The need to better understand how to manage the real logistics operations in Schiphol Airport, a strategic hub for the economic development of the Netherlands, created the conditions to develop a project where academia and industry partnered to build a simulation model of the Schiphol Airport Landside operations. This paper presents such a model using discrete-event simulation. A realistic representation of the open road network of the airport as well as the (un)loading dock capacities and locations of the five ground handlers of Schiphol Airport was developed. Furthermore, to provide practitioners with applicable consolidation and truck-dispatching policies, some easy-to-implement rules are proposed and implemented in the model. Preliminary results from this model show that truck-dispatching policies have a higher impact than consolidation policies in terms of both distance travelled by cooperative logistic operators working within the airport and shipments’ average flow time. Furthermore, the approach presented in this study can be used for studying similar megahubs.
We used a validated agent-based model—Socio-Emotional CONcern DynamicS (SECONDS)—to model real-time playful interaction between a child diagnosed with Autism Spectrum Disorders (ASD) and its parent. SECONDS provides a real-time (second-by-second) virtual environment that could be used for clinical trials and testingprocess-orientedexplanationsofASDsymptomatology.Weconductednumerical experiments with SECONDS (1) for internal model validation comparing two parental behavioral strategies for stimulating social development in ASD (play-centered vs. initiative-centered) and (2) for empirical case-based model validation. We compared 2,000 simulated play sessions of two particular dyads with (second-by-second) time-series observations within 29 play sessions of a real parent-child dyad with ASD on six variables related to maintaining and initiating play. Overall, both simuladistributions. Given the idiosyncratic behaviors expected in ASD, the observed correspondence is non-trivial. Our results demonstrate the applicability of SECONDS to parent-child dyads in ASD. In the future, SECONDS could help design interventions for parental care in ASDted dyads provided a better fit to the observed dyad than reference null
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.
MULTIFILE
In recent years, disasters are increasing in numbers, location, intensity and impact; they have become more unpredictable due to climate change, raising questions about disaster preparedness and management. Attempts by government entities at limiting the impact of disasters are insufficient, awareness and action are urgently needed at the citizen level to create awareness, develop capacity, facilitate implementation of management plans and to coordinate local action at times of uncertainty. We need a cultural and behavioral change to create resilient citizens, communities, and environments. To develop and maintain new ways of thinking has to start by anticipating long-term bottom-up resilience and collaborations. We propose to develop a serious game on a physical tabletop that allows individuals and communities to work with a moderator and to simulate disasters and individual and collective action in their locality, to mimic real-world scenarios using game mechanics and to train trainers. Two companies–Stratsims, a company specialized in game development, and Society College, an organization that aims to strengthen society, combine their expertise as changemakers. They work with Professor Carola Hein (TU Delft), who has developed knowledge about questions of disaster and rebuilding worldwide and the conditions for meaningful and long-term disaster preparedness. The partners have already reached out to relevant communities in Amsterdam and the Netherlands, including UNUN, a network of Ukrainians in the Netherlands. Jaap de Goede, an experienced strategy simulation expert, will lead outreach activities in diverse communities to train trainers and moderate workshops. This game will be highly relevant for citizens to help grow awareness and capacity for preparing for and coping with disasters in a bottom-up fashion. The toolkit will be available for download and printing open access, and for purchase. The team will offer training and facilitate workshops working with local communities to initiate bottom-up change in policy making and planning.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
The bi-directional communication link with the physical system is one of the main distinguishing features of the Digital Twin paradigm. This continuous flow of data and information, along its entire life cycle, is what makes a Digital Twin a dynamic and evolving entity and not merely a high-fidelity copy. There is an increasing realisation of the importance of a well functioning digital twin in critical infrastructures, such as water networks. Configuration of water network assets, such as valves, pumps, boosters and reservoirs, must be carefully managed and the water flows rerouted, often manually, which is a slow and costly process. The state of the art water management systems assume a relatively static physical model that requires manual corrections. Any change in the network conditions or topology due to degraded control mechanisms, ongoing maintenance, or changes in the external context situation, such as a heat wave, makes the existing model diverge from the reality. Our project proposes a unique approach to real-time monitoring of the water network that can handle automated changes of the model, based on the measured discrepancy of the model with the obtained IoT sensor data. We aim at an evolutionary approach that can apply detected changes to the model and update it in real-time without the need for any additional model validation and calibration. The state of the art deep learning algorithms will be applied to create a machine-learning data-driven simulation of the water network system. Moreover, unlike most research that is focused on detection of network problems and sensor faults, we will investigate the possibility of making a step further and continue using the degraded network and malfunctioning sensors until the maintenance and repairs can take place, which can take a long time. We will create a formal model and analyse the effect on data readings of different malfunctions, to construct a mitigating mechanism that is tailor-made for each malfunction type and allows to continue using the data, albeit in a limited capacity.
