This paper presents an innovative approach that combines optimization and simulation techniques for solving scheduling problems under uncertainty. We introduce an Opt–Sim closed-loop feedback framework (Opt–Sim) based on a sliding-window method, where a simulation model is used for evaluating the optimized solution with inherent uncertainties for scheduling activities. The specific problem tackled in this paper, refers to the airport capacity management under uncertainty, and the Opt–Sim framework is applied to a real case study (Paris Charles de Gaulle Airport, France). Different implementations of the Opt–Sim framework were tested based on: parameters for driving the Opt–Sim algorithmic framework and parameters for riving the optimization search algorithm. Results show that, by applying the Opt–Sim framework, potential aircraft conflicts could be reduced up to 57% over the non-optimized scenario. The proposed optimization framework is general enough so that different optimization resolution methods and simulation paradigms can be implemented for solving scheduling problems in several other fields.
For long flights, the cruise is the longest phase and where the largest amount of fuel is consumed. An in-cruise optimization method has been implemented to calculate the optimal trajectory that reduces the flight cost. A three-dimensional grid has been created, coupling lateral navigation and vertical navigation profiles. With a dynamic analysis of the wind, the aircraft can perform a horizontal deviation or change altitudes via step climbs to reduce fuel consumption. As the number of waypoints and possible step climbs is increased, the number of flight trajectories increases exponentially; thus, a genetic algorithm has been implemented to reduce the total number of calculated trajectories compared to an exhaustive search. The aircraft’s model has been obtained from a performance database, which is currently used in the commercial flight management system studied in this paper. A 5% average flight cost reduction has been obtained.
MULTIFILE
Food security depends on a network of actors and elements working together to produce and deliver healthy, sustainable, varied, safe and plentiful food supply to society. The interactions between these actors and elements must be designed, managed and optimized to satisfy demand. In this chapter we introduce Food Supply Chain Optimization and Demand, providing a framework to understand and improve food security from an operational and strategic point of view.
In order to stay competitive and respond to the increasing demand for steady and predictable aircraft turnaround times, process optimization has been identified by Maintenance, Repair and Overhaul (MRO) SMEs in the aviation industry as their key element for innovation. Indeed, MRO SMEs have always been looking for options to organize their work as efficient as possible, which often resulted in applying lean business organization solutions. However, their aircraft maintenance processes stay characterized by unpredictable process times and material requirements. Lean business methodologies are unable to change this fact. This problem is often compensated by large buffers in terms of time, personnel and parts, leading to a relatively expensive and inefficient process. To tackle this problem of unpredictability, MRO SMEs want to explore the possibilities of data mining: the exploration and analysis of large quantities of their own historical maintenance data, with the meaning of discovering useful knowledge from seemingly unrelated data. Ideally, it will help predict failures in the maintenance process and thus better anticipate repair times and material requirements. With this, MRO SMEs face two challenges. First, the data they have available is often fragmented and non-transparent, while standardized data availability is a basic requirement for successful data analysis. Second, it is difficult to find meaningful patterns within these data sets because no operative system for data mining exists in the industry. This RAAK MKB project is initiated by the Aviation Academy of the Amsterdam University of Applied Sciences (Hogeschool van Amsterdan, hereinafter: HvA), in direct cooperation with the industry, to help MRO SMEs improve their maintenance process. Its main aim is to develop new knowledge of - and a method for - data mining. To do so, the current state of data presence within MRO SMEs is explored, mapped, categorized, cleaned and prepared. This will result in readable data sets that have predictive value for key elements of the maintenance process. Secondly, analysis principles are developed to interpret this data. These principles are translated into an easy-to-use data mining (IT)tool, helping MRO SMEs to predict their maintenance requirements in terms of costs and time, allowing them to adapt their maintenance process accordingly. In several case studies these products are tested and further improved. This is a resubmission of an earlier proposal dated October 2015 (3rd round) entitled ‘Data mining for MRO process optimization’ (number 2015-03-23M). We believe the merits of the proposal are substantial, and sufficient to be awarded a grant. The text of this submission is essentially unchanged from the previous proposal. Where text has been added – for clarification – this has been marked in yellow. Almost all of these new text parts are taken from our rebuttal (hoor en wederhoor), submitted in January 2016.
