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Carbon dioxide concentration in enclosed spaces is an air quality indicator that affects occupants' well-being. To maintain healthy carbon dioxide levels indoors, enclosed space settings must be adjusted to maximize air quality while minimizing energy consumption. Studying the effect of these settings on carbon dioxide concentration levels is not feasible through physical experimentation and data collection. This problem can be solved by using validated simulation models, generating indoor settings scenarios, simulating those scenarios, and studying results. In previous work, we presented a formal Cellular Discrete Event System Specifications simulation model for studying carbon dioxide dispersion in rooms with various settings. However, designers may need to predict the results of altering large combinations of settings on air quality. Generating and simulating multiple scenarios with different combinations of space settings to test their effect on indoor air quality is time-consuming. In this research, we solve the two problems of the lack of ground truth data and the inefficiency of producing and studying simulation results for many combinations of settings by proposing a novel framework. The framework utilizes a Cellular Discrete Event System Specifications model, simulates different scenarios of enclosed spaces with various settings, and collects simulation results to form a data set to train a deep neural network. Without needing to generate all possible scenarios, the trained deep neural network is used to predict unknown settings of the closed space when other settings are altered. The framework facilitates configuring enclosed spaces to enhance air quality. We illustrate the framework uses through a case study. [ABSTRACT FROM AUTHOR]

An important step in the development of any process is evaluation. Evaluation ensures that the process affects effectiveness, efficiency, or other metrics as designed. Without evaluation, processes must stand solely on the theories, axioms, and/or heuristics that formed them. Outcome or objectives-based evaluations are especially useful in seeing if the expected impact is realized. Systems and design engineering present problems for outcome evaluations due to system design efforts being long in duration, expensive, organization/team specific, and environment/context specific. These characteristics make repeating the same effort under the same conditions using different processes difficult, if not impossible. While a comparison of processes for evaluation in a real application may not be feasible, a simulation of the processes with agents that capture system design behaviors may produce findings to support hypotheses. This paper examines the use of agent-based modeling and simulation to compare pseudo-waterfall and pseudo-agile engineering processes for a simple design problem. The Function–Behavior–Structure (FBS) model of design is used along with empirical data from FBS studies to examine the performance of a two-person design team using pseudo-waterfall and pseudo-agile engineering processes. The results of this exercise show possible advantages of agile-like processes in total time to complete the design. [ABSTRACT FROM AUTHOR]

The relationship between military expenditures and economic productivity has taken the attention of many researchers and there exist an important number of studies approaching the topic through several techniques. However, there is no consensus among the scholars whether military expenditures trigger economic growth, productivity, and other macroeconomic indicators. Such arguments are mainly due to unclear results obtained from the existing studies, in which the complex relationships between military expenditures and macroeconomics are not fully incorporated. Considering the bidirectional and nonlinear relationships among macroeconomic indicators and complex feedback mechanisms, a system dynamics (SD) model for examining the impacts of military expenditures on economic productivity in Turkey is proposed. The proposed SD model aims to reflect the complex environment surrounding the military spending–economic productivity nexus and to analyze the feedback structures that lead to miscellaneous consequences with delays. A stock–flow model is developed to represent the complex nonlinear relationships and causalities between the variables. Data from SIPRI, the World Bank, and several local statistical sources covering the years 2009–2018 are utilized to simulate the existing case, warfare in neighbors, economic shrinkage scenarios, and the combination of the latter two. The results obtained from the scenarios suggest that short fixes such as importing military products instead of national investments give rise to chronic issues like continual dependence on foreign supply, hence, leading to decrease in overall economic growth. To the best of our knowledge, this is the first attempt to integrate SD methodology with military expenditure and economic productivity analysis. [ABSTRACT FROM AUTHOR]

In the face of developments in maritime transportation due to globalization, ports are tending toward new tugboat investments in order to respond to the increasing demand. Due to the high costs of tugboats, organizations may face high investment costs. It is important to determine the most suitable number of tugboats, considering the variables in a port area where towage services will be provided. In this study, a simulation model was developed to determine sufficient tugboat allocation according to some variables in ports. The simulation model was subjected to various experiments, and statistical analyses of the obtained results were performed. The relationships between the variables affecting the level of towage service were revealed. [ABSTRACT FROM AUTHOR]

Multi-section continuum robots' (CRs) behavior is still an outstanding problem because of the highly non-linearity of its equation of motions. To this end, in this paper, particle swarm optimization (PSO) is adopted to solve the inverse kinematic model (IKM) of CRs. First, the CR's structure is properly described. Then, the aforementioned algorithm is elaborately discussed and implemented in figuring out the IKM of CR and verified through forward kinematic model by choosing the PSO parameters, namely, cognitive factors (C 1 = C 2 = 1. 2) and inertia weight (ω = 0. 79) for 200 positions on an arc-like trajectory. The optimal angle values (θ = 0. 0346 and φ = 0. 00013) which ensure the lowest distance between the attainably desired position and the robot's end effector are 1. 04497 × 10 − 9 mm which is perfectly accurate. After that, simulation through MATLAB is carried out, namely, in the first simulation, a three-section CR follows a linear trajectory with a precision approximately equal to 0. 75 × 10 − 9 mm. Furthermore, PSO takes 7 ms as a mean consumption time to make the robot's end effector attain to each position. Then, a circular trajectory is followed using PSO. Comparatively speaking, PSO is compared with four meta-heuristic approaches; it is remarked that PSO is a good compromise between accuracy and time consumption. Based on the obtained results, PSO can be considered as a trade-off between accuracy and time consumption for solving the IKM of CRs with complex structure. [ABSTRACT FROM AUTHOR]

