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In the context of increasing complexity in manufacturing and logistic systems, the combination of optimization and simulation can be considered a versatile tool for supporting managerial decision-making. An informed storage location assignment policy is key for improving warehouse operations, which play a vital role in the efficiency of supply chains. Traditional approaches in the literature to solve the storage location assignment problem present some limitations, such as excluding the stochastic variability of processes or the interaction among different warehouse activities. This work addresses those limitations by proposing a discrete-event simheuristic framework that ensures robust solutions in the face of real-life warehouse conditions. The approach followed embraces the complexity of the problem by integrating the order sequence and picking route in the solution construction and uses commercial simulation software to reduce the impact of stochastic events on the quality of the solution. The implementation of this type of novel methodology within a warehouse management system can enhance warehouse efficiency without requiring an increase in automation level. The method developed is tested under a number of computational experiments that show its convenience and point toward future lines of research.

The modelling of photovoltaic (PV) solar panels requires electrical parameters which are dependent on the manufacturing materials and their physical properties. Manufacturers typically do not disclose detailed physical properties of the PV module, except for some electrical quantities such as open circuit voltage (Voc), short-circuit current (Isc), maximum power point voltage (Vm), maximum power point current (Im) and maximum power (PM). However, to model the PV panels comprehensively, it is necessary to determine other physical parameters, e.g., series resistance of PV cell (Rs), shunt resistance of PV cell (RSh) and diode ideality factor (n). This paper presents a generalised mathematical model of a PV panel utilising only the quantities provided in manufacturer's datasheet. The proposed modelling technique determines all the PV panel parameters without any explicit repetitive iteration. Although the developed model is general and can be implemented on any software platform, its implementation is demonstrated on a commercial electromagnetic transients simulation software electro magnetic transient including direct current power systems computer aided design. The electrical parameters obtained from the proposed PV panel model are validated for six different commercially available PV panels from their datasheet values and also from measurements provided by National Institute of Standards and Technology for solar irradiation and temperature at nonstandard test conditions.

The principles of open collaboration for innovation (and production), once distinctive to open source software, are now found in many other ventures. Some of these ventures are Internet based: for example, Wikipedia and online communities. Others are off-line: they are found in medicine, science, and everyday life. Such ventures have been affecting traditional firms and may represent a new organizational form. Despite the impact of such ventures, their operating principles and performance are not well understood. Here we define open collaboration (OC), the underlying set of principles, and propose that it is a robust engine for innovation and production. First, we review multiple OC ventures and identify four defining principles. In all instances, participants create goods and services of economic value, they exchange and reuse each other’s work, they labor purposefully with just loose coordination, and they permit anyone to contribute and consume. These principles distinguish OC from other organizational forms, such as firms or cooperatives. Next, we turn to performance. To understand the performance of OC, we develop a computational model, combining innovation theory with recent evidence on human cooperation. We identify and investigate three elements that affect performance: the cooperativeness of participants, the diversity of their needs, and the degree to which the goods are rival (subtractable). Through computational experiments, we find that OC performs well even in seemingly harsh environments: when cooperators are a minority, free riders are present, diversity is lacking, or goods are rival. We conclude that OC is viable and likely to expand into new domains. The findings also inform the discussion on new organizational forms, collaborative and communal.