TU Delft, in collaboration with Gravity Energy BV, has conducted a feasibility study on harvesting electric energy from wind and vibrations using a wobbling triboelectric nanogenerator (WTENG). Unlike conventional wind turbines, the WTENG converts wind/vibration energy into contact-separation events through a wobbling structure and unbalanced mass. Initial experimental findings demonstrated a peak power density of 1.6 W/m² under optimal conditions. Additionally, the harvester successfully charged a 3.7V lithium-ion battery with over 4.5 μA, illustrated in a self-powered light mast as a practical demonstration in collaboration with TimberLAB. This project aims to advance this research by developing a functioning prototype for public spaces, particularly lanterns, in partnership with TimberLAB and Gravity Energy. The study will explore the potential of triboelectric nanogenerators (TENG) and piezoelectric materials to optimize energy harvesting efficiency and power output. Specifically, the project will focus on improving the WTENG's output power for practical applications by optimizing parameters such as electrode dimensions and contact-separation quality. It will also explore cost-effective, commercially available materials and best fabrication/assembly strategies to simplify scalability for different length scales and power outputs. The research will proceed with the following steps: Design and Prototype Development: Create a prototype WTENG to evaluate energy harvesting efficiency and the quantity of energy harvested. A hybrid of TENG and piezoelectric materials will be designed and assessed. Optimization: Refine the system's design by considering the scaling effect and combinations of TENG-piezoelectric materials, focusing on maximizing energy efficiency (power output). This includes exploring size effects and optimal dimensions. Real-World Application Demonstration: Assess the optimized system's potential to power lanterns in close collaboration with TimberLAB, DVC Groep BV and Gravity Energy. Identify key parameters affecting the efficiency of WTENG technology and propose a roadmap for its exploitation in other applications such as public space lighting and charging.
The growing energy demand and environmental impact of traditional sources highlight the need for sustainable solutions. Hydrogen produced through water electrolysis, is a flexible and clean energy carrier capable of addressing large-electricity storage needs of the renewable but intermittent energy sources. Among various technologies, Proton Exchange Membrane Water Electrolysis (PEMWE) stands out for its efficiency and rapid response, making it ideal for grid stabilization. In its core, PEMWEs are composed of membrane electrode assemblies (MEA), which consist of a proton-conducting membrane sandwiched between two catalyst-coated electrodes, forming a single PEMWE cell unit. Despite the high efficiency and low emissions, a principal drawback of PEMWE is the capital cost due to high loading of precious metal catalysts and protective coatings. Traditional MEA catalyst coating methods are complex, inefficient, and costly to scale. To circumvent these challenges, VSParticle developed a technology for nanoparticle film production using spark ablation, which generates nanoparticles through high-voltage discharges between electrodes followed by an impaction printing module. However, the absence of liquids poses challenges, such as integrating polymeric solutions (e.g., Nafion®) for uniform, thicker catalyst coatings. Electrohydrodynamic atomization (EHDA) stands out as a promising technique thanks to its strong electric fields used to generate micro- and nanometric droplets with a narrow size distribution. Co-axial EHDA, a variation of this technique, utilizes two concentric needles to spray different fluids simultaneously.The ESPRESSO-NANO project combines co-axial EHDA with spark ablation to improve catalyst uniformity and performance at the nanometer scale by integrating electrosprayed ionomer nanoparticles with dry metal nanoparticles, ensuring better distribution of the catalyst within the nanoporous layer. This novel approach streamlines numerous steps in traditional synthesis and electrocatalyst film production which will address material waste and energy consumption, while simultaneously improve the electrochemical efficiency of PEMWEs, offering a sustainable solution to the global energy crisis.