The increase in demand for outpatient departments (OPDs) has contributed to overcrowded clinics and patient dissatisfaction. Computer simulation can help decision-makers meet the operational challenge of balancing the demand for outpatient services with considerations of available capacity. The paper presents a synthesis of the literature on simulation modeling in OPD using two approaches: a bibliometric analysis (employing keyword co-occurrence network) and a literature classification focusing on OPD strategy, OPD performance measures, and simulation techniques. Our review is based on 161 papers, published between 2006 and 2020, identified through a methodological search of the literature. The objective of the review is threefold: (1) to identify the major and emerging research issues in general and specialized OPD, (2) to find the commonly used performance measures in OPD and how it is associated with the strategies used to improve the performance, and (3) to identify the commonly used simulation methods for OPD modeling. A key finding from the bibliometric analysis is that most OPD research can be classified under one of the four clusters—"organization and management,""patient satisfaction,""overbooking," and "performance." We also find that patient waiting time has received much attention among the performance measures reported in the literature, followed by server idle time/overtime (server here is the OPD consultant or other healthcare resource). Our review serves as a key reference point for scholars, practitioners, students, and healthcare stakeholders, and those who use quantitative tools to aid operational decision-making. [ABSTRACT FROM AUTHOR]

This paper presents the development of a dynamic positioning in ice validation platform (DPIVP) which is part of a larger research project aimed at developing dynamic positioning (DP) system technologies for ice-rich environments. One outcome is simulation software to aid research in this area. The DPIVP software was designed to realistically simulate the dynamics of ice-structure interactions for real-time applications and to validate components common to DP in ice simulations. The software consists of many components which the DPIVP ties together as a unified system. All components have well-defined interfaces. Many of them are also distributed, allowing execution on separate computers and/or CPUs which helps ensure real-time operation. These two characteristics also decreases coupling and encourages a more modular design with the benefit of easily substituting alternative component implementations without reprogramming the DPIVP. Alternate implementations are useful for conducting research in specific DP in ice areas without substantially changing the system, such as alternative ice force models, DP control algorithms, vessel models, 3D and 2D visualization, environment models, and data acquisition systems. The integrated system was tested and evaluated using unit testing, integration testing, and system testing. The completed system was also validated using test cases that match physical model tests; the results compared favorably. Although the software has some limitations, for example, validated ice-force models being limited to two vessels, and thus lacks the generality we wish, the end result is a working prototype that satisfies the research requirements and provides an architecture and framework for future development. [ABSTRACT FROM AUTHOR]

This article describes the advances in the design, implementation, and evaluation of efficient algorithms for self-gravity simulations in astronomical agglomerates. Three algorithms are presented and evaluated: the occupied cells method, and two variations of the Barnes–Hut method using an octal and a binary tree. Two scenarios are considered in the evaluation: two agglomerates orbiting each other and a collapsing cube. The results show that the proposed octal tree Barnes–Hut method allows improving the performance of the self-gravity calculation up to 100 times with respect to the occupied cells method, while having good numerical accuracy. The proposed algorithms are efficient and accurate methods for self-gravity simulations in astronomical agglomerates. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) which began in Wuhan in December 2019 has permeated all over the world in such a short time and was declared as a pandemic by World Health Organization (WHO). The pandemic that is erupting all of a sudden attracts the researchers to examine the spread and effects of the disease as well as the possible treatments and vaccine developments. In addition to the analytical models, such as compartmental modeling, Markov decision process, and so on, simulation and system dynamics (SD) are also widely applied in this field. In this study, we adopt the compartmental modeling stages to build an SD approach for the spread of the disease. A dynamic control measure decision support system (DSS) that varies depending on the number of daily cases is incorporated to the model. Furthermore, the economic loss in the gross domestic product (GDP) and workforce due to hospital stay and death caused by the COVID-19 are also investigated. The model is tested with various numerical parameters and the results are presented. The results on the spread of the disease and the associated economic loss provide meaningful insights into when control measures need to be imposed at which level. We also provide some policy insights, including some alternative policies, such as increasing awareness of people and vaccination in addition to control measures. The results reveal that the total number of cases and deaths is approximately 37% higher in the absence of dynamic DSS. However, everything comes at a price and applying such control measures brings about an increase in the economic loss about 47%. [ABSTRACT FROM AUTHOR]

Despite advances in clinical care for the coronavirus (COVID-19) pandemic, population-wide interventions are vital to effectively manage the pandemic due to its rapid spread and the emergence of different variants. One of the most important interventions to control the spread of the disease is vaccination. In this study, an extended Susceptible-Infected Healed (SIR) model based on System Dynamics was designed, considering the factors affecting the rate of spread of the COVID-19 pandemic. The model predicts how long it will take to reach 70% herd immunity based on the number of vaccines administered. The designed simulation model is modeled in AnyLogic 8.7.2 program. The model was performed for three different vaccine supply scenarios and for Turkey with ~83 million population. The results show that, with a monthly supply of 15 million vaccines, social immunity reached the target value of 70% in 161 days, while this number was 117 days for 30 million vaccines and 98 days for 40 million vaccines. [ABSTRACT FROM AUTHOR]

Mission engineering is the quantification of the effects applied by a system of systems to achieve measurable desired results. The execution of the mission is defined by a mission thread; that is the sequence of actions/processes executed by elemental systems. The domain of complex missions has been described as "wicked" because traditional military and space program-based systems engineering practices fail due to a lack of discrete phases, a dependence on context, and the non-uniqueness of a "good-enough" mission thread. Wicked problems also tend to be unstructured (non-hierarchical) with no centralized control and do not lend themselves to linear step-by-step processes. Wicked problems are inherently uncertain leading to the broader issue of trust across a mission knowledge base, and any mission level analyses. Wicked problems are also characterized and challenged by combinatoric complexity. Mission success is primarily driven by the interrelationships between systems and not just by the individual systems themselves. The success of mission engineering will require an iterative approach of modeling, simulation, and analysis resulting in a continuous reduction in uncertainty and refinement in the topology of the mission thread. OODA-based decomposition of mission threads focused on Boyd's Orient function provides focus on system interrelationships. Trust provides decision-maker confidence in the results that has proved elusive to traditional validation approaches. The approach described in this paper is based upon using applied category theory as a basis for building mission threads, mission models, data stores, and integrating simulation ensembles. [ABSTRACT FROM AUTHOR]

Tanker ships arriving at liquid cargo terminals have to wait for getting service due to various operational restrictions in the terminal. Waiting time is the time passed until the tankers get service after the notice of readiness. The increase in the waiting times of tanker ships causes high demurrage costs. Reducing the waiting times of ships is of vital importance for the operational efficiency of the terminal. In this study, it is aimed to develop a simulation model in which alternative solutions in the cargo transfer operations can be tested to reduce the waiting times of tanker ships. The simulation model has several constraints that can be encountered in liquid cargo terminals, and it can be adapted to any liquid cargo terminal. The developed model was then applied to a liquid cargo terminal in Turkey. [ABSTRACT FROM AUTHOR]

