Landside operations in air cargo terminals consist of many freight forwarders (FFWs) delivering and picking up cargo at the capacity-constrained loading docks at the airport's ground handlers' (GHs) facilities. To improve the operations of the terminal and take advantage of their geographical proximity a small set of FFWs can build a coalition to consolidate stochastically-arriving shipments and share truck fleet capacity while other FFWs continue bringing cargo to the terminal in a non-cooperative manner. Results from a detailed discrete-event simulation model of the cargo landside operations in Amsterdam Aiport showed that all operational policies had trade-offs in terms of the average shipment cycle time of coalition FFWs, the average shipment cycle time of non-coalition FFWs, and the total distance traveled by the coalition fleet, suggesting that horizontal cooperation in this context was not always beneficial, contrary to what previous studies on horizontal cooperation have found. Since dock capacity constitutes a significant constraint on operations in air cargo hubs, this paper also investigates the effect of dock capacity utilization and horizontal cooperation on the performance of consolidation policies implemented by the coalition. Thus, we built a general model of the air cargo terminal to analyze the effects caused by dock capacity utilization without the added complexity of landside operations at Amsterdam Airport to investigate whether the results hold for more general scenarios. Results from the general simulation model suggest that, in scenarios where dock and truck capacity become serious constraints, the average shipment cycle times of non-coalition FFWs are reduced at the expense of an increase in the cycle times of FFWs who constitute the coalition. A good balance among all the performance measures considered in this study is reached by following a policy that takes advantage of consolidating shipments based on individual visits to GH.
The Interoceanic corridor of Mexico stands as a pivotal infrastructure project poised to significantly enhance Mexico's national and regional economy. Anticipated to start the operations in 2025 under the auspice of the national government, this corridor represents a strategic counterpart to the Panama Canal, which faces capacity constraints due to climate change and environmental impacts. Positioned as a promising alternative for transporting goods from Asia to North America, this corridor will offer a new transport route, yet its real operational capacity and spatial impacts remains uncertain. In this paper, the authors undertake a preliminary, informed analysis leveraging publicly available data and other specific information about infrastructure capacities and economic environment to forecast the potential throughput of this corridor upon full operationalization and in the future. Applying simulation techniques, the authors simulate the future operations of the corridor according to different scenarios to offer insights into its potential capacity and impacts. Furthermore, the paper delves into the opportunities and challenges that are inherent in this project and gives a comprehensive analysis of its potential impact and implications.
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The constant growth of air traffic, especially in Europe, is putting pressure on airports, which, in turn, are suffering congestion problems. The airspace surrounding airport, terminal manoeuvring area (TMA), is particularly congested, since it accommodates all the converging traffic to and from airports. Besides airspace, airport ground capacity is also facing congestion problems, as the inefficiencies coming from airspace operations are transferred to airport ground and vice versa. The main consequences of congestion at airport airspace and ground, is given by the amount of delay generated, which is, in turn, transferred to other airports within the network. Congestion problems affect also the workload of air traffic controllers that need to handle this big amount of traffic.This thesis deals with the optimization of the integrated airport operations, considering the airport from a holistic point of view, by including operations such as airspace and ground together. Unlike other studies in this field of research, this thesis contributes by supporting the decisions of air traffic controllers regarding aircraft sequencing and by mitigating congestion on the airport ground area. The airport ground operations and airspace operations can be tackled with two different levels of abstractions, macroscopic or microscopic, based on the time-frame for decision-making purposes. In this thesis, the airport operations are modeled at a macroscopic level.The problem is formulated as an optimization model by identifying an objective function that considers the amount of conflicts in the airspace and capacity overload on the airport ground; constraints given by regulations on separation minima between consecutive aircraft in the airspace and on the runway; decision variables related to aircraft entry time and entry speed in the airspace, landing runway and departing runway choice and pushback time. The optimization model is solved by implementing a sliding window approach and an adapted version of the metaheuristic simulated annealing. Uncertainty is included in the operations by developing a simulation model and by including stochastic variables that represent the most significant sources of uncertainty when considering operations at a macroscopic level, such as deviation from the entry time in the airspace, deviation in the average taxi time and deviation in the pushback time. In this thesis, optimization and simulation techniques are combined together by developing two methods that aim at improving the solution robustness and feasibility. The first method acts as a validation tool for the optimized solution, and it improves the robustness of solution by iteratively fine-tuning some of the optimization model input parameters. The second method embeds the optimization in a simulation environment by taking full advantage of the sliding window approach and creating a loop for a continuous improvement of the optimized solution at each window of the sliding window approach. Both methods prove to be effective by improving the performance, lowering the total amount of conflicts up to 23.33% for the first method and up to 11.2% for the second method, however, in contrast to the deterministic method, the two methods they are not able to achieve a conflict-free scenario due to the effect of uncertainty.In general, the research conducted in this thesis highlights that uncertainty is a factor that affects to a large extent the feasibility of optimized solution when applied to real-world instances, and it, moreover, confirms that using simulation together with optimization has the potentiality toivdeal with uncertainty. The framework developed can be potentially applied to similar problems and different optimization solving methods can be adapted to it.Keywords: Optimization, Simulation, Integrated airport operations, Uncertainty
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ATAL: Automated Transport and Logistics Automatisering van transportmodaliteiten is overal ter wereld gaande. Met een Duurzaam Living Lab kunnen multimodale geautomatiseerde transportoperaties verder in de praktijk duurzaam en opschaalbaar worden ontwikkeld. Hierbij worden beleidsmakers en organisaties ondersteund in deze transitie. De maatschappelijke voordelen van grootschalige uitrol van Automated Trucks en Platooning, Automated Train Operations en Autonomous Sailing zijn onder andere minder energieverbruik en emissies, betere doorstroming en betere verkeersveiligheid. De Duurzame Living Lab heeft betrekking op het haven-achterland vervoer van Rotterdam richting Duitsland en België. Het wegvervoer maakt gebruik van de TULIP-Corridor, water en spoor modaliteit volgen de MIRT goederencorridors tot in het Ruhrgebied.
