AnyLogic Simulation Software Research Articles

Research on passenger flow control at metro transfer stations based on real-time flow calculation of streamlines

PurposeThe volume of passenger traffic at metro transfer stations serves as a pivotal metric for the orchestration of crowd flow management. Given the intricacies of crowd dynamics within these stations and the recurrent instances of substantial passenger influxes, a methodology predicated on stochastic processes and the principle of user equilibrium is introduced to facilitate real-time traffic flow estimation within transfer station streamlines.Design/methodology/approachThe synthesis of stochastic process theory with streamline analysis engenders a probabilistic model of intra-station pedestrian traffic dynamics. Leveraging real-time passenger flow data procured from monitoring systems within the transfer station, a gradient descent optimization technique is employed to minimize the cost function, thereby deducing the dynamic distribution of categorized passenger flows. Subsequently, adhering to the tenets of user equilibrium, the Frank–Wolfe algorithm is implemented to allocate the intra-station categorized passenger flows across various streamlines, ascertaining the traffic volume for each.FindingsUtilizing the Xiaozhai Station of the Xi’an Metro as a case study, the Anylogic simulation software is engaged to emulate the intra-station crowd dynamics, thereby substantiating the efficacy of the proposed passenger flow estimation model. The derived solutions are instrumental in formulating a crowd control strategy for Xiaozhai Station during the peak interval from 17:30 to 18:00 on a designated day, yielding crowd management interventions that offer insights for the orchestration of passenger flow and operational governance within metro stations.Originality/valueThe construction of an estimation methodology for the real-time streamline traffic flow augments the model’s dataset, supplanting estimated values derived from surveys or historical datasets with real-time computed traffic data, thereby enhancing the precision and immediacy of crowd flow management within metro stations.

Simulation study on the road system of port collection and distribution in the harbor area

Abstract This paper comprehensively considers the impact of both internal and external traffic environments on the collection and distribution road system in the harbor area. Utilizing Anylogic simulation software, we construct a test environment using Yulong Port’s collection and distribution scenario as a case study. The analysis examines the influence of changes in road construction and the growth of external traffic on the operational efficiency of the port’s road collection and distribution system. The results indicate that with the addition of new roads, the efficiency of car and freight vehicle operations improves, leading to a respective decrease in the average travel time within the system by 3.6% and 4.4%. However, under conditions where social traffic flow increases to 1.2 times, the average travel time of the traffic flow within the system significantly rises, and congestion occurs in the external traffic around the port. The findings of this research provide valuable decision-making insights for the construction of collection and distribution channels in port areas and the approval of governmental projects.

Model predictive control for thermal comfort and energy optimization of an air handling unit system in airport terminals using occupant feedback

The dynamic fluctuations in occupant flow within airport terminals contribute to delays in the system's response under traditional feedback control. This imbalance between supply and demand can result in discomfort and unnecessary energy consumption. While Occupant-Based Model Predictive Control (OBMPC) has gained research interest as it leverages occupant forecasts to enhance control performance, there is limited knowledge regarding its application in the air-conditioning systems of airport terminals, mainly due to the intricate dynamics of occupant flow in terminal buildings. In this study, we proposed a model predictive control strategy based on dynamic occupant flow with the goal of fast responding to occupant flow and reducing energy consumption. We integrated mathematical formulas and the Anylogic simulation software to predict occupant variations in the baggage claim hall. The model predictive control strategy of the air conditioning system was proposed with the predicted occupant flow. The proposed strategy was evaluated throughout the whole cooling season in the validated simulation model of the air handling unit system of the baggage claim hall at an airport terminal. The results demonstrated that the proposed strategy could effectively respond to variations in occupancy and achieve 10% energy savings throughout the entire cooling season, compared to conventional feedback control. Our work presents a viable solution to address system regulation lag and reduce energy consumption without jeopardizing thermal comfort.

Based on Hierarchical Reinforcement Learning for Large-scale Pedestrian Trajectory Prediction

This paper describes the construction of an airport terminal simulation model using AnyLogic simulation software. It considers the advantages of hierarchical reinforcement learning and divides the complete process of pedestrian trajectories at the airport into layers. Pedestrians are treated as intelligent agents for hierarchical reinforcement learning. A large-scale pedestrian trajectory planning algorithm based on hierarchical reinforcement learning is designed to match the hotspots in the airport region simulated by pedestrian trajectories with congested areas in the terminal scene. A comparison is made with traditional multi-agent Q-learning algorithms and single-table hierarchical reinforcement learning. The results show that our algorithm can accurately identify the pedestrian flow hotspots in the actual terminal, with improved matching accuracy compared to traditional multi-agent Q-learning algorithms and single-table hierarchical reinforcement learning. The algorithm also exhibits faster convergence speed.