Over the past 10 years, there has been an increase in the use of high-fidelity simulation (HFS) as a tool to support and enhance learning in health profession programs. In this article, we review the utilization of HFS in biomedical (basic science) courses for health professions students, and we compare its effectiveness to traditional teaching methods. Studies exploring the impact of HFS on students and residents were included in the review. The use of HFS is more prevalent in advanced clinical settings such as in training residents and nurses than in teaching students in preclinical years. When compared to traditional teaching methods, HFS is noted to be superior in delivering core biomedical concepts to students and healthcare professionals. However, a few studies showed no significant differences between HFS and traditional teaching methods when assessing clinical management skills. Overall, HFS is a valuable teaching tool which enhances knowledge retention and clinical skill acquisition in medical education. [ABSTRACT FROM AUTHOR]

The design of embedded real-time systems (RTS) is challenging due to the criticality of the timing constraints of these systems. Various informal and formal methods for RTS design have been proposed, both in the design space and the real-time execution at the hardware level, but many of these methods are not effective when the complexity of the system scales up. Here, we discuss a new method to integrate a modeling (and simulation) formalism that allows designing complex systems specifications for real-time constraints called Imprecise-DEVS (I-DEVS), and the mapping of such high-level models into a real-time task model. This method allows analyzing real-time constraints both at the high level of modeling as well as the low level of the tasks executed by the processing units and the Operating System. A new method to study the schedulability of the task models is proposed. The method provides a design analysis space from the model level, up to the individual tasks, with a focus on the schedulability of real-time constraints under transient overloading conditions. [ABSTRACT FROM AUTHOR]

Retrial queueing systems play a significant role in the modeling and performance evaluation of various dynamic stochastic systems, such as computer networks, airlines, communication and financial systems. This article presents a comprehensive assessment on the applicability of three popular variance reduction techniques (VRTs) in the performance evaluation of an M/M/1/K retrial queue based on shortest job first served (SJFS) retrial policy. The M/M/1/K retrial queue with SJFS retrial policy is not currently analytically tractable. Therefore, the credibility of such queueing systems is heavily dependent on simulations and accurate output data analysis. We seek to identify the best possible VRT so that a system performance evaluator can focus on optimizing the accuracy of relevant performance metrics under study. A comparative applicability analysis of three VRTs, i.e., antithetic variates, importance sampling, and control variates, on the retrial M/M/1/K queueing model has been presented. Our analysis showed a significant level of reduction in terms of variance and confidence interval size of the final estimates for the control variates technique. Furthermore, it has been found that unlike the other VRTs, the control variates technique shows a consistent trend in variance reductions as the number of retrials increases. [ABSTRACT FROM AUTHOR]

Although the citizens’ satisfaction in smart cities (CSSC) has become a vital criterion for smart cities, which symbolizes the people-centric concept, the previous research on simulating CSSC only considered the technological innovation and policy changes at the macro level, resulting in the neglect of the micro emotional contagion between citizens. To address this gap, a simulation approach is proposed to consider the influence caused by emotional contagion through the agent-based model (ABM). Supported by the expectation confirmation model (ECM) and the emotional contagion theory (ECT), the ABM simulation is implemented through the Anylogic platform, based on survey data from 19 smart cities in China. The results of various scenarios show that citizens’ expectations are the key factor affecting the CSSC, and the influence of emotional contagion cannot be ignored, especially in the acquaintance society. As social networks become more intimate, the impact of emotional contagion will turn the citizen group into an “antithetical society,” which means a situation with severe polarization of CSSC. This study also discusses the possible threshold of this transition process. Based on the analysis of multiple scenarios, it is implied that CSSC should be evaluated by considering the emotional contagion, and smart cities prioritize quality over quantity. [ABSTRACT FROM AUTHOR]

The transactional data typically collected/available on queueing systems are often subject to censoring as unsuccessful arrivals due to balking and/or unserved entities due to reneging are not recorded. In fact, in many situations, the true arrival, balking, and reneging events are unobservable, making it virtually impossible to collect data on these stochastic processes—information that is crucial for capacity planning and process improvement decisions. The objective of this paper is to estimate the true (latent) external arrival, balking, and reneging processes in queueing systems from such censored transactional data. The estimation problem is formulated as an optimization model and an iterative simulation-based inference approach is proposed to find appropriate input models for these stochastic processes. The proposed method is applicable in any complex queueing situation as long as it can be simulated. The problem is investigated under both known and unknown reneging distribution. Through extensive simulation experiments, general guidelines are provided for specifying the parameters of the proposed approach, namely, sample size and number of replications. The proposed approach is also validated through a real-world application in a call center, where it successfully estimates the underlying arrival, balking, and reneging distributions. Finally, to enable reproducibility and technology transfer, a working example, including all codes and sample data, are made available in an open online data repository associated with this paper. [ABSTRACT FROM AUTHOR]

The delay is a very important element in modeling hardware behavior, and is realized in many hardware description languages such as ADLIB-SABLE, Verilog, and VHDL. The state of the art on hardware delay identifies four classes. In the first class, mean values are used as a precise delay element in the simulation; we found it in VHDL (VHSIC (very high speed integrated circuit) Hardware Description Language), where a single value is utilized to characterize the transport delay. In the second class, the delay is represented by an interval min max, meaning that the delay value is precisely unknown and every value in the interval can represent a possible value for the actual delay. In the third class, a delay is expressed in the form of a stochastic distribution. Fuzzy models of delay constitute the last class. In reality, it is very difficult, if not impossible, to obtain a precise value of the delay; there are many reasons for that: temperature, voltage, variation in the manufacturing process, and other environment parameters. The Min Max DEVS formalism allows an efficient design of the min max delay by proposing a definition of the lifetime function based on time interval. Moreover, its simulation semantics allows the simulation of Min Max DEVS models with only one replication, allowing us to conclude whether the min max delay is too large or exact simulations cannot be obtained. In this paper, we propose to highlight the Min Max DEVS formalism through examples from digital circuits, after having recalled its basic definitions and its simulation semantics. Then, we compare the simulation results obtained with those provided by the well-known tool in the field of digital circuits, Verilog, using the same examples. [ABSTRACT FROM AUTHOR]

