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Air traffic controllers (ATCOs) play a fundamental role in the safe, orderly and efficient management of air traffic. In the interests of improving safety, it would be beneficial to know what the workload thresholds are that permit ATCOs to carry out their functions safely and efficiently. The purpose of this paper is to present the development of a simulation platform to be able to validate an affective-cognitive performance methodology based on neurophysiological factors applied to ATCOs, to define the said thresholds.

The process followed in setting up the simulation platform is explained, with particular emphasis on the design of the program of exercises. The tools designed to obtain additional information on the actions of ATCOs and how their workload will be evaluated are also explained.

To establish the desired methodology, a series of exercises has been designed to be simulated. This paper describes the project development framework and validates it, taking preliminary results as a reference. The validation of the framework justifies further study to extend the preliminary results.

This paper describes the first part of the project only, i.e. the definition of the problem and a proposed methodology to arrive at a workable solution. Further work will concentrate on carrying out a program of simulations and subsequent detailed analysis of the data obtained, based on the conclusions drawn from the preliminary results presented.

The methodology will be an important tool from the point of view of safety and the work carried out by ATCOs. This first phase is crucial as it provides a solid foundation for later stages.

This work analyses autonomous ships' specific needs of external and environmental information in restricted pilotage waters. Harbour pilots use conventional well-tested techniques when piloting the manned vessel. In this work, the authors propose technological solutions to be installed or adapted in ports to feed the autonomous ships' systems with the information considered relevant by pilots.

To investigate what pilots consider relevant, the authors submitted a questionnaire to the pilotage of Paranaguá Port. Then the authors presented a case study including the critical areas for the navigation of ships.

These technological solutions aim to allow vessels reaching critical areas in a position, a time, a speed and a course that compensates the external forces and/or avoid high-risk situations. The authors have proposed technological solutions considering those already available in the ports, particularly in Paranaguá and Antonina in Brazil.

There is little published data on navigation of ships in pilotage waters. So far, there has been little discussion on autonomous ships in restricted waters.

Marksmanship data is a staple of military and law enforcement evaluations. This ubiquitous nature creates a critical need to use all relevant information and to convey outcomes in a meaningful way for the end users. The purpose of this study is to demonstrate how simple simulation techniques can improve interpretations of marksmanship data.

This study uses three simulations to demonstrate the advantages of small arms combat modeling, including (1) the benefits of incorporating a Markov Chain into Monte Carlo shooting simulations; (2) how small arms combat modeling is superior to point-based evaluations; and (3) why continuous-time chains better capture performance than discrete-time chains.

The proposed method reduces ambiguity in low-accuracy scenarios while also incorporating a more holistic view of performance as outcomes simultaneously incorporate speed and accuracy rather than holding one constant.

This process determines the probability of winning an engagement against a given opponent while circumventing arbitrary discussions of speed and accuracy trade-offs. Someone wins 70% of combat engagements against a given opponent rather than scoring 15 more points. Moreover, risk exposure is quantified by determining the likely casualties suffered to achieve victory. This combination makes the practical consequences of human performance differences tangible to the end users. Taken together, this approach advances the operations research analyses of squad-level combat engagements.

For more than a century, marksmanship evaluations have used point-based systems to classify shooters. However, these scoring methods were developed for competitive integrity rather than lethality as points do not adequately capture combat capabilities. The proposed method thus represents a major shift in the marksmanship scoring paradigm.

An integrated queueing network focused on container storage/retrieval operations occurring on the yard of a transshipment hub is proposed. The purpose of the network is to support decisions related to the organization of the yard area, while also accounting for operations policies and times on the quay.

A discrete-event simulation model is used to reproduce container handling on both the quay and yard areas, along with the transfer operations between the two. The resulting times, properly estimated by the simulation output, are fed to a simpler queueing network amenable to solution via algorithms based on mean value analysis (MVA) for product-form networks.

Numerical results justify the proposed approach for getting a fast, yet accurate analytical solution that allows carrying out performance evaluation with respect to both organizational policies and operations management on the yard area.