Client: Foundation Innovation Alliance (SIA - Stichting Innovatie Alliantie) with funding from the ministry of Education, Culture and Science (OCW) Funder: RAAK (Regional Attention and Action for Knowledge circulation) The RAAK scheme is managed by the Foundation Innovation Alliance (SIA - Stichting Innovatie Alliantie) with funding from the ministry of Education, Culture and Science (OCW). Early 2013 the Centre for Sustainable Tourism and Transport started work on the RAAK-MKB project ‘Carbon management for tour operators’ (CARMATOP). Besides NHTV, eleven Dutch SME tour operators, ANVR, HZ University of Applied Sciences, Climate Neutral Group and ECEAT initially joined this 2-year project. The consortium was later extended with IT-partner iBuildings and five more tour operators. The project goal of CARMATOP was to develop and test new knowledge about the measurement of tour package carbon footprints and translate this into a simple application which allows tour operators to integrate carbon management into their daily operations. By doing this Dutch tour operators are international frontrunners.Why address the carbon footprint of tour packages?Global tourism contribution to man-made CO2 emissions is around 5%, and all scenarios point towards rapid growth of tourism emissions, whereas a reverse development is required in order to prevent climate change exceeding ‘acceptable’ boundaries. Tour packages have a high long-haul and aviation content, and the increase of this type of travel is a major factor in tourism emission growth. Dutch tour operators recognise their responsibility, and feel the need to engage in carbon management.What is Carbon management?Carbon management is the strategic management of emissions in one’s business. This is becoming more important for businesses, also in tourism, because of several economical, societal and political developments. For tour operators some of the most important factors asking for action are increasing energy costs, international aviation policy, pressure from society to become greener, increasing demand for green trips, and the wish to obtain a green image and become a frontrunner among consumers and colleagues in doing so.NetworkProject management was in the hands of the Centre for Sustainable Tourism and Transport (CSTT) of NHTV Breda University of Applied Sciences. CSTT has 10 years’ experience in measuring tourism emissions and developing strategies to mitigate emissions, and enjoys an international reputation in this field. The ICT Associate Professorship of HZ University of Applied Sciences has longstanding expertise in linking varying databases of different organisations. Its key role in CARMATOP was to create the semantic wiki for the carbon calculator, which links touroperator input with all necessary databases on carbon emissions. Web developer ibuildings created the Graphical User Interface; the front end of the semantic wiki. ANVR, the Dutch Association of Travel Agents and Tour operators, represents 180 tour operators and 1500 retail agencies in the Netherlands, and requires all its members to meet a minimum of sustainable practices through a number of criteria. ANVR’s role was in dissemination, networking and ensuring CARMATOP products will last. Climate Neutral Group’s experience with sustainable entrepreneurship and knowledge about carbon footprint (mitigation), and ECEAT’s broad sustainable tourism network, provided further essential inputs for CARMATOP. Finally, most of the eleven tour operators are sustainable tourism frontrunners in the Netherlands, and are the driving forces behind this project.
Production processes can be made ‘smarter’ by exploiting the data streams that are generated by the machines that are used in production. In particular these data streams can be mined to build a model of the production process as it was really executed – as opposed to how it was envisioned. This model can subsequently be analyzed and stress-tested to explore possible causes of production prob-lems and to analyze what-if scenarios, without disrupting the production process itself. It has been shown that such models can successfully be used to diagnose possible causes of production problems, including scrap products and machine defects. Ideally, they can even be used to model and analyze production processes that have not been implemented yet, based on data from existing production pro-cesses and techniques from artificial intelligence that can predict how the new process is likely to be-have in practice in terms of data that its machines generate. This is especially important in mass cus-tomization processes, where the process to create each product may be unique, and can only feasibly be tested using model- and data-driven techniques like the one proposed in this project. Against this background, the goal of this project is to develop a method and toolkit for mining, mod-elling and analyzing production processes, using the time series data that is generated by machines, to: (i) analyze the performance of an existing production process; (ii) diagnose causes of production prob-lems; and (iii) certify that a new – not yet implemented – production process leads to high-quality products. The method is developed by researching and combining techniques from the area of Artificial Intelli-gence with techniques from Operations Research. In particular, it uses: process mining to relate time series data to production processes; queueing networks to determine likely paths through the produc-tion processes and detect anomalies that may be the cause of production problems; and generative adversarial networks to generate likely future production scenarios and sample scenarios of production problems for diagnostic purposes. The techniques will be evaluated and adapted in implementations at the partners from industry, using a design science approach. In particular, implementations of the method are made for: explaining production problems; explaining machine defects; and certifying the correct operation of new production processes.