The paper delivers a tribute to Professor Giambiasi by pointing out how the work and ideas stemming from the research performed under his umbrella in the 1980s on behavioral testing has been an inspiration for the research around DEVS formalism at the University of Corsica in the late 1990s and the beginning of the new century. The paper retraces how Giambiasi's heritage allowed researchers at the University of Corsica to go from behavioral and hierarchical testing to DEVS fault simulation. [ABSTRACT FROM AUTHOR]

An optimization of the production process is defined as the search for solutions with improved production efficiency. Process optimization should be one of the main components of a business strategy that not only delivers benefits to customers but also helps increase the performance of production processes and benefits the entire business. The traditional approaches to job shop scheduling are based on the exact mathematically formalized model. If the number of model parameters is high and the environment is rather uncertain, the practical applications are quite restricted. That is why the theory proposes an approach based on a large-scale computer simulation. The main goal of this paper is to show that in a concrete company case, the simulation-based approach provides increased productivity. The presented study proposes the practical application of the Tecnomatix software used in the research to optimize the production system. The partial aims of the paper are as follows: (1) to create a simulation model of the production system with the help of the Plant Simulation module, (2) to model the current state of matters in the company, and (3) to propose a solution to the problem. Ultimately, we show that the simulation approach to the production line control provides rather effective solutions when compared to the intuitive one based on trial-and-error experience. The improvement includes a significant (1) shortening of the production cycle and (2) increase in productivity. [ABSTRACT FROM AUTHOR]

To help meet the ever-increasing demand for cloud computing services and resources worldwide, while providing resilience and adequate resource utilization, cloud service providers have opted to distribute their data centers around the world. This trend has been motivating research from the data center management research and practitioner community on new job schedulers that take into account data center geographical distribution. However, testing and benchmarking new schedulers for geo-distributed data centers is complicated by the lack of a common, easily extensible experimental platform. To fill this gap, we propose GDSim, an open-source, extensible job scheduling simulation environment for geo-distributed data centers that aims at facilitating the benchmarking of existing and new geo-distributed schedulers by subjecting them to a variety of data center features and conditions We use our geo-distributed job scheduler simulation platform to reproduce experiments and results for recently proposed geo-distributed job schedulers, as well as testing those schedulers under new conditions which can reveal trends that have not been previously uncovered. [ABSTRACT FROM AUTHOR]

Supply chain planning and control approaches need to include a wide range of factors in order to optimize production. Supply chain simulation modeling has been identified as a potential methodology toward increasing the efficiency of current systems to this end. The purpose of this paper is to evaluate the impact of inventory management decisions on supply chain performance using a Colored Petri Net based simulation modeling method. The presented method uses hierarchical timed Colored Petri Nets to model inventory management in a multi-stage serial supply chain, under normal operating conditions, and under the presence of disruptions, for both traditional and information sharing configurations. Disruptions are introduced as canceled orders and canceled deliveries, in a time period. Supply chain performance has been evaluated, in the context of order variance amplification and stockout amplification. Validation of the method is done by comparing results obtained for the bullwhip effect with published literature results, as well as by state space analysis results. [ABSTRACT FROM AUTHOR]

Warehouse design is critical in the air cargo industry, where service standards are high and competition is fierce. The designers of air cargo warehouses consider various criteria like costs, system failure risks, customer perception, and marketing power to evaluate alternative designs. The issue of designing an air cargo warehouse is generally considered by design and consulting companies, which have broad experience in operational design are lacking in suggesting robust alternatives that build upon on a well-structured theoretical framework. The alternative warehouse designs are generated by considering different combinations of resource capacities such as the number of gates, workstations and storage areas. The evaluation of these alternative designs requires the use of analytical methodologies for multi-criteria decision-making. At this point, two methodologies from literature may be helpful: (i) simulation, which is a popular tool to evaluate operational performance and (ii) the analytical hierarchy process (AHP), which provides the ability to evaluate qualitative as well as quantitative criteria. In this study, a flexible and user-friendly decision support system (DSS) is developed based on combining simulation and AHP. The DSS is used to generate design alternatives, evaluate their quantitative performance via simulation, and subsequently rank the best scoring ones via AHP according to a set of quantitative as well as qualitative decision criteria. The graphical interfaces are designed with a keen eye on man-machine interaction to increase their functionality. The environment is tested and validated with real-time data in one of Europe's largest air cargo carriers. [ABSTRACT FROM AUTHOR]

Wind turbines experience stochastic loading due to seasonal variations in wind speed and direction. These harsh operational conditions lead to failures of wind turbines, which are difficult to predict. Consequently, it is challenging to schedule maintenance actions that will avoid failures. In this article, a simulation-driven online maintenance scheduling algorithm for wind farm operational planning is derived. Online scheduling is a suitable framework for this problem since it integrates data that evolve over time into the maintenance scheduling decisions. The computational study presented in this article compares the performance of the simulation-driven online scheduling algorithm against two benchmark algorithms commonly used in practice: scheduled maintenance and condition-based monitoring maintenance. An existing discrete event system specification simulation model was used to test and study the benefits of the proposed algorithm. The computational study demonstrates the importance of avoiding over-simplistic assumptions when making maintenance decisions for wind farms. For instance, most literature assumes maintenance lead times are constant. The computational results show that allowing lead times to be adjusted in an online fashion improves the performance of wind farm operations in terms of the number of turbine failures, availability capacity, and power generation. [ABSTRACT FROM AUTHOR]

This paper aims to identify and evaluate the bottlenecks and wastes in the loading, unloading, and in-transit processes of iron ore railcars using simulation. A field survey was conducted in one of the largest steel factories and a mine, and each process time was extracted using the work-study and time-study methods. The data for more than 34 thousand railcars were collected from the database of the steel company, Iran Rail Transport company, and a private rail transport company. Seven scenarios have been developed according to (a) operations management, (b) equipment procurement, and (c) infrastructure development. It was found that utilization of the automated loading system would improve performance by 20%. This causes a 24% increase in railcars' turnover, which means obtaining US$10,000 additional income per railcar. It was expected, it would improve performance. Conversely, adding a railcar tippler and developing the tracks reduces performance. [ABSTRACT FROM AUTHOR]