Research on transportation management model of COVID-19 medical waste: a case study in Beijing, China.

During the COVID-19 pandemic, disposable masks, protective clothing, gloves, and nasopharyngeal swabs collected by nucleic acid testing formed a large amount of medical waste. Medical waste has strict temporary storage time requirements in hospitals, which need to be transported to medical waste disposal plants within the specified time. However, as most of disposal plants are far away from downtown, they also need to be responsible for the transportation and disposal of medical waste in many hospitals, and put forward higher requirement for transportation routes. Rapid and safe disposal of all types of medical waste generated by COVID-19 is crucial to the prevention and control of the epidemic. This paper designs the transportation route optimization model using Anylogic simulation software based on the regional distribution of 118 tertiary hospitals and 2 large medical waste disposal plants in Beijing, China. At the same time, transportation routes of 118 tertiary hospitals in the morning peak, evening peak, all-day, and ordinary periods were simulated based on the Beijing traffic index in 2017. On this basis, through the analysis of the simulation data, the selection of medical waste transport routes for 118 tertiary hospitals in the morning peak, evening peak, all day, and ordinary periods is further clarified, so as to ensure that medical waste can be transported to the medical waste disposal plant in the shortest time. The shortest path and fastest speed transport mode, medical waste transport data set, and the selection of transport mode of 118 tertiary hospitals formed by this research provide certain reference experience for the rapid and safe transport of medical waste during the epidemic period, and also provides corresponding data support for medical waste transportation management in the post-epidemic era and medical waste transportation decision-making when facing major public health problems.

Strategic Approach for Electric Vehicle Charging Infrastructure for Efficient Mobility along Highways: A Real Case Study in Spain

The Electric Vehicle (EV) market has been growing exponentially in recent years, which is why the distribution network of public charging stations will be subject to expansion and upgrading. In order to improve the public charging infrastructure, this paper aims to develop a model capable of analyzing the current situation of a stretch of highway, identifying the congestion points, created by the formation of queues at the charging points. A specific section of a highway in Spain was selected as a case study to evaluate the performance of the model, allowing for rigorous testing and thorough analysis of its performance in a real-world scenario. The first step is to define and evaluate the effects of factors affecting EV consumption, such as the slope of the road, weather conditions, and driving style. Subsequently, a simulation model is developed using the agent-based simulation software AnyLogic, which simulates the journey of a fleet of electric vehicles, taking into account the battery charging and discharging process. Based on the obtained results, the charging infrastructure is improved to minimize the total travel time of an electric vehicle on a long-distance trip.

Simulation-optimization of inventory control of multiple products on a single machine with sequence-dependent setup times

This work studies the problem of lot sizing and scheduling of multiple products on a single machine, with stochastic demand and sequence-dependent setup times, called Stochastic Economic Lot Scheduling Problem (SELSP). The present work differs from others in the literature by considering simple inventory control policies and using the simulation–optimization approach to calibrate their parameters. We consider two inventory control policies: (i) fixed cycling (First in Sequence - FIS) and (ii) dynamic scheduling based on inventory levels (Lowest Days of Supply - LDS), combined with an “order-up-to” lot sizing. The problem is solved using AnyLogic simulation software and the OptQuest search engine to minimize total inventory cost (ordering, holding and shortage costs). The experimental design included the following factors: number of items, coefficient of variation of demand, system workload, and degree of setup increment, allowing the comparison of the two inventory control policies in different scenarios. Experiments show that LDS outperforms FIS in all scenarios, achieving up to 4.6% cost savings for cases of more products, higher workload, and greater demand variance. The developed models proved to solve the problem, effectively generating reasonable solutions. Furthermore, as they are user-friendly, we believe they can be adapted, without great difficulties, to real-life scenarios of the process industry.

