not only is it interesting for Cisco to know their emissions, but it can also be used for benefits for Kuehne+Nagel Helmond. Kuehne+Nagel nationally is willing to take responsibility for the emission caused by their operations and is actively working on monitoring and reducing emissions. Furthermore, Kuehne+Nagel wants to be unique in the sector and nice side effect is the fact that actively reducing carbon emissions can be used for a green image, to drive a sustainable business and carbon reduction often goes hand-in-hand with cost reductions.

Schiphol is a very important transfer hub for KLM and therefore short connection times are important to be competitive with other hubs. KLM sells flights with a minimal connection time of 40 minutes between European flights and 50 to, from, and between intercontinental flights. However passport control and security checks for transfer passengers can take much time resulting in missed (or delayed) flights. The passenger terminal at Schiphol is divided into a part for flights to and from Schengen countries which do not have border control between them (including most European countries) and a part for Non-Schengen countries. Transfer passengers crossing the Schengen border must pass passport control. Transfer passengers into the Schengen area also pass a security check (the transfer passengers to Non-Schengen countries have this check decentralised at the gate). Often congestion occurs at the border control and security check causing passengers to miss or delay flights. Therefore KLM would like to gain more insight into the performance and influence of border filters at the Non-Schengen/Schengen filter in the terminal on the transfer passenger process for flights of KLM. The main question is therefore formulated as follows: Which, when and why do problems occur in the transfer passenger process at Schiphol and how can this situation be improved for KLM?

Vopak biedt opslag voor vloeibare bulkgoederen. Vopak Vlaardingen is een van de tachtig terminals van Vopak, waar biobrandstoffen en plantaardige oliën en vetten opgeslagen worden. Eén van de acht productgroepen die opgeslagen wordt, is oleochemie. Deze productgroep bestaat uit vetzuren, vetalcoholen en glycerine en wordt met name met grote zeeschepen aangeleverd en weer afgevoerd met trucks. Hierbij wordt gebruik gemaakt van een roestvrijstalennetwerk van pijpleidingen om deze oliën op te slaan in tanks. Korte laad- en lostijden voor schepen zijn van belang op deze terminal voor een goede dienstverlening voor de klanten. Daarom moet het leidingennetwerk voldoende capaciteit kunnen bieden, zodat geen lange ligtijden (of wachttijden) ontstaan. Daarom is de hoofdvraag: Op welke manier kunnen de knelpunten van het roestvrijstalen netwerk geïdentificeerd worden en mogelijke oplossingen getoetst worden en wat is het effect van deze oplossingen op de ligtijd van schepen?

CEVA Logistics in The Hague is a 3PL that performs logistics activities for its customers. These activities include transportation and different warehousing activities. CEVA's main customer is KPN. It shows that the capacity needed in order to perform the activities at CEVA is not scheduled properly. This is caused by fluctuating demand and late knowledge of the demand. KPN forwards the demand of their customers towards CEVA, however the demand is only known the evening before and during the day. The effect is that there is over- or under capacity, which causes that overtime is needed or extra temporary personnel is needed at the last moment. This will lead to low productivity and extra costs. To better schedule the required capacity a forecasting and planning tool was developed. The main research question is: Can costs be saved and the service level increased by using a forecasting and planning tool? Cooperation with KPN in the development process of this tool is important, because KPN knows when the demand gets influenced by sales promotions, introductions and back orders. The most labour-intensive process is the outbound handling, therefore it is important to schedule the capacity for these processes accurate. The objective of this project is to design a forecasting and planning tool with which the demand of the customers of KPN of the 6 outbound handling order processes can be forecasted and with which the required capacity can be scheduled more accurate at CEVA.

This report contains the detailed course of designing an ontology that formalises the domain knowledge of City Logistics and in turn facilitates relevant agent-based modelling. Validation and example of application are also included. The formal output of this work is an ontology document edited with Web Ontology Language (OWL) and is named as 'City_logistics_ontology.owr.

This research, with the title "Redesign Logistic System Schiphol Oost", was written as the final thesis project of the Master Program "Transport, Infrastructure and Logistics", which is a jointly operated master of three faculties at the Delft University of Technology: Civil Engineering and Geosciences; Mechanical-, Maritime- and Materials Engineering; and Technology, Policy and Management. The research was an assignment for SIM (Samenwerking Innovatieve Mainport), which is a cooperation between Schiphol Group, Air France-KLM, Delft University of Technology, the National Aerospace Laboratory (NLR) and the Netherlands Organization for Applied Scientific Research (TNO); and it was commissioned by the Delft Centre for Aviation (DCA). The research was executed as an internship at Schiphol Group.