The purpose of this research is to define the bottlenecks in a port's rail container transport process and simulate the proposed scenarios in a real port using the lean-railroading approach. After visiting the selected port and holding several meetings with the authorities, all functions relating to containers have been identified. Each section's arrival and departure times were used in this analysis for all containers shipped by rail in 2017, including 19,000 records. The data was accomplished by the processing time of transferring the containers between the railway areas and the port berth. The value stream map (VSM) related to the processes was prepared using the lean approach, the current port situation and proposed scenarios were simulated using the AnyLogic software. The results showed that the two proposed solutions effectively reduced time and costs by up to 20%. The research port also could increase the rail share to 8% without spending on infrastructure. Nevertheless, the rail container terminal warehouse will be a significant bottleneck for the port by growing the load operations. Reducing the turnover of the wagon will also halve the required number of wagons in the network. Therefore, it is recommended that a framework be established to direct freight to rail transport through the Ports Organization by meeting the infrastructure and operational requirements of the port and through the Railroad Company by launching the scheduled freight train and providing the required fleets. [ABSTRACT FROM AUTHOR]

Metamodels are an important tool in simulation analysis as they can provide insight about the behavior of the simulation response. Modeling the response with low-degree polynomial segments allows the identification of different behavior zones and the parameters still have relation with the physical world. The purpose of this paper is to extend the use of segmented polynomial functions for simulation metamodeling, where the segments have at most identical value and slope at the breaks. Our approach is to build segmented polynomials metamodels where the hypothesis of degree and continuity of splines are less exigent, allowing more flexibility of the approximation. When breaks are known, constrained least squares are used for metamodel estimation, taking into account the linear formulation of the problem. If breaks have to be estimated, the unconstrained nonlinear regression theory is used, when it can be applied. Otherwise, the estimation is performed using an iterative algorithm which is applied repeatedly in a cyclic manner for estimating the breaks, and jackknifing yields the confidence intervals. [ABSTRACT FROM AUTHOR]

The effects of adding ethanol on the macroscopic and microscopic spray characteristics of biodiesel were investigated by experimental and numerical methods. The spray tip penetration (STP) was recorded with a high-speed camera system, while the statistical size distribution of atomized droplets and the Sauter mean diameter (SMD) were obtained using a Malvern laser particle size analyzer. The spray calculation models were established with Fluent software, and the calculation results were validated by the experimental results. Then the numerical simulation of the spray characteristics for diesel, biodiesel, and biodiesel–ethanol fuel blends were conducted. The results showed that the farther the distance is from the nozzle exit, the lower the concentration and the velocity of atomized droplets. The concentration and the velocity of atomized droplets gradually decrease along the radial direction, extending from the center to the periphery. The turbulent kinetic energy (TKE) at the front and rear of the spray is relatively lower, while the TKE is higher in the middle of the spray, and turbulent vortices appear on both sides of the axial spray center. In addition, the spray characteristics of the different fuels revealed that compared to diesel, the STP and SMD of biodiesel increase by 33.59% and 33.08% on average, respectively. The maximum concentration of biodiesel is higher than that of diesel, and the concentration distribution is more concentrated. The droplet velocity and TKE of biodiesel respectively decrease by 7.38% and 14.49% in comparison to diesel. When ethanol was added to biodiesel by a volume percentage of 30%, the STP and SMD of biodiesel–ethanol fuel blends reduce by 22.05% and 20.88%, respectively. The concentration distribution, the velocity distribution, and the TKE distribution of the fuel blends are similar to that of diesel. This indicates that adding 30% ethanol to biodiesel can effectively improve its spray quality. [ABSTRACT FROM AUTHOR]

Urban fires are probably the most frequent catastrophic incidents in urban areas, requiring a prompt response due to life endangerment in highly populated zones and the high risk of fire propagation to buildings and parked cars in the vicinity. Robot assistance has been identified as a valuable resource for such urban search and rescue (USAR) scenarios by taking advantage of robots' expendability. However, it is still unclear or not quantified how advantageous such human–robot cooperation can be to the final outcome of firefighting operations and other USAR missions. This article reports research in this context by modeling key features of a firefighting mission in response to an urban fire in a large basement garage. Besides building a behavioral model of human firefighting teams based on interviews with a Portuguese Fire Department, and assessing their performance under different circumstances, this work studies the addition of robotic teams in cooperation with human firefighters to overcome communication issues and improve situation awareness. The results obtained highlight the importance of such human–robot partnership for a more effective response to an urban fire and mitigation of life endangerment of first responders and victims. [ABSTRACT FROM AUTHOR]

This paper deals with a model builder for Hybrid Dynamic Systems (HDS), where technological components including sensors in an iconic format can be automatically dragged from a database and interconnected to produce the overall HDS, following industrial technological schemes. Once the model has been created, its consistency can be checked, and a block diagram simulation can be performed. The theory behind the software is based on Hybrid Bond Graphs (HBG), whose structural and causal properties will be exploited for the automatic generation of the diagnosis algorithms, which will be implemented online. Compared with developed programs for automated modeling of HDS, the presented methodology is valid for continuous and hybrid systems, and the HBG is used not only for block diagram simulation builders but also for the generation of Fault Detection and Isolation algorithms. The theory is illustrated by an industrial application that consists of the pneumo-electrical control of metro rolling stock. [ABSTRACT FROM AUTHOR]

Down-times from the in-service failure of power plant components, such as turbine blades, portend dire impacts and consequences in the form of huge financial losses. The susceptibility of steam turbine blades to failure is now being reduced by a novel technique, laser shock peening (LSP), which induces compressive layers through the surface of the blades. Current simulation studies of LSP employing conventional methods are indeed computationally expensive and time-consuming. Hence, this paper explores an alternative numerical modeling technique to investigate the economic parameters of residual stresses induced in martensitic steel turbine blades when subjected to LSP treatment. An explicit simulation method of analysis, using commercial finite-element software, which employs time-dependent damping, is presented. The study shows that this technique is time-efficient compared with the conventional explicit-implicit methods commonly used for such simulations. It is interesting to note that the results indicate that up to 500 MPa of induced compressive stress of layers reaching 1 mm in depth can be obtained. Encouragingly, these results correlate well with previous experimental studies, lending credence to the method's validity. The technique employed hence offers solutions for timely, non-destructive, methodical maintenance and improvement of the mechanical properties of turbine blades, in the quest to reduce the risks of their in-service failure, as well as lengthening of their service life. [ABSTRACT FROM AUTHOR]

