This study focuses on teachers’ group decision making during Dutch allocation meetings. A previous interview study showed that teachers question the objectivity of decisions due to negative interaction experiences and a lack of structure during these meetings. To characterize the structure and interaction of these meetings, 33 student allocations were observed. Results showed a variety of structures and interactions, including differences in the degree to which the meetings met criteria relevant to achieving objective allocation decisions. It can be concluded that – based on the criteria of acceptance, fairness, and transparency as used in this study – allocation meetings need to be well-prepared and substantiated, to allow for every teacher’s opinion to be heard, and follow a procedure that is clear to everyone. In view of students’ future school careers, it is important to pay close attention to functional interaction and structured discussions that ensure transparent, acceptable and fair decision-making.
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This study examines the effect of seat assignment strategies on the transfer time of connecting passengers at a hub airport. Passenger seat allocation significantly influences disembarkation times, which can increase the risk of missed connections, particularly in tight transfer situations. We propose a novel seat assignment strategy that allocates seats to nonpaying passengers after check-in, prioritising those with tight connections. This approach diverges from traditional methods focused on airline turnaround efficiency, instead optimizing for passenger transfer times and reducing missed connections. Our simulation, based on real-world data from Paris-Charles de Gaulle airport, demonstrates that this passenger-centric model decreases missed connections by 12%, enhances service levels, reduces airline compensation costs, and improves airport operations. The model accounts for variables such as seat occupancy,luggage, and passenger type (e.g., business, leisure) and is tested under various scenarios, including air traffic delays.
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The purpose of this study is to investigate the impact that unevenly allocating buffer capacity has on throughput and average buffer level regarding unreliable lines to better understand the relevant factors in supply chain design. Results show that the best patterns for unreliable merging lines in terms of generating higher throughput rates (TR), as compared to a balanced merging line counterpart, are those where total available buffer capacity is allocated between workstations in either an inverted bowl pattern (i.e. concentrating buffer capacity towards the centre of the line), or a balanced line pattern. In contrast, when considering the trade-off between generating revenue resulting from TR and reducing cost created by average buffer levels (ABL), we found that the balanced pattern was not the best pattern. The best pattern was dependent on the length of the line and on the total buffer capacity as shorter lines with very constrained buffers were best served with an inverted bowl pattern while longer lines had the best results when applying an ascending buffer allocation pattern. Longer lines, in contrast, had the best results regarding the trade-off between TR and ABL, on average, by allocating buffer capacity evenly in one of the parallel lines while applying any other pattern in the remaining parallel line.
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The aeronautical industry is still under expansion in spite of the problems it is facing due to the increase in oil prices, limited capacity, and novel regulations. The expansion trends translate into problems at different locations within an airport system and are more evident when the resources to cope with the demand are limited or are reaching to theirs limits. In the check-in areas they are appreciated as excessive waiting times which in turn are appreciated by the customers as bad service levels. The article presents a novel methodology that combines an evolutionary algorithm and simulation in order to give the best results taking into account not only the mandatory hard and soft rules determined by the internal policies of an airport terminal but also the quality indicators which are very difficult to include using an abstract representation. The evolutionary algorithm is developed to satisfy the different mandatory restrictions for the allocation problem such as minimum and maximum number of check-in desks per flight, load balance in the check-in islands, opening times of check-in desks and other restrictions imposed by the level of service agreement. Once the solutions are obtained, a second evaluation is performed using a simulation model of the terminal that takes into account the stochastic aspects of the problem such as arriving profiles of the passengers, opening times physical configurations of the facility among other with the objective to determine which allocation is the most efficient in real situations in order to maintain the quality indicators at the desired level.
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Aviation increasingly faces capacity challenges exposing inefficiencies and shortcomings of aviation related processes and systems. The European slot allocation system was designed in an era with little to no capacity constraints, now resulting in regulations not fitting in today’s developments.
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Airport management is regularly challenged by the task of assigning flights to existing parking positions in the most efficient way while complying with existing policies, restrictions and capacity limitations. However, such process is frequently disrupted by various events, affecting punctuality of airline operations. This paper describes an innovative approach for obtaining an efficient stand assignment considering the stochastic nature of airport environment. Furthermore, the presented methodology combines benefits of Bayesian modelling and metaheuristics for generating solutions that are more robust to airport flight schedule perturbations. In addition, this paper illustrates that the application of the presented methodology combined with simulation provides a valuable tool for assessing the robustness of the developed stand assignment to flight delays.
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This study tackles the gate allocation problem (GAP) at the airport terminal, considering the current covid-19 pandemic restrictions. The GAP has been extensively studied by the research community in the last decades, as it represents a critical factor that determines an airport's capacity. Currently, the airport passenger terminal operations have been redesigned to be aligned and respect the covid-19 regulation worldwide. This provides operators with new challenges on how to handle the passengers inside the terminal. The purpose of this study is to come up with an efficient gate allocator that considers potential issues derived by the current pandemic, i.e., avoid overcrowded areas. A sim-opt approach has been developed where an evolutionary algorithm (EA) is used in combination with a dynamic passenger flow simulation model to find a feasible solution. The EA aims to find a (sub)optimal solution for the GAP, while the simulation model evaluates its efficiency and feasibility in a real-life scenario. To evaluate the potential of the Opt-Sim approach, it has been applied to a real airport case study.
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Background: Research into termination of long-term psychosocial treatment of mental disorders is scarce. Yearly 25% of people in Dutch mental health services receive long-term treatment. They account for many people, contacts, and costs. Although relevant in different health care systems, (dis)continuation is particularly problematic under universal health care coverage when secondary services lack a fixed (financially determined) endpoint. Substantial, unaccounted, differences in treatment duration exist between services. Understanding of underlying decisional processes may result in improved decision making, efficient allocation of scarce resources, and more personalized treatment.
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The aviation industry is expected to grow at a pace of 4 % per annum in the coming years, therefore it is necessary to have techniques that support the management of the resources at hand in the best possible way so that facility expansion is delayed as much as possible with the corresponding capital savings.
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