Practically, the expected performance measures on the yard subsystem can be obtained avoiding additional time-expensive simulation experiments on the entire detailed model.

As a major takeaway, deepening the MVA for generally distributed service times has proven to produce reliable estimations on expected values for both user- and system-oriented performance metrics.

Nonlinear solution approaches for inverse problems require fast simulation techniques for the underlying sensing problem. In this work, the authors investigate finite element (FE) based sensor simulations for the inverse problem of electrical capacitance tomography. Two known computational bottlenecks are the assembly of the FE equation system as well as the computation of the Jacobian. Here, existing computation techniques like adjoint field approaches require additional simulations. This paper aims to present fast numerical techniques for the sensor simulation and computations with the Jacobian matrix.

For the FE equation system, a solution strategy based on Green’s functions is derived. Its relation to the solution of a standard FE formulation is discussed. A fast stiffness matrix assembly based on an eigenvector decomposition is shown. Based on the properties of the Green’s functions, Jacobian operations are derived, which allow the computation of matrix vector products with the Jacobian for free, i.e. no additional solves are required. This is demonstrated by a Broyden–Fletcher–Goldfarb–Shanno-based image reconstruction algorithm.

MATLAB-based time measurements of the new methods show a significant acceleration for all calculation steps compared to reference implementations with standard methods. E.g. for the Jacobian operations, improvement factors of well over 100 could be found.

The paper shows new methods for solving known computational tasks for solving inverse problems. A particular advantage is the coherent derivation and elaboration of the results. The approaches can also be applicable to other inverse problems.

Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

The purpose of this research is to explore the utility of autonomous transport across two independent airframe maintenance operations at a single location.

This study leveraged discrete event simulation that encompassed real-world conditions on a United States Air Force flight line. Though the Theory of Constraints (TOC) lens, a high-demand, human-controlled delivery asset is analyzed and the impact of introducing an autonomous rover delivery vehicle is assessed. The authors’ simulations explored varying numbers and networks of rovers as alternative sources of delivery and evaluated these resources’ impact against current flight line operations.

This research indicates that the addition of five autonomous rovers can significantly reduce daily expediter delivery tasks, which results in additional expertise necessary to manage and execute flight line operations. The authors assert that this relief would translate into enhancements in aircraft mission capable rates, which could increase overall transport capacity and cascade into faster cargo delivery times, systemwide. By extension, the authors suggest overall inventory management could be improved through reduction in transportation shipping time variance, which enhances the Department of Defense’s overall supply chain resilience posture.

When compared against existing practices, this novel research provides insight into actual flight line movement and the potential benefits of an alternative autonomous delivery system. Additionally, the research measures the potential savings in the workforce and vehicle use that exceeds the cost of the rovers and their employment.

This paper aims to propose a simulation-based performance evaluation model for the drone-based delivery of aid items to disaster-affected areas. The objective of the model is to perform analytical studies, evaluate the performance of drone delivery systems for humanitarian logistics and can support the decision-making on the operational design of the system – on where to locate drone take-off points and on assignment and scheduling of delivery tasks to drones.

This simulation model captures the dynamics and variabilities of the drone-based delivery system, including demand rates, location of demand points, time-dependent parameters and possible failures of drones’ operations. An optimization model integrated with the simulation system can update the optimality of drones’ schedules and delivery assignments.

An extensive set of experiments was performed to evaluate alternative strategies to demonstrate the effectiveness for the proposed optimization/simulation system. In the first set of experiments, the authors use the simulation-based evaluation tool for a case study for Central Florida. The goal of this set of experiments is to show how the proposed system can be used for decision-making and decision-support. The second set of experiments presents a series of numerical studies for a set of randomly generated instances.

The goal is to develop a simulation system that can allow one to evaluate performance of drone-based delivery systems, accounting for the uncertainties through simulations of real-life drone delivery flights. The proposed simulation model captures the variations in different system parameters, including interval of updating the system after receiving new information, demand parameters: the demand rate and their spatial distribution (i.e. their locations), service time parameters: travel times, setup and loading times, payload drop-off times and repair times and drone energy level: battery’s energy is impacted and requires battery change/recharging while flying.