The anisotropy of single crystal copper and crystal orientation have a significant effect on the micro-milling process. At present, there is no systematic and perfect theory to explain the influence of single crystal orientation on the micro-milling process. Therefore, it is urgent to conduct an in-depth study on the micro-milling process of single crystal copper. In this paper, based on the theory of crystal plasticity, considering the anisotropy of single crystal copper, the VUMAT material subroutine of single crystal copper is programmed by the Fortran language, and the crystal plastic constitution is introduced into the finite element simulation. The model of the micro-milling tool and work-piece is established and meshed. Considering the friction among the tool and the work-piece, material removal, etc., the three-dimensional finite element simulation model of single crystal copper micro-milling process is achieved by ABAQUS software. The validity of the simulation model of the micro-milling process of single crystal copper considering the single crystal plastic constitution is verified by experimental micro-milling forces. The research has explored a feasible way to predict the micro-milling force of single crystal copper, and has provided a reference for revealing the micro-milling mechanism of single crystal materials. [ABSTRACT FROM AUTHOR]

Since high performance is essential to the functioning of emergency departments (EDs), they must constantly pursue sensible and empirically testable improvements. In light of recent advances in computer science, an increasing number of simulation-based approaches for studying and implementing ED performance optimizations have become available in the literature. This paper aims to offer a survey of these works, presenting progress made on the topic while indicating possible pitfalls and difficulties in EDs. With that in mind, this review considers research studies reporting simulation-based optimization experiments published between 2007 and 2019, covering 38 studies. This paper provides bibliographic background on issues covered, generates statistics on methods and tools applied, and indicates major trends in the field of simulation-based optimization. This review contributes to the state of the art on ED modeling by offering an updated picture of the present state of the field, as well as promising research gaps. In general, this review argues that future studies should focus on increasing the efficiency of multi-objective optimization problems by decreasing their cost in time and labor. [ABSTRACT FROM AUTHOR]

Numerous studies have confirmed the negative impact of the lack of coordination on supply chain performance. In particular, the lack of coordination leads to the bullwhip effect, which has severe impacts on supply chain stability. This paper evaluates a proposed coordination mechanism that allows a decentralized information sharing in multi-echelon supply chains. The proposed mechanism "Info-Smooth" utilizes the ordering rule of the generalized (R, S) policy in which a replenishment order can be transferred to upstream echelons including two pieces of information (demand forecast and inventory balance). As the generalized (R, S) can allow order smoothing, Info-Smooth combines the power of both information sharing and order smoothing. A simulation modeling methodology is employed to investigate the effectiveness of Info-Smooth in a multi-echelon supply chain. The factorial design results have shown that Info-Smooth is successful in mitigating the bullwhip effect whilst keeping acceptable inventory stability, compared to the traditional supply chain model. [ABSTRACT FROM AUTHOR]

The study of the impact phenomenon on rubber-like materials has been traditionally related to lumped parameter modeling or discrete Finite Element models that require experimentation associated with the material behavior at a level of constitutive modeling, and additional testing to validate their operation in case of engineering applications. This article presents an Artificial Neural Network approach to predict and simulate the low-velocity impact force in a thermoplastic elastomer material. Neural network models were trained and validated with experimental data obtained from impact tests in a modified Charpy apparatus. An experimental setup and a data acquisition procedure were set out to record the impact forces on elastomer specimens. The coefficient of determination R 2, the Root Mean Square Error, and the Maximum Absolute Error measures were implemented as error functions to evaluate the performance of the neural networks regarding experimental data. Results show that the proposed method helps to predict and derive impact force curves within the range of the training data, since errors below 1% regarding experimental values were obtained. The results also demonstrate that the neural networks can simulate impact force curves within the range of the experimental values without the need to involve parameters of material strain-rate sensitivity. In addition, the approach was tested in another material, and the corresponding results show good prediction capabilities since errors below 1% were obtained. Therefore, it is concluded that the presented artificial neural models, and the approach, could be useful to create solution spaces for low-velocity impact responses of thermoplastic elastomers. [ABSTRACT FROM AUTHOR]

The study of waterway through capacity for ports is a challenging problem due to the randomness and complexity of the waterway system, which needs to be analyzed by simulation technology. This paper quantitatively studies the influence of special ships on waterway through capacity and provides corresponding measures to improve waterway through capacity. The complex waterway system in the Port of Meizhou Bay is employed as a case study. Considering the actual conditions in the Port of Meizhou Bay, including the ships, berths, waterways, and natural conditions, a Simio-based simulation model is established. The real operation data from the Port of Meizhou Bay in 2015 are used to verify the correctness and effectiveness of the model. The simulation results show that special ships reduce the waterway through capacity by 4.9% and suggest that circumnavigation strategies that can be adopted after the extension and new construction of waterway systems can improve the waterway through capacity by 5.9%. [ABSTRACT FROM AUTHOR]

Large-scale modeling and simulation (M&S) applications that do not require run-time inter-process communications can exhibit scaling problems when migrated to high-performance computing (HPC) clusters if traditional software parallelization techniques, such as POSIX multi-threading and the message passing interface, are used. A comprehensive approach for scaling M&S applications on HPC clusters has been developed and is called "computation segmentation." The computation segmentation is based on the built-in array job facility of job schedulers. If used correctly for appropriate applications, the array job approach provides significant benefits that are not obtainable using other methods. The parallelization illustrated in this paper becomes quite complex in its own right when applied to extremely large M&S tasks, particularly due to the need for nested loops. At the United States Food and Drug Administration, the approach has provided unsurpassed efficiency, flexibility, and scalability for work that can be performed using embarrassingly parallel algorithms. [ABSTRACT FROM AUTHOR]