Recent developments in dynamic energy simulation tools enable the definition of energy performance in buildings at the design stage. However, there are deviations among building energy simulation (BES) tools due to the algorithms, calculation errors, implementation errors, non-identical inputs, and different weather data processing. This study aimed to analyze several building energy simulation tools modeling the same characteristic office cell and comparing the heating and cooling loads on a yearly, monthly, and hourly basis for the climates of Boston, USA, and Madrid, Spain. First, a general classification of tools was provided, from basic online tools with limited modeling capabilities and inputs to more advanced simulation engines. General-purpose engines, such as TRNSYS and IDA ICE, allow users to develop new mathematical models for disruptive materials. Special-purpose tools, such as EnergyPlus, work with predefined standard simulation problems and permit a high calculation speed. The process of reaching a good agreement between all tools required several iterations. After analyzing the differences between the outputs from different software tools, a cross-validation methodology was applied to assess the heating and cooling demand among tools. In this regard, a statistical analysis was used to evaluate the reliability of the simulations, and the deviation thresholds indicated by ASHRAE Guideline 14-2014 were used as a basis to identify results that suggested an acceptable level of disagreement among the outcomes of all models. This study highlighted that comparing only the yearly heating and cooling demand was not enough to find the deviations between the tools. In the annual analysis, the mean percentage error values showed a good agreement among the programs, with deviations ranging from 0.1% to 5.3% among the results from different software and the average values. The monthly load deviations calculated by the studied tools ranged between 12% and 20% in Madrid and 10% and 14% in Boston, which were still considered satisfactory. However, the hourly energy demand analysis showed normalized root mean square error values from 35% to 50%, which were far from acceptable standards.

Urban large-scale complexes, such as shopping malls, pose significant challenges for fire safety management due to their intricate spatial layouts, high population density, and diverse occupancy characteristics. Efficient fire evacuation strategies are critical for minimizing casualties and economic losses; however, existing approaches often overlook the dynamic interplay between fire propagation and human behavior, resulting in suboptimal safety assessments. This study proposes an integrated simulation framework to optimize evacuation strategies by coupling fire dynamics with pedestrian flow modeling, aiming to enhance both evacuation efficiency and personnel safety. The methodology comprises three key steps: (1) Fire scenario simulation: A Building Information Modeling (BIM)-based digital platform is constructed to simulate fire propagation. Critical fire parameters (e.g., heat release rate, combustion model) are calibrated to quantify temporal variations in smoke temperature, CO concentration, and visibility across different zones. (2) Evacuation dynamics modeling: A pedestrian evacuation model is developed by integrating demographic factors (age structure, movement speed, population density) and fire-induced regional risks, enabling realistic simulation of crowd movement under fire conditions. (3) Safety performance evaluation and strategy optimization: Safety margins at staircases are assessed by comparing Required Safe Egress Time (RSET) and Available Safe Egress Time (ASET), followed by a safety grading system to identify high-risk bottlenecks. Evacuation strategies are then optimized to mitigate these risks. A case study was conducted on a shopping mall in Chengdu to validate the framework. Simulation results indicate an initial evacuation time of 260.4 seconds. Safety performance analysis revealed critical risks at staircases A and C (1st floor) and D (2nd floor) due to insufficient safety margins. After strategy optimization, the total evacuation time was reduced to 245.5 seconds, with safety margins at the three high-risk staircases increased by 130.8 s, 115.2 s, and 72 s, respectively, fully meeting safety requirements. The overall evacuation efficiency was significantly improved. This study demonstrates the effectiveness of the proposed framework in quantifying fire risks and optimizing evacuation strategies for large-scale complexes. The integrated simulation approach provides a scientific basis for evidence-based safety management and evacuation planning, offering valuable insights for urban fire safety engineering and emergency response optimization.

For electrically driven commercial vehicles equipped with three-speed automatic mechanical transmission (AMT), the transmission control unit (TCU) without vehicle mass and road gradient estimation function will lead to frequent shifting and insufficient power during vehicle full-load or grade climbing. Therefore, it is necessary to estimate the mass and road gradient for the electrically driven commercial vehicles equipped with the three-speed AMT, and to adjust the shift rule according to the estimation results. Given the above problems, this paper focuses on the control and development of the electrically driven three-speed AMT and takes the shift controller with the vehicle mass and road gradient estimation as the research goal. The mathematical model and simulation model of vehicle dynamics are established to verify the shift function of TCU. The least squares method and calibration techniques are applied to estimate the vehicle mass and road gradient. According to the estimation results, the existing shift strategy is optimized, and the software-in-the-loop simulation of the transmission controller is carried out to verify the function of the control algorithm software. The hardware-in-the-loop test model is established to verify the shift strategy’s optimization effect, which shortens the controller’s forward development cycle. According to the estimation results of mass and gradient, the error result of the proposed method is controlled within 4.5% for mass and 8.6% for gradient. The experiment verifies that the optimized shift strategy can effectively improve the dynamic performance of the vehicle. The HIL experimental results show that the vehicle can maintain low gear while climbing the hill, and the vehicle speed does not decrease significantly.

