System Dynamics Method Research Articles

Study of the factors influencing the life cycle cost of green buildings using SEM-SD method

ABSTRACT The recent surge in carbon emissions from the building sector has raised significant environmental concerns. While green building practices have proven effective, their widespread adoption has been hindered by high costs. This study aims to explore the factors impacting the life cycle costs of green buildings, with the aim of achieving “double carbon” reduction. Through an analysis of 42 articles, 24 factors were distilled based on expert opinion. After collecting 322 data through questionnaires, a model of the factors influencing the life cycle cost of green buildings was constructed using structural equation modeling, and a cost simulation study was carried out through the system dynamics method in the practical case to examine how the cost changed. The findings highlight the pivotal role of planning and design, implementation, and operation and maintenance phases. Notably, green building design programs, green construction programs, and the property management level stand out as critical factors emerged as critical factors. These insights can inform strategies to reduce the cost of green buildings, thereby fostering energy conservation and emission reduction in the construction industry.

Assessment of Reliability of Power Systems Under Adverse Weather Condition Using Markov System Dynamic Method

Abstract We aim to extend the Markov System Dynamic method (MSD) to evaluate the reliability of repairable systems in two-state weather (2SW) models. Increasing the number of components in real practices has limited the use of the Markov method, because of increasing the number of equations and the complexity of the problem. There is no such restriction in the MSD method. Thus, it can be applied to complex systems. Theoretically, we indicate that the 2SW model is a type of linear dynamical system. Therefore, we can use MSD to solve it. Through an example, the validity of the proposed method is confirmed in comparison to the Markov method for the 2SW model. Since the MSD method does not use equations, it can be a preferred alternative for calculating the reliability of 2SW models. Accordingly, we apply the MSD method for assessing the reliability of a four-component system.

Human Capital at the Regional Level: Population Forecast Insights into Latvia from a Long-Term Perspective

To remain prosperous and competitive, modern regions must meet the challenge of becoming smart and sustainable, and human capital has a key role in this context. Depopulation increases risks of human capital availability and emigration is a primary reason. Regions that are experiencing a decreased population must develop strategic sustainable regional development policies that consider scientifically based population forecasts. The youth population characterises regional potential for long-term human capital availability. First, children represent a region’s future employees and entrepreneurs. Second, children require availability of educational services at regional level, which promotes regional vitality. Third, the youth population indirectly demonstrates a region’s socio-economic attractiveness for living and working. This study employs the system dynamic method to forecast different population change scenarios at the regional level in Latvia. The results reveal a declining, long-term population tendency for working age and youth populations in Latvian regions. Overall, Latvian regions will remain extremely homogeneous in the long term, with minor differences in the short term. In conclusion, despite regional disparities in terms of quality of life with some leading examples, all regions will experience high emigration loss. Regional social, economic and environmental characteristics demonstrate a need to assess population change trends in a complex manner. This study provides data for understanding short- and long-term perspectives that must be addressed through sustainable regional development policies. Received: 27 October 2023 / Accepted: 24 March 2024 / Published: 5 May 2024

Artificial neural network‐based adaptive control for nonlinear dynamical systems

SummaryThis research article presents an artificial neural network (ANN)‐based indirect adaptive control method for nonlinear dynamical systems. In this article, a modified Elman recurrent neural network (MERNN) is proposed as an identifier and controller for controlling nonlinear systems. The architecture of the proposed controller is a modified form of the existing Elman recurrent neural network. The parameter training of ANN‐based controllers is obtained by using the most popular optimization algorithm which is known as the back‐propagation algorithm. A comparative study includes Elman, Diagonal, Jordan, feed‐forward neural network (FFNN), and radial basis function network (RBFN)‐based controllers to compare with the proposed MERNN controller. To determine the controller's robustness, parameter variations, and disturbance signals have been considered. The performance analysis of the proposed controller is illustrated by two simulation examples. The simulation results reveal that MERNN can not only identify the unknown dynamics of the plant but also adaptively control it compared to the others.