Enterprises face the challenge of identifying, following, and controlling the performance of their processes in the whole supply chain (SC) to guarantee an adequate level of service to the client. Since the delivery of orders on time has a direct impact in the client's satisfaction, the managers of the SC face the challenge of choosing the best location, number, and capacity of the facilities to deliver the client's orders on time. In this paper, we address a facility relocation problem. A motor oil manufacturing company currently has a distribution center (DC) with 17,800 storage locations and 20 docks for loading and unloading, operated traditionally, and it wishes to relocate the DC to a smaller one of 10,800 storage locations operated in an automated way. However, they do not have the certainty that the relocated DC will support the distribution logistics operation to deliver the clients' orders on time. In this research, we make a feasibility analysis of the distribution logistics operation when relocating the DC through a simulation model. For that, it was essential to analyze the main storage and distribution processes of the new DC. The main contribution of this work is the development of a simulation model that can be used to analyze the feasibility of relocating a warehouse or DC to a specific location; the model establishes the main key performance indicators that must be evaluated in the simulation. The results of the simulation show that it is feasible to implement the relocation. [ABSTRACT FROM AUTHOR]

Network virtualization concerns an important issue for the future internet. There have been many advances in the state of the art, but there are still challenges to be explored. The virtual network embedding problem is one of these, and is an NP-hard problem. Aiming to improve the embedding task, reconfiguration and expansion appear as possible strategies for the service provider. In this work, we explore the management of resources in order to improve the acceptance of requests deciding when and how reconfiguration and expansion shall apply. An exact approach based on integer linear programming along with a heuristic approach is evaluated. Simulation results indicate both reconfiguration and expansion strategies are able to improve the acceptance, but reconfiguration should be considered first as it can provide significant improvements in managing the fragmentation problem. Expansion comes as a secondary decision if necessary. [ABSTRACT FROM AUTHOR]

This paper deals with the fault detection, isolation (FDI) and reconfiguration of the locomotion of a bipedal-legged robot. Initially, the planar model of the legged robot in the vertical plane is developed using a bond graph (BG) approach. Then, the planar BG model of the legged robot is extended to the three-dimensional legged robot. Two individual motors are used to actuate the prismatic leg of the robot for locomotion. The BG simulation provides results for straight walking based on an oscillating cylinder mechanism and the turning motion of the legged robot are discussed. The prototype model of the legged robot is also developed and experimentation is done for straight and inclined plane applications. Finally, an FDI technique for the three-dimensional model of a legged robot is developed for the generation of fault indicators (i.e., analytical redundancy relations; ARRs) in the presence of system failure. The ARRs are derived from the BG model of the legged robot during the occurrences of the fault. The experimental results are validated with the simulation results for FDI and reconfiguration when the robot manoeuvres in a U-shaped path. The real-time fault diagnosis and reconfiguration for locomotion of the legged robot is possible with this FDI approach. [ABSTRACT FROM AUTHOR]

The ever-increasing worldwide container throughput and the intensified port competition have demanded better terminal operational performance, which is usually measured by the gross crane rate (GCR). In this paper, a multiagent-based simulation model is proposed to investigate how the block length of the storage yard and the tactical-level stacking policy affect the operational performance of non-transshipment container terminals over the long term. Experiments consider seven block lengths, two yard truck fleet sizes and two stacking policies. The results demonstrate that the best block length yielding the highest GCR is dependent on the stacking policy and the yard truck fleet size, and the separate stacking policy is essentially superior to the scattered stacking policy. Specifically, only when 9-yard trucks are deployed per quay crane under the separate stacking policy, can the typical 42-slot block length achieve the highest GCR. Although the experiments are not exhaustive, they do provide the first fundamental insights, with respect to the effects of block lengths and stacking policies, on the long-term performance at a manually controlled non-transshipment container terminal. [ABSTRACT FROM AUTHOR]

Optimization-based motion cueing algorithms based on model predictive control have been recently implemented to reproduce the motion of a car within the limited workspace of a driving simulator. These algorithms require a reference of the future vehicle motion to compute a prediction of the system response. Assumptions regarding the future reference signals must be made in order to develop effective prediction strategies. However, it remains unclear how the prediction of future vehicle dynamics influences the quality of the motion cueing. In this study two prediction strategies are considered. Oracle : the ideal prediction strategy that knows exactly what the future reference is going to be. Constant : a prediction strategy that ignores every future change and keeps the current vehicle's linear accelerations and angular velocities constant. The two prediction strategies are used to reproduce a sequence of maneuvers between 0 and 50 km/h. A comparative analysis is carried out to objectively evaluate the influence of the prediction strategies on motion cueing quality. Dedicated indicators of correlation, delay and absolute error are used to compare the effects of the adopted prediction on simulator motion. Also the motion cueing mechanisms adopted by the different conditions are analyzed, together with the usage of simulator workspace. While the constant strategy provided reasonable cueing quality, the results show that knowledge of the future vehicle trajectory reduces the delay and improves correlation with the reference trajectory, it allows the combined usage of different motion cueing mechanisms and increases the usage of workspace. [ABSTRACT FROM AUTHOR]

The screw drive in-pipe robot is a type of effective intelligent equipment for inspecting pipeline failure. In-pipe robots have a wide range of design parameters due to varied pipe types and application conditions. Experiential or theoretical design means cannot ensure the validity and success rate, which leads to the increase in production cost and cycle. Therefore, in order to solve the rapid design problem of screw drive in-pipe robots, a parameterized simulation system based on ADAMS secondary development technology and a simple design strategy is proposed. Firstly, the motion principles of screw drive in-pipe robots are analyzed to establish a generalized design model. Then, the key parameters of the geometric structure are extracted and associated, which work as the input variables to design the robotic model. Then the general parameterized simulation system is established based on the CMD language of ADAMS simulation software. Finally, a simple design strategy is proposed and a design application case for pipeline diameter of 225 mm is studied. The results show that the parameterized simulation system can build and test varied screw drive in-pipe robot models quickly by only inputting some design parameters. The proposed design system and strategy can help realize the production of a screw drive in-pipe robot rapidly. [ABSTRACT FROM AUTHOR]