The ORTEC Consulting Group (OCG) advises express service providers on efficient network design. A part of the challenge to this design is the hub location problem (HLP). The HLP consists of selecting optimal locations for large sorting centres (hubs) from a range of smaller sorting centres (depots) to which the rest of these depots can connect. The resulting network aims to transport parcels in the most cost effective manner achievable. The OCG is interested in multi-agent modelling technique as a new approach to the HLP, since this technique provides a natural way of modelling complexity that arises from interacting nodes. Literature showed no research on solving the HLP from a multi-agent systems (MAS) perspective. This thesis aims to fill this gap. With the main research goal being "Designing a model that can solve the hub location problem for express networks with use of multi-agent modelling", the model was designed using the Prometheus methodology. The model uses information on the volume distributions of a set of fixed depots to determine optimal hub locations from these depots. Simultaneously, the model ensures that parcels can be sent from any location and will be delivered within the set service time. Optimal hub locations in this sense means that the total network cost consisting of hub cost and transport cost is as low as possible. The inputs of the designed model are the depot locations, their volume distributions, the driving times between nodes, hub cost and transport cost. The outputs are the network cost, the number of hubs, the locations of these hubs and the routes of all the different parcels. The designed MAS consists of three main phases. Phase 1 is responsible for creating hubs based on volume distributions. Hubs are placed in regions that have a lot of parcels to be transported between them. Phase 2 creates routes via the hubs that resulted from Phase 1. Although the most efficient routes are calculated, the main focus of this phase is to create routes for every parcel in the first place. During Phase 3 the main focus is cost reduction through reducing air transport cost, reducing road transport cost and reducing hub cost. In addition, part of this design is implemented into a proof of concept using the JACK Agent Language (a Java based language) to show the added value of multi-agent modelling. This proof of concept, Preliminary Organisation of Hub Location Tool (POHST), contains the implementation of the first part of Phase 1. Hence, it creates hubs based on the volume distribution. A graphical user interface is added to turn POHST into an easily accessible tool. This tool is applied to two datasets to demonstrate its use in the preliminary phase of network research aiding in data gathering and the generation of initial hub configurations. Experts of the OCG confirmed the usefulness of POHST as strategic analysis tool. In addition, the type of model outputs did not allow for thorough integral testing of the tool, although other verification methods showed that the tool behaved as intended by the model design. Due to the lack of quantitative output, the tool could not be validated using traditional methods. Instead, the same experts were asked to validate the model. Although they had quite a few recommendations for further improvement of the tool, there was a consensus on its validity. The design process revealed interesting benefits and drawbacks of using the agent paradigm to solve the HLP. The source of much of the complexity of the HLP is the interaction between nodes. One of the major advantages of using the agent paradigm is that it provides a natural way of modelling such interactions. Furthermore, the scalability of agent models is an attractive feature. When a few types of agents have been designed, an unlimited amount of such agents can be used when applying the model, scaling along with the inserted data. Next, the research shows the benefit of agents adapting to local circumstances. By locally looking around for inefficiencies agents are capable to enhance the solution with limited data. Another advantage of using agents is the detailed level of statistics gathering it enables. Every agent decision can be tracked. Consequently, an agent model does not only produce an outcome, it can also show how and why this outcome evolved as it did. The major challenge of using agents to solve the HLP is the difficulty of making local decisions that might impact the entire infrastructure. To be absolutely sure that a local change will lead to lower network cost, all possible consequences are checked and valued for their change in cost. This process can lead to a vast amount of communication, because the agents are practically considering global data. Thus, undermining the multi-agent modelling values of local data views. Although this risk exists in the presented model, it does not prove that it is impossible to achieve a design that reaches a global optimum using strictly local information. However, it is the greatest challenge that the HLP poses to the use of multi-agent modelling. In conclusion, it can be said that this research has proved the potential of using MAS to solve the HLP. It is also certain that many interesting properties of agents can be further investigated. Subsequently, these models could add great value to compete with and possibly even defeat current HLP models.