Simulasi Sistem Dinamik Pada Air Minum Dalam Kemasan Cup di Perusahaan Tirta Sasmita

The industry is experiencing rapid growth, as evidenced by the many new companies emerging. To remain competitive, companies must have a strong strategy. Tirta Sasmita Company, an integrated company with Pamulang University, which focuses on the production of Bottled Drinking Water (AMDK), faces fluctuations in the production of AMDK cups every month. Data shows an average production output of 8,430 units, with an average fluctuation of 8.33% every month in 2022. This study aims to understand the dynamic behavior of AMDK cup production and the factors that influence it. In the simulation using Anylogic 8.8. In general, the stages of solving problems using the dynamic system method are formulating the problem, identifying existing data in the production system, processing the data obtained, formulating the model mathematically, simulating the model that has been designed and obtaining results from the simulation. The results of the research show that the simulation shows that the increase in sales by implementing the optimistic scenario is quite significant, namely, in 2027 the average sales will increase by 120,892 cups or 2,518 cartons every month. Factors that influence each other in the production of AMDK cups include raw materials, production processes and sales. Therefore, the optimistic scenario is worth considering as it promises higher sales projections

Dynamical behaviors, chaotic pattern and multiple optical solitons for coupled stochastic Schrödinger–Hirota system in magneto-optic waveguides with multiplicative white noise via Itô calculus

The primary focus of this study was on exploring the optical soliton solutions and chaotic patterns in magneto-optic waveguides through the coupled stochastic Schrödinger–Hirota equation with multiplicative white noise. Firstly, by means of traveling wave transformations and homogeneous balance principle, the coupled stochastic Schrödinger–Hirota equation in magneto-optic waveguides is transformed into ordinary differential equation. By selecting some suitable parameters, phase diagrams are plotted with the help of the mathematical software Maple. Secondly, the optical soliton solutions of the coupled stochastic Schrödinger–Hirota equation corresponding to phase orbits can be easily deduced through the method of dynamical systems. In addition, chaotic behavior for the coupled stochastic Schrödinger–Hirota system with perturbation term has been discussed in detail. Finally, the two-dimensional and three-dimensional graphs of the stochastic Schrödinger–Hirota equation are drawn, which further explain the propagation of the coupled stochastic Schrödinger–Hirota equation in nonlinear optics.

A second order dynamical system method for solving a maximally comonotone inclusion problem

In this paper a second order dynamical system model is proposed for computing a zero of a maximally comonotone operator in a Hilbert space. Under mild conditions, we prove existence and uniqueness of a strong global solution of the proposed dynamical system. A proper tuning of the parameters can allow us to establish fast convergence properties of the trajectories generated by the dynamical system. The weak convergence of the trajectory to a zero of the maximally comonotone operator is also proved. Furthermore, a discrete version of the dynamical system is considered and convergence properties matching to that of the dynamical system are established under a same framework. Finally, the validity of the proposed dynamical system and its discrete version is demonstrated by two numerical examples.

The behavioral strategies of multiple stakeholders in the NIMBY facility public-private partnership project: a tripartite evolutionary game analysis based on prospect theory

In this study, evolutionary game method, prospect theory, and system dynamics (SD) method are combined to analyze the key stakeholders’ behaviors in the public-private partnership project of not-in-my-back-yard facility. First, the interaction behavior of the public sector, the private sector, and the public and their equilibrium state was analyzed based on the evolutionary game method and prospect theory. Then, the SD method was used to simulate and analyze the impact of different variables on the behavior of the three stakeholders. The results show that the greater the perceived cost difference between the active and negative behavior among the three stakeholders, the more likely they are to take negative behavior. The private sector tends to act opportunistically under low-risk loss situations. Dynamic rewards are more likely to incentivize the public to supervise than high rewards. Some recommendations to promote active behavioral interactions and cooperation among stakeholders were presented accordingly.

Peakon, Periodic Peakons, Compactons and Bifurcations of nonlinear Schrödinger’s Equation with Kudryashov’s Law of Refractive Index

In this paper, we consider the nonlinear Schrödinger’s equation with Kudryashov’s law of refractive index. By using the method of dynamical systems, we obtain bifurcations of the phase portraits of the traveling wave system under different parameter conditions. Corresponding to some special level curves, we derive possible exact explicit parametric representations of solutions (including peakon, periodic peakon, solitary wave solutions and compactons) under different parameter conditions.