This paper exposes and explains the synthesis of a mechanism, the Eclipses Abacus, by which the date of solar and lunar eclipses can be predicted and determined sufficiently accurately. Moreover, a three-dimensional (3D) model of this mechanical device is created by using Solid Works application and the simulation of its operation is accomplished by which the accuracy of its predictions can be demonstrated and inspected. This work is significant mainly for education in the field of 3D modeling, mechanism synthesis, and simulation. Moreover, the Eclipses Abacus is interesting for teaching courses in astronomy since it can demonstrate solar and lunar eclipses simply and clearly. [ABSTRACT FROM AUTHOR]

The diffusion of information is defined as the communication process by which an idea or information spreads within a social system and impacts the behavior of social actors (individuals). The social interaction plays an important role in studying the propagation of information and how it influences people. When an informational event occurs, it can either die out quickly or have significant impact on a population. The interactions could be supported by physical proximity contact, remote collaboration, any type of social meetings, and some forms of verbal or written communication, depending on the situations. Institutions and firms search to understand and predict the impact of information propagation on individuals. Agent-based modeling is a powerful approach for studying such a collective process. However, existing models oversimplify the cultural attributes, the different types of links, and information content, despite the evidence of their central role in the diffusion process.In this context, great benefits could be derived from the exploitation of an individual's personality and cultural values in the diffusion models. In this paper, we describe a new architecture for an agent-based model using the DEVS (Discrete Event System Specification) framework and show how this architecture is flexible and can support the simulation of the dissemination process. In more detail, we define a set of models of individuals characterized by a set of state variables to represent the behavior of an individual and the individual's network within a multi-layer social network. Then, we start by introducing the platform architecture, specifically designed to simulate message propagation in a multi-layer network. Finally, a military scenario of message diffusion during a stabilization phase is used to test our DEVS models on the platform and the relevancy of the simulation results. [ABSTRACT FROM AUTHOR]

The torque motor is an intricate assembly in electro-hydraulic technology and plays a crucial role in converting the electrical signal into controlled mechanical output signal. It involves many precise components, such as the feedback spring, armature and its coil, permanent magnet, feed pipe, flexure shaft, jetpipe, and flexure support. The components are embedded together as a single operating component. Each component contributes to the effective dynamics of the system. The present paper proposes a novel approach to investigate the effect of critical parameters on the working design dynamics of the torque motor employed in the jetpipe electro-hydraulic servovalve. Based on the principles of mechatronics, a mathematical model is developed. The model-based design approach is employed to investigate the dynamics of the system. The required simulation parameters of the critical and precision components were obtained from solid and finite element (FE) models. The solid and FE models of the critical and precision components were first analyzed with suitable boundary and loading conditions to establish the stiffness. To validate the obtained FE results, experiments were carried out with a specially designed and fabricated test set-up. Based on the basic principle of electromagnetics, a nonlinear FE model of torque motor is analyzed for magnetic field distribution, the torque developed, and armature and jetpipe deflection for varied input current. From the results obtained, good agreement was observed between FE, simulated, and experimental values. The present novel approach enables one to improve the working design dynamics of the torque motor. [ABSTRACT FROM AUTHOR]

In recent years, the simulation of personal car driver behavior has attracted increasing attention in recent research works. Such works are based on models and systems derived from social and psychological studies. The complexity of the simulation of such systems is due to the need for modeling driver behavior and the integration of psychological and physiological factors that can affect driver performance. Although there is only a limited number of models that have been proposed to simulate driver behavior, most of them suffer from limitations pertaining to the integration of some factors, an inadequacy that will be discussed in this paper. This investigation work focuses on the development of a new model for driver behavior simulation based on recent physiological and psychological theories. The model aims to reproduce the driver behavior with respect to some psychological factors. An experimental framework is also presented to build the simulation model. This article concludes by describing some examples of use or application of the suggested model. [ABSTRACT FROM AUTHOR]

Continuous improvement in quality is the most important mantra for success in today’s competitive market. Previous studies indicated that although quality is the most important factor in gaining competitive superiority, increasing the level of quality alone cannot meet customers’ needs. One complementary approach to improving the level of quality to meet customers’ expectations is to lower the production costs and price of finished products. The quality circle as a path to achieve greater customer satisfaction is formed to identify the cost of quality and thus reduce this cost, which is a significant cost portion of the entire life cycle of products. This paper presents a case study in an electromotor manufacturing company. First, we build a mathematical model to allocate inspection stations to manufacturing processes and propose a heuristic approach to optimize it. Next, we use Enterprise Dynamic software for simulation. Finally, we compare these methods. [ABSTRACT FROM AUTHOR]

Over the past few years the applications for Wireless Sensor Networks (WSNs) have grown at an ever-increasing rate. However, the evolution of those networks has been reduced by the energy scarcity. It retards the development of the WSN performances required while exploring new applications and improving the WSN potential. Besides, in order to design energy-efficient solutions, it is important to take into account the power dissipation due to noncompliance with time constraints. As a result, we will provide a model of power management that will be simulated and validated by the STORM Simulator (Simulation TOol for Real time Multiprocessor scheduling). However, unlike traditional WSN energy management systems, our power manager reduces the energy consumption through a dual approach: a global and dynamic approach using the analysis of the behavior of the network and a local one applied at the node level. We have relied on energy optimization techniques to yield extensive lifetime for every node battery and mainly both Dynamic Power Management and Dynamic Voltage and Frequency Scaling, which are appropriate for the WSN. This model will be based on a global Earliest Deadline First scheduling policy. Besides, we aim to extend the STORM simulation tool to include those power management techniques. [ABSTRACT FROM AUTHOR]

An inventory transshipment model comprising manufacturers, distributors, and retailers based on multi-echelon supply chains is established using the system dynamics method. Thereafter, the single-, double-, three-, and four-chain inventory transshipment models at different levels are comparatively analyzed through simulation. Studies indicated that from the single- to four-chain inventory transshipment models, the average stock level minimally changed and declined. Meanwhile, the stability of the supply chain inventory improved continuously. The average customer requirement satisfaction rate of the inventory transshipment system consistently increases as the number of chains added to the transshipment system increases. However, the growth range of the average customer satisfaction rate continuously decreases with the increasing transshipment costs. [ABSTRACT FROM AUTHOR]