KLM Cargo would like to improve short-term resource scheduling at their World Port cargo handling terminal, in order to increase both quality and efficiency of their service. Forecasts of air cargo demand could support decisions on manpower deployment. This thesis contains a forecast comparison study for short-term forecasts of the amount of departing cargo from World Port in kilograms per day, based on historical booking data. A single regression forecast model on pre-departure booking levels came up as top-performing model, capable of predicting cargo demand per day with only 3% mean absolute percentage error. This thesis also examines departure behaviour over the course of each day of the week. Furthermore, arrival behaviour is analysed, which is defined as the connection between the departure flow and several arrival flows at the terminal. The main conclusions out of the behavior analyses is that cargo departure is unevenly spread out over each day and that cargo is delivered at World Port relatively far in advance of planned departure.

KLM’s Components Services is responsible for availability of repairable aircraft components for KLM and third party airliners; on behalf of planned and unplanned maintenance. Repairable aircraft components are interchangeable. Hence the realization of components availability includes monitoring components flowing through a continuous cycle that consists of storage, logistics, component removal-exchange-installation, reverse logistics, repair, and storage again. This monitoring process is executed by the unit vendor control (VC), part of Component Services (CS). Since components availability is sold as insurance, CS needs to fulfill 100% of the component requests. Thereby it is complicated by the complex nature of the process cycle, the high value and large variety of unique components as well as the unpredictability of component failure. Clearly this has in impact on inventory management and maintenance logistics headed by Component Services. Define the problem While ensuring 100% component availability, often last-minute activities are required to recover delivery problems that are caused by stock-out situations. These activities are referred to as recovery activities. A stock-out of a component is the result of unstable demand and unstable repair times (TAT). Stock-outs indicate that the process cycle is not fully under control, this is called process instability. At present, the consequences of process instability (i.e. stock-outs) are only reactively recovered. This means that people tend to act as the stock-out already originated and not try to avoid it. Thereby, the following research question is formulated: Main research question: How should Component Services proactively anticipate on process instability in order to improve availability management by avoiding stock-outs instead of only solving these? The DMAIC methodology is adopted to create insight in the root-causes and impact of process instability and finally give an answer to the main research question. DMAIC is a six-sigma improvement methodology that attempts to understand and eliminate the negative effects of process instability. Measure delivery problems To be able to know what kind of component often cause stock-out situations, the operational characteristics of service parts defined by Huiskonen (2001) are used for component categorization. From that is concluded that problematic components have in common that these represent an important share of the turnover realized by CS. Besides that, problematic components are critical to direct aircraft operations and have a demand that can be describes as not frequent and when occurring; low amounts. When demand and turn around time performance of 25 problematic components is simulated, the average stock-out probability (PSO) is 23% while the average turn around time reliability is 30%. This means 23% of the component requests cannot be delivered directly from stock while only 30% of the repairs are executed within determined turn around time-norms. These findings conflict with CS its objective to deliver 95% of the all component requests from stock (i.e. a maximum PSO of 5%) and repair 95% of all components within time-norms. Analyze process instability The reason for the high PSO and low TAT-reliability is analyzed. In the current situation, the probability to run out of stock is intensified by the high variance of repair time (TAT) of separate components. Three clear root-causes can be distinguished related to the variance in turn around time regarding problematic components. A first root-cause is the misalignment of repair priority between vendor control and the related workshop. This is especially the case with regard to the Engine Shop which mainly focuses on repairing engines instead of separate components. A second root-cause is the fraction of units of which the repair is dedicated to an internal workshop but whose repair is sometimes outsourced to an external vendor. The third root-cause is the fraction of forward exchange requests on a component. Forward exchange requests originate from customers who want to receive a serviceable component first after which they return the defective counterpart. In a theoretical situation -with fixed turn around times are simulated- the demand variance intensifies the stock-out probability too. A root-cause for demand variance is the determined maintenance strategy of a component: components with a fixed maintenance interval (hard timers) show a more unstable demand pattern. The demand variance of the regarded components is independent of numerousness and heterogeneity of the customer population active on a component as well as the age of the component population itself. When absolute demand of these problematic components is analyzed, no clear influence is found of seasonality and the aircraft utilization rate. Absolute monthly demand has a direct relationship with monthly flight hours with concerning half of the analyzed components. The four deducted root-causes