Agent-based modeling and optimization of the characteristics for research-and-production clusters

This paper presents a developed agent-based simulation model for the development of research-and-production clusters in Russia implemented with the use of high-tech enterprises located in four science cities (Troitsk, Obninsk, Pushchino and Protvino) as the case study. A new approach to modeling and optimization of gross metropolitan product (GMP) is proposed, taking into account the influence of the “gravity effect” on the redistribution of labor resources between developing science cities and appropriate enterprises united in single research and research-and-production clusters An important element of this approach is the formation of various scenarios for the strategic development of the research-and-production clusters being assessed and support for the possibility of choosing the most preferable scenario using an evolutionary optimization algorithm. An enlarged simulation model has been developed and implemented in AnyLogic describing the possible development trajectories of science cities with a corresponding change in the values of the most important characteristics: the number of economically active population, the number of research-and-production enterprises, the volume of products produced in high-tech sectors of the economy, GMP, etc. The designed framework is intended primarily for the management of research-and-production clusters implementing the strategy of innovative development. Such a framework uses methods of system dynamics and agent-based simulation modeling supported in the AnyLogic system, genetic optimization algorithms and GIS mapping for science cities, etc. to implement the required functionality. The approbation of the framework was completed with the use of real data published in the approved strategies of the relevant science cities development. As a result of the numerical experiments carried out, some recommendations were proposed for the development of the research-and-production clusters under study considering their mutual influence and the existing base of resources.

Dynamics simulation model for risk probability assessment based on cognitive modeling in manned deep dive mission scenario

The manned submersible, as the main vehicle of transportation for human navigation in deep sea, has closed and narrow cabin, and with longer operating time, harder operating environment. However, once a manned deep submergence accident occurs, it is highly likely to lead to disastrous consequences. Therefore, it is urgent to develop a dynamic probabilistic risk assessment method which can quantitatively assess the cognitive behavior performance and human error of oceanauts. In this paper, a cognitive behavior simulation method for oceanaut based on knowledge and experience was proposed, which integrated richer performance shaping factors (PIF) into the IDAC model, and a cognitive environment simulation program of the IDAC model was developed through the system dynamics method, so as to analyze the dynamic human error probability of the oceanaut. In addition, the effectiveness and feasibility of the proposed model were evaluated based on real case data of manned deep submergence tasks, and the cognitive characteristics of PIFs were interpreted and classified. The research results are helpful for research institutions of manned deep submersible to accurately identify key risk points, improve the operational efficiency of the human-machine system in the cabin, and reduce the risks of the overall operating system.

Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview

Imitation Learning (IL), also referred to as Learning from Demonstration (LfD), holds significant promise for capturing expert motor skills through efficient imitation, facilitating adept navigation of complex scenarios. A persistent challenge in IL lies in extending generalization from historical demonstrations, enabling the acquisition of new skills without re-teaching. Dynamical system-based IL (DSIL) emerges as a significant subset of IL methodologies, offering the ability to learn trajectories via movement primitives and policy learning based on experiential abstraction. This paper emphasizes the fusion of theoretical paradigms, integrating control theory principles inherent in dynamical systems into IL. This integration notably enhances robustness, adaptability, and convergence in the face of novel scenarios. This survey aims to present a comprehensive overview of DSIL methods, spanning from classical approaches to recent advanced approaches. We categorize DSIL into autonomous dynamical systems and non-autonomous dynamical systems, surveying traditional IL methods with low-dimensional input and advanced deep IL methods with high-dimensional input. Additionally, we present and analyze three main stability methods for IL: Lyapunov stability, contraction theory, and diffeomorphism mapping. Our exploration also extends to popular policy improvement methods for DSIL, encompassing reinforcement learning, deep reinforcement learning, and evolutionary strategies. The primary objective is to expedite readers’ comprehension of dynamical systems’ foundational aspects and capabilities, helping identify practical scenarios and charting potential future directions. By offering insights into the strengths and limitations of dynamical system methods, we aim to foster a deeper understanding among readers. Furthermore, we outline potential extensions and enhancements within the realm of dynamical systems, outlining avenues for further exploration.