Optimization of the Decision-Making System for Advertising Strategies of Small Enterprises—Focusing on Company A

In the post-COVID-19 era, the founding rates of micro enterprises and startups will increase due to the low youth employment rates and increased retirement of baby boomers. Therefore, the portion of small enterprises among all enterprises is expected to grow. The rapid change in consumption patterns due to the COVID-19 pandemic has accelerated the entry of small enterprises into the online advertising market. However, advertising costs spent in running the businesses are taking up a large portion of their sales budgets due to intense competition and various advertising platforms. This study examines the decision-making system to optimize advertising expenditures considering the difference in advertising costs depending on various media types and keywords based on limited advertising budgets for stable management of small enterprises. To this end, this study modeled the advertising system of small enterprise A (Company A) with system dynamics and used the Java-based simulation software AnyLogic. Through simulation modeling, we conducted optimization analysis of two scenarios, maximum buyers and minimum advertising costs, in the post-COVID-19 era. Based on the results, this study forecast the conditions for optimization of decision-making in each advertising platform.

Analysis of Toilet Scale of Freeway Service Areas Based on Crowd Gatherings

On fully enclosed freeways, the service area is the only key node for certain crowd activities such as resting, toileting, and dining. In these activities, using the toilet is the most important purpose for people to enter the service area. A reasonable toilet location and size in the service area is the basis for its efficient operation. This study conducts field surveys on several service areas, including Meicun, Tai’an, and Qianxian, through video recording and crowd tracking questionnaires. We summarize and analyze the characteristics of population flow in Chinese freeway service areas. The key data obtained through field surveys are reported, such as the priority and length of stay of customers in the facilities during peak hours at weekends, to analyze the characteristics of pedestrian traffic in the service areas. Finally, based on a large number of pedestrian simulation methods used in studies on the layout of subways and airport facilities, the AnyLogic simulation software is applied to establish a crowd movement model for these areas. The study not only obtained the pedestrian characteristics of the service area but also showed that only changing the location of toilets has no effect on the efficiency of toilet use without changing the level of service, whereas changing the number of toilets has a significant impact on the efficiency. Most importantly, this paper establishes a relationship model between the toilet size of the service area and the service area pedestrians flow; when the toilet seats 266/158/148 people, the maximum flow of people in the service area is about 3,115/2200/2000 p/h, which can provide help for the scale design of the service area.

Simulation of migration and demographic processes using FLAME GPU

This article presents an approach to modeling migration and demographic processes using a framework designed for large-scale agent-based modeling – FLAME GPU. This approach is based on the previously developed simulation model of interaction between two communities: migrants and natives that is implemented in the AnyLogic simulation software. The model has had a low dimensionality of the discrete space representing the operating environment of the agent populations and a deterministic decision-making system of each agent. At the same time, the presence of multiple interactions between agents and transitions between their states determines a high computational complexity of such a model. The use of FLAME GPU makes it possible to conduct extensive simulation experiments with the model, mainly due to the parallelization of computational processes at the level of each agent, as well as the implementation of the mechanism of multiple computations using Monte Carlo techniques. The developed framework is used to study the impact of the most important parameters of the model (e.g., rate of migration, governmental expenditures on integration, frequency of creation of new workplaces, etc.) on the key outputs of the modeled socio-economic system (in particular, population size, share of migrants, number of assimilated migrants, GDP growth rate, etc.). The proposed approach can be used to develop decision-making systems for planning the hiring of new employees based on the forecast dynamics of migration and demographic processes.

Simulation and optimization of mining-separating-backfilling integrated coal mine production logistics system

As a new collaborative mining technology, the integration of mining-separating-backfilling (IMSB) enables the coal industry to realize safe, efficient, and green mining of coal resources at the expense of making the underground coal mine production more complex. In coal mining, the primary problem is to eliminate the production logistics system bottleneck to increase coal output. The key to synergetic mining is to realize that the mining capacity of the fully mechanized face should match the underground raw coal separating and gangue backfilling capacities. Considering a coal mine with IMSB in Henan Province, AnyLogic simulation software was used to simulate and optimize the production logistics system based on a generalized stochastic Petri net (GSPN). The main simulation results show that: (1) the raw coal separating capacity of this deep mine matches its mining capacity, and the gangue backfilling capacity can almost meet the demand for backfilling after raw coal mining; (2) belt conveyors 1 and 6 are the transportation bottlenecks of increasing production in the mine, and the lifting capacity of the main shaft is insufficient; (3) after optimization, the clean coal output of this coal mine increased by 3087 t monthly. This research promotes synergetic mining in the coal industry and can serve as a reference for the optimization of similar coal mine production logistics systems.