lead to the recognition of four problem fields. All problem fields intensify process instability and have different causes, but have a general solution in common: increase of TAT-reliability. This means, more components need to be returned into serviceable status, within the agreed time-norms. Improve availability management The most effective way to create stability in the process cycle is maintenance planning on component level. When maintenance is planned, components are removed according to a planning and thereby vendor control and workshops, both have balanced workloads. It is expected that this results in a higher TAT-reliability. Due to the fact that many stakeholders are related to maintenance planning, it is hard to realize this in practice. Therefore is requires further analysis. The most feasible way to create stability in the process cycle is proactive monitoring of components. This can be realized by the unit vendor control (VC) alone. VC is responsible for process cycle stability and monitors the repair and logistical processes related to component availability. In order to effectively decrease PSO by increasing the TAT-reliability, VC needs to utilize its reclamation capacity according to priority. This means it has to ensure that the components that have the highest probability to run out of stock are repaired first. Thereby VC dictates the repair order sequence of the workshops’ workload buffer. The repair order sequence is determined by a component’s minimal serviceable stock level and the deducted root-causes, which function as early warning indicators. It might be the case that VC and internal shops do not posses the required resources to realize the required TAT-reliability without the necessity to pay extra costs. Therefore a theoretical balance exists between the TAT-reliability and the expected PSO, resulting in the lowest total costs. This balance depends on the cost to recover a stock-out and the proactive (on-time) reclamation costs (i.e. the costs required to realize component repair within time-norms). To be able to make this trade-off accurately, further research needs to be done on costs drivers of availability management. Control proactive anticipating From the perspective of transparent performance measurement, a variable TAT-reliability per component or workshop is not desired. Therefore a TAT-reliability of 95% is a condition. When CS wants to realize a TAT-reliability of 95%, the proactive (on-time) reclamation costs remain for optimization towards lowest total costs. Two general measures can be applied to do that: realize more efficient component repair or reclamation (1) or adjust TAT-norms so that on-time repair requires less effort (2). Hence, vendor control proactively monitors components based on minimal serviceable stock and early warning indicators. As VC recognizes that the TAT-reliability of a component is hard to realize, internal shops are forced to take responsibility. As extensive additional costs are required, vendor control proposes to adjust TAT-norms and thereby increase stock levels. As a rule of thumb applies that as the costs of a stock-out are less than 11.5 times the costs of proactive (on-time) reclamation, TAT-norm adjustments are justified. To realize continuous improvement of the monitoring-effectivity, a method can be used that detects and analyzes process instability and assists to reduce it cost-effectively. Conclusion Component Services should proactively anticipate on process instability by monitoring those components that have the highest risk to run out of stock. A height stock-out probability is indicated by a components minimal serviceable stock and early warning indicators. Thereby it has to balance the on-time reclamation effort (i.e. workload) and PSO according to cost-ratios of those two variables. To realize continuous improvement of process stability, CS should use the general method of cost-effective process instability reduction. This method enables to detect and analyze process instability and assists to reduce it in a cost-effective way. When CS still wants to realize a TAT-reliability of 95% it can increase its reclaiming capacity by working more efficient or decrease the required effort by adjusting TAT-norm. Thereby it has to find a total cost optimum taking into account all related cost-drivers.

This paper is a research proposal to develop a tool to analyze logistic concepts of the air taxi service of Aeolus Aviation in different scenarios. Based on this analysis recommendations can be done for a suitable logistic concept for Aeolus. Based on background analysis of the air taxi service three objectives are formulated; analyzing the air taxi service on strategic level, developing a decision support tool to analyze logistic concepts and finally developing a suitable logistic concept for Aeolus Aviation. Based on these objective research questions are formulated and research methods to answer these questions are given.

Improving service level at the lowest possible costs is and will always be one of the key objectives of Philips Electronics. This research illustrates how a part of the Philips supply chain control is setup /designed to support this objective. Within Philips, the business unit called Lumileds supplies LED’s to its customers. One of the components used to manufacture LED’s is made at the component manufacturer that is subject to this thesis. The objective of improving service levels at the lowest possible cost can be enabled by supply chain control. Of course superior service levels can be realized with excessive inventory levels. However that ignores the objective of lowest costs, because inventory cost money. Therefore an optimal balance between service level and inventory must be achieved. This balance depends on a number of different drivers like lead-time, lead-time variability, manufacturing quality and demand pattern. This research will define a model that generates advice to achieve the desired balance, taking into account all relevant drivers.