Forecasting Research on Urban Green Development Based on System Dynamics—A Case Study of Hefei in China

Urban green development is a way of economic growth and social development aiming at efficiency, harmony and sustainability, and in recent years urban green development has become an important trend for future urban development. In this study, Hefei City is selected as the study area, and a comprehensive green-development model is constructed by applying the system dynamics method, which integrates five important subsystems, namely, economy, environment, people’s livelihood, S and T and resources. Through simulation analysis, this study reveals the dynamic trend of green development in Hefei City. The analysis results show that economic growth provides the foundation for green development, scientific and technological innovation promotes the development of green technology, and that sustained investments in people’s livelihoods and environmental protection improves people’s quality of life and a city’s eco-friendliness. This study aims to promote the coordinated economic, social and environmental development of Hefei to ensure that Hefei is steadily moving towards high-quality and sustainable development goals; it effectively reveals the current situation, forecasts the trend of green development in Hefei, and also provides a reference for the urban green development of other cities.

Existence of Kink and Antikink Wave Solutions of Singularly Perturbed Modified Gardner Equation

In this paper, the singularly perturbed modified Gardner equation is considered. Firstly, for the unperturbed equation, under certain parameter conditions, we obtain the exact expressions of kink wave solution and antikink wave solution by using the bifurcation method of dynamical systems. Then, the persistence of the kink and antikink wave solutions of the perturbed modified Gardner equation is studied by exploiting the geometric singular perturbation theory and the Melnikov function method. When the perturbation parameter is sufficiently small, we obtain the sufficient conditions to guarantee the existence of kink and antikink wave solutions.

A model reduction method for parametric dynamical systems defined on complex geometries

Dynamic mode decomposition (DMD) describes the dynamical system in an equation-free manner and can be used for the prediction and control. It is an efficient data-driven method for the complex systems. In this paper, we extend DMD to the parameterized problems and propose a model reduction method based on DMD to improve the computation efficiency. This method is an offline-online mechanism. In the offline phase, we need to generate the snapshots data by solving the parameterized equations for each parameter in the training set and perform the singular value decomposition (SVD) to get the reduced operator matrices, which would lead to the substantial computation. The weighted & interpolated nearest-neighbors algorithm (wiNN) is adopted to construct the efficient surrogate models of the reduced operator matrices (including the reduced Koopman operator matrix and SVD-modes). In the online phase, for each parameter, we only need to perform the operations based on the low-dimensional matrices to get the parameter DMD solution. Moreover, we choose the least squares radial basis function finite difference method for the spatial discretization. This can make our method more applicable to the parameterized problems defined on complex geometries. At last, the reaction-diffusion, the incompressible miscible flooding and the incompressible Navier-Stokes models defined on the complex geometries are presented to illustrate the effectiveness of the proposed method.

Research on the Decision Making of Value Chain Reconstruction of Chinese Port Enterprises under the Background of Free Trade Zone Policy

This paper aims to solve the decision-making problem of value chain reconstruction of Chinese port enterprises under the background of the Free Trade Zone policy. Based on value chain theory and system dynamics method, this paper constructs a simulation model that can simulate the value-added change process of port enterprises under different combination input conditions. Furthermore, it conducts simulation case studies. The research indicates that the Free Trade Zone policy has a significant promoting effect on the restructuring of port enterprise value chains and the transformation and upgrading of enterprises. Moreover, considering the impact of the Free Trade Zone policy and limited resources, the overall benefits to port enterprises from combined investments are superior to those from single-factor investments. According to the value chain theory, the business segments of a port are decomposed into ancillary value activities, basic value activities, and external value activities. The investments in these three types of value activities play roles, respectively, in enhancing the operational efficiency of port enterprises, expanding the business scope of port enterprises, and strengthening the core competitiveness of port enterprises. From the overall perspective of the system, Shanghai Port can obtain the maximum operating profit when the endogenous factor input rate is 13%, the basic factor input rate is 4%, and the exogenous factor input rate is 13%. The findings of this research provide a decision-making reference for Chinese port enterprises to realize value reconstruction, transformation, and upgrading in the context of the Free Trade Zone policy.