Prediction of the spatiotemporal passenger distribution of a large airport terminal and its impact on energy simulation

• The passenger distribution in the airport terminals is related to their functions and the peak flow should not occur all over the terminal simultaneously. • A spatiotemporal passenger distribution model was proposed for energy simulation of large airport terminals. • The proposed model can reduce the HVAC energy consumption by over 11% compared to traditional passenger distribution model. • Optimize the design and operation of HVAC systems in airport terminals. As the essential infrastructure and one of the transportation centers, most cities now have more than one airport. Mass constructions of sustainable airports play an important role for sustainable cities and societies. The energy consumption of airport terminals poses a major challenge to the carbon neutrality policy due to the high energy demand and round-the-clock operation of these terminals. One major direction for sustainable future is to make buildings smart in terms of energy use and management. One of the solutions is to refine the energy supply according to needs, which calls for knowledge of the locations and dwell duration of passengers in the airport. With this background, this study established a spatiotemporal passenger distribution model to describe the transient passenger distribution in 14 characteristic zones of an airport terminal. After validation against airport operation data and results from passenger flow simulation software AnyLogic, the passenger flows calculated by the spatiotemporal model were inputted into the energy simulation software IES as well as the traditional calculation method to compare the energy consumptions. Results showed that the difference of the cooling and heating loads from the two models was 7%–148.9% in different zones. And the designed annual cooling and heating consumption of the terminal given by the proposed passenger distribution model was 11.3% lower. The proposed model could potentially optimize the HVAC system of future airport design.

Optimizing planning and design of COVID-19 drive-through mass vaccination clinics by simulation.

Drive-through clinics have previously been utilized in vaccination efforts and are now being more widely adopted for COVID-19 vaccination in different parts of the world by offering many advantages including utilizing existing infrastructure, large daily throughput and enforcing social distancing by default. Successful, effective, and efficient drive-through facilities require a suitable site and keen focus on layout and process design. To demonstrate the role that high fidelity computer simulation can play in planning and design of drive-through mass vaccination clinics, we used multiple integrated discrete event simulation (DES) and agent-based modelling methods. This method using AnyLogic simulation software to aid in planning, design, and implementation of one of the largest and most successful early COVID-19 mass vaccination clinics operated by UCHealth in Denver, Colorado. Simulations proved to be helpful in aiding the optimization of UCHealth drive through mass vaccination clinic design and operations by exposing potential bottlenecks, overflows, and queueing, and clarifying the necessary number of supporting staff. Simulation results informed the target number of vaccinations and necessary processing times for different drive through station set ups and clinic formats. We found that modern simulation tools with advanced visual and analytical capabilities to be very useful for effective planning, design, and operations management of mass vaccination facilities.

Study on Optimization of Passenger Flow at a Metro Station Based on AnyLogic—Case Study of Youfangqiao Station of Nanjing Metro Line 2

In this study, simulation software AnyLogic was used to establish a station simulation model for a metro line. First, a basic model of the environment of the metro station was drawn, and accordingly, reasonable assumptions and simplifications were proposed. Then, a diagram of the passenger walking path was created and the simulation variables and functions for passenger flow management were designed. Considering Youfangqiao Station of Nanjing Metro Line 2 in China as an example, the real passenger flow data of this station were statistically analyzed. To simulate the station passenger flow management, input parameters such as the passenger space diameter, passenger flow generation rate, delay rate of automatic fare collection equipment and security check machine, and the number of gates were considered. Passenger flow management was optimized for the morning and evening peak periods, and reasonable suggestions were proposed based on the optimization results, providing a theoretical basis for the construction planning and pre-evaluation of station operation capacities of urban rail transit systems.

Designing the Re-layout of the Production Floor Using Integrated Systematic Layout Planning (SLP) and Simulation Methods

In the manufacturing company, an efficient production floor affects the productivity of the company. Thus, a well design production floor layout assists the company to achieve its objectives. In this regard, this study aims to design a new alternative production floor layout for the XYZ manufacturing company. The company facing the facility layout problem (FLP) where their workstation on the production floor was not located based on the activity-relationship. Thus, the company struggles to reduce the distance travel of their workers from one station to another by re-layout their production floor. The Systematic Layout Planning (SLP) method was used to determine the best new alternative layout for the company. Subsequently, the AnyLogic simulation software was utilized to test the effectiveness of the layout by using the number of steps as the parameter. As a result, it is found that the total number of steps of workers in the production floor can be reduced from 16,554 steps (in existing layout) to 15,956 steps (in new alternative layout).