As the power grid continues to evolve with advanced wide‐area monitoring, protection, and control (WAMPAC) algorithms, there is an increasing need for realistic testbed environments with industry‐grade software and hardware‐in‐the‐loop (HIL) to perform verification and validation studies. Such testbed environments serve as ideal platforms to perform WAMPAC prototyping, operator training, and also to study the impacts of different types of cyberattack scenarios on the operation of the grid. In this study, the authors introduce pacific northwest national laboratory(PNNL) cyber‐physical systems testbed (PRIME): the testbed that integrates real‐time transmission system simulator with commercial industry‐grade energy management system software and remote HIL (RHIL). PRIME is an end‐to‐end, modular testbed that allows high‐fidelity RHIL experimentation of a power system. We present two detailed case studies (fault location and clearing in the transmission system and operator training) to show the capabilities of their PRIME testbed. Finally, we briefly discuss some of the potential limitations of their testbed in terms of scalability and flexibility to set up larger test systems and identify directions for future work to address these limitations.

Parentage identification technology is of great significance in the selective breeding of aquatic animals. Procambarus clarkii, at present, is an important freshwater economic species in China, and its genetic breeding is quite urgent for its aquaculture. In this study, 18 novel polymorphic microsatellite markers were developed for P. clarkii from 144 potential primers. The efficiency of 16 developed tetrameric markers with higher PIC values and two published tetrameric markers for parentage identification was evaluated in 15 maternal half-sib families. Ten highly polymorphic markers (mean HE = 0.720 and PIC = 0.669) were identified as being suitable for inclusion in the parentage marker suite. Simulation analysis indicated that the cumulative exclusion probabilities for the ten loci were 97.55% and 99.86%, respectively, when no parental information or only one parental information was available. In actual parentage assignments, 94.67% of offspring were correctly assigned to their mothers, which confirmed the application value of this marker suite in the parentage identification of P. clarkii.

As one of the primary users of the electric grid, buildings and building equipment, including heating ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants' comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment of the building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants' thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the Zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVA C system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVA C systems in terms of demand flexibility potential under the impact of occupant behaviors.

The existing non-residential building stock can generally be considered energy-inefficient. The ECUK 2017 report states that the final energy consumption for commercial buildings remained static. The 2010 recast Energy Performance Building Directive (EPBD) has set out a requirement for commercial and residential buildings to be nearly Zero Energy Buildings (nZEBs) by 2020. Despite this, within the UK, a definition does not exist at the national level for commercial nZEBs (new or existing). This paper utilises the EU zebra2020 data tool to set a standard based on the existing UK nZEB commercial building stock. The aim of this paper is to investigate and assess the potential of various energy-efficient measures (EEMs) and their contribution to reducing energy consumption, primary energy consumption (PEC), and CO2 emissions whilst taking into consideration the energy and cost savings of those measures. The analysis is carried out using Thermal Analysis Simulation software (Tas, Edsl). The model validation obtained a performance gap of less than 5%. The results show that it is possible to achieve the nZEB standard for older UK hotel buildings if several measures are implemented and the initial selection of EEMs is carefully investigated. Based on the results, reaching the nZEB target should first take into consideration improving the building fabric and/or building envelope elements to lower the energy demand. Once the energy demand of the building is lowered, the incorporation of a renewable/microgeneration system is essential to meeting the nZEB target.