Research on the Evolution Path of Supply Chain Financing for Small and Medium-sized Enterprises

Information asymmetry is the main reason why it is difficult and expensive for small and medium-sized enterprises to obtain financing. This paper uses the system dynamics method to construct an evolution model of the supply chain financing system of small and medium-sized enterprises, simulates the dynamic changes of the financing amount of the supply chain financing system of small and medium-sized enterprises, and explores the relationship between various influencing factors and their effects on the financing amount. The research shows that the supply chain financing of SMEs is a complex and dynamic system, and the overall financing amount has a significant cumulative effect, and is more sensitive to the solvency of SMEs, the relationship between core enterprises and financial institutions, government support, and the degree of supply chain information sharing. In order to alleviate the financing difficulties of small and medium-sized enterprises, the government should increase support for the supply chain financing of small and medium-sized enterprises, banks and other financial institutions should strengthen the supply chain financing service capabilities of small and medium-sized enterprises, small and medium-sized enterprises should pay attention to the improvement and upgrading of internal management, and the main body of the supply chain should strengthen cooperation and communication.

Dynamics of a strongly coupled quantum heat engine-Computing bath observables from the hierarchy of pure states.

We present a fully quantum dynamical treatment of a quantum heat engine and its baths based on the Hierarchy of Pure States (HOPS), an exact and general method for open quantum system dynamics. We show how the change of the bath energy and the interaction energy can be determined within HOPS for arbitrary coupling strength and smooth time dependence of the modulation protocol. The dynamics of all energetic contributions during the operation can be carefully examined both in its initial transient phase and, also later, in its periodic steady state. A quantum Otto engine with a qubit as an inherently nonlinear work medium is studied in a regime where the energy associated with the interaction Hamiltonian plays an important role for the global energy balance and, thus, must not be neglected when calculating its power and efficiency. We confirm that the work required to drive the coupling with the baths sensitively depends on the speed of the modulation protocol. Remarkably, departing from the conventional scheme of well-separated phases by allowing for temporal overlap, we discover that one can even gain energy from the modulation of bath interactions. We visualize these various work contributions using the analog of state change diagrams of thermodynamic cycles. We offer a concise, full presentation of HOPS with its extension to bath observables, as it serves as a universal tool for the numerically exact description of general quantum dynamical (thermodynamic) scenarios far from the weak-coupling limit.

Dynamic Coupling Analysis Between Scale of Network Retail Market and Green Circulation Efficiency

Abstract A sound circulation system is an important carrier for the development of the retail industry, and the promotion of green circulation efficiency (GCE) plays an important role in driving the market scale expansion of network retail (NR). This study uses the development status of China’s NR and circulation industries as basis to estimate the total carbon emissions (CE) of the circulation industry by using the system dynamics method. Moreover, this research measures China’s GCE by combining data envelopment analysis (DEA) and Malmquist index to construct a coupling analysis model to empirically investigate the dynamic coupling relationship between the NR scale and green circulation efficiency. Results indicate that China’s GCE is generally increasing, but growth rate fluctuates significantly. Additionally, the improvement of the quality and efficiency of China’s circulation industry has not yet formed a steady green path. China’s NR market scale continues to expand, but NR at this stage is still mainly concentrated in the eastern region. The efficiency of green circulation and scale of the NR market show a good coupling trend, but there is still a significant gap between the central and western regions. Accordingly, this study proposes that the construction of a green circulation system should be strengthened, and that the development of a green and efficient circulation industry should be taken as the engine of the NR scale expansion.

A multiscale differential‐algebraic neural network‐based method for learning dynamical systems

AbstractThe objective of dynamical system learning tasks is to forecast the future behavior of a system by leveraging observed data. However, such systems can sometimes exhibit rigidity due to significant variations in component parameters or the presence of slow and fast variables, leading to challenges in learning. To overcome this limitation, we propose a multiscale differential‐algebraic neural network (MDANN) method that utilizes Lagrangian mechanics and incorporates multiscale information for dynamical system learning. The MDANN method consists of two main components: the Lagrangian mechanics module and the multiscale module. The Lagrangian mechanics module embeds the system in Cartesian coordinates, adopts a differential‐algebraic equation format, and uses Lagrange multipliers to impose constraints explicitly, simplifying the learning problem. The multiscale module converts high‐frequency components into low‐frequency components using radial scaling to learn subprocesses with large differences in velocity. Experimental results demonstrate that the proposed MDANN method effectively improves the learning of dynamical systems under rigid conditions.