Digital twin technology as a modern approach to quality management

The concept of a digital twin and its types are discussed in this paper. The essence of the development of digital twins in the SysML language, the use of AnyLogic (simulation software) and the use of MES (Manufacturing Execution Systems) software for communication with production systems are described. The functions of neural technologies that can be integrated into a modern QMS (quality management system) are considered. The use of digital twins in the quality management system allows monitoring inconsistencies in the technological component of production and quickly eliminating them, and also offers solutions to the identified problem. Companies are now finding a competitive advantage in digital technologies, especially digital twins, whose economic benefits are undeniable. The quality management system of enterprises with high-tech industries is more and more moving into the analysis of big data and in interaction with the digital environment. Thus, it is so important to pay attention to technologies directly related to the analysis of the quality of the product and the processes of its creation. As a result of the study, a clear connection between advanced information technologies and the development of the quality management system of enterprises was revealed.

Simulation of the steelmaking process using collision avoiding cranes moving models

The paper discusses a steelmaking process and its optimisation using simulation. The optimisation goal is to reduce the total distance travelled by cranes in all spans as well as to reduce the steel waiting time before casting on a continuous casting machine. Models of the process under study have been developed in the AnyLogic simulation software and the modelling subsystem of the metallurgical enterprise automated system. A comparative analysis of the simulation results has been conducted. All results are feasible in terms of meeting the technological constraint on the steel downtime in front of the continuous casting machine for no more than 15 minutes. When comparing two collision avoiding cranes moving models, a choice has been made in favour of a model that provides cranes movement to the nearest steel processing aggregate. Using this model allows to reduce the total path travelled by cranes by 6%. The results of using different modelling systems are consistent.

Разработка мультиагентной модели интегрированной энергоснабжающей системы в программной среде AnyLogic

The purpose of this work is to develop a multi-agent model of an integrated energy supply system in the AnyLogic simulation software and to conduct a research on the operation and interaction of objects in this system using the obtained model. A multi-agent approach is proposed to study integrated energy supply systems as it enables to carry out a detailed research of interaction and coordination mechanisms of various elements and subsystems (energy sources, transport subsystems, active consumers, etc.) of the object under investigation. The model is implemented in AnyLogic simulation software that supports designing, development and documenting of the created models, carrying out computer experiments, parameter optimization according to some criterion that enables visualization of the mechanisms of interaction and connection between the agents. A multi-agent model of the integrated power supply system has been developed. Agent state diagrams that take into account the operation of its elements have been created and the principles of their interaction and coordination have been analyzed. The structure of the developed model of an integrated power supply system contains four types of agents and connections between them. An experiment has been conducted on the basis of the developed model, in which the optimal solution was found for energy supply of consumers. The results of the conducted computational experiment show that the specified conditions and restrictions are met; messages and parameters are correctly transmitted between the agents and the agents perform the assigned functions in the system. The results obtained will allow to model real power supply systems of any complexity in order to study the properties and improve the efficiency of these systems. The developed model enables the possibility to model complex processes in an integrated energy supply system which relate to production, transport, distribution and consumption of energy.

Consumer acceptance of Blockchain-Based Traceability Systems in Food Supply Chains

The demand for the history of food is increasing. The used food traceability systems also provide this kind of information, but the appearance of blockchain could open new opportunities in this field. To set up a new food tracking system cost a lot and presumably the companies devolution it's costs on the consumers. To see their openness we examined the previous studies. According to these papers, the interest of the costumers is mixed. There are studies where a huge percentage of the surveyed want more information but the extra fee what the new system would cause should be not so much higher than the original price. Based on the available information the forecast of a new food tracking system was made with Anylogic simulation software that showed a lack of interest from the consumers’ side. The reason could be the low rate of the advertisements’ effectiveness and the high premium price of extra information as well.

Agent-based simulation for a regional guarantee circle crisis

ABSTRACT In view of the phenomenon of the guarantee network formed by the mutual guarantee of Chinese enterprises. First, the mechanism of crisis formation in regional guarantee circles is analysed through the network contagion effect, then the enterprise and guarantee model are constructed using the agent method. Second, the evolution rules of a regional guarantee circle crisis under different situations are designed. Finally, with the help of the Anylogic simulation software, the dynamic evolution process of the regional guarantee circle crisis “brewing-triggering-burst-diffusion” is realised. By simulating the contrast demonstration of the operation effect of the guarantee circle crisis in different situations, we find the network effect between the enterprise’s individual behaviour and the guarantee circle crisis, and summarise the critical conditions that trigger the guarantee circle crisis and the general law of crisis diffusion, after which we propose the policy reasons and preventive measures arising from the guarantee circle crisis.