Combining System Dynamics Research Articles

System Dynamic Modeling: A Case Study of a Hotel Food Supply Chain

However, a closer examination reveals that many firms are battling inventory bottleneck as the covid pandemic spike in demand converges, which results in unpredictability and unstable sale fluctuation. This situation forced the hotel industry to find a balance between fulfilling demand and inventory turnover, which is impossible to predict accurately. Therefore, this research aims to combine system dynamics modelling with a hotel food supply chain system to solve the unpredictability of supply chain dynamics. In addition, The Causal Loop Diagram (CLD) and Stock and Flow Diagram (SFD) are used in this study to model the complexity of the case study supply chain, with an objective model to minimize the instability of inventory turnover and to stabilize the sales movement. This study proposes five policy scenario simulations where standard deviation (SD) and Standard of Error (SE) are used as the decision-making parameters. The simulation result suggests that the fifth scenario provides the highest SD (49.484) and the lowest SE (6.336). Therefore, controlling the customer response time variable to a maximum of 25 minutes and the menu unavailability variable to 15 occurrences per week will result in higher stability of the case study inventory turnover.

Embedding sustainable land-use optimization within system dynamics: bidirectional feedback between spatial and non-spatial drivers

In this paper, a space-time model is developed to support spatial planning. The temporal nature of the model allows the variables that condition land use to evolve periodically. In each time-step, land use allocation is optimized considering sustainability criteria. The model combines system dynamics techniques to simulate the evolution and interactions of economic and biophysical drivers using a multi-objective algorithm for spatial optimization that introduces bidirectional spatio-temporal feedback. It provides sets of maps for decision makers in which income and food self-sufficiency are maximized and the environmental impact of different what-if scenarios is minimized. The model has been applied in Los Llanos de San Juan (Mexico) for three scenarios: baseline, high and sustainable growth. The maps obtained by 2050 reduced water demand pressure by 1.4% and 4.7%, while crop production (in calories) was balanced with population food needs in 2035 and 2045 in the high and sustainable growth scenarios, respectively.

Collaborative optimization method of transportation planning and land use and its system unit decision making

This paper proposes a collaborative optimization method for transportation planning and land use. This method combines system dynamics models, structural equations, DEA evaluation and BP neural network technology to construct a complete transportation planning and land use collaborative optimization system. In this system, system unit determination is a key problem, which has a different relationship with each stage. This paper uses the DEMATEL model to construct a causality diagram and an incidence matrix for the system units, to identify the central influencing unit and causal influencing unit of the method of collaborative optimization of transportation planning and land use. While simplifying the model through the DEMATEL model, the macro interaction mechanism between urban traffic and land use will be clarified. Further, the extracted key influence units can be micro-decomposed to construct subsystems of micro-elements, which makes preliminary preparation for the analysis of static and dynamic integration model.

Evolutionary game of government safety supervision for prefabricated building construction using system dynamics

PurposeThis study aims to reduce the construction safety accidents of prefabricated building (PB) projects, improve the efficiency and effectiveness of safety supervision by government departments, and provide theoretical reference for improving the safety supervision system of PB construction.Design/methodology/approachConsidering the information asymmetry between government supervision departments and construction contractors and the interactive relationship between the two parties under bounded rationality, we propose an evolutionary game model for the construction safety dynamic supervision of PBs and analyze the evolutionary strategy of the game. The system dynamics (SD) method is used to simulate and analyze the evolutionary game process under a dynamic supervision strategy and the adjustment of external variables.FindingsThe cost difference between the government's strong and weak supervision, the construction contractor's additional expenditure for strengthening safety management, and other factors affect system stability. The government can dynamically adjust the penalties based on the construction contractor's subjective willingness to ignore safety management and further adjust their rate of change based on the completion of the supervision goals to improve the efficiency and effectiveness of construction safety supervision.Originality/valueThis study makes contributions in two areas. Through a combination of SD and an evolutionary game, it provides new insights into the strategic choice of the main body related to PB construction safety. Additionally, considering the nonlinear characteristics of construction safety supervision, it provides useful universal suggestions for PB construction safety.

Design, Modeling, and Visual Learning-Based Control of Soft Robotic Fish Driven by Super-Coiled Polymers.

A rapidly growing field of aquatic bio-inspired soft robotics takes advantage of the underwater animals’ bio-mechanisms, where its applications are foreseen in a vast domain such as underwater exploration, environmental monitoring, search and rescue, oil-spill detection, etc. Improved maneuverability and locomotion of such robots call for designs with higher level of biomimicry, reduced order of complex modeling due to continuum elastic dynamics, and challenging robust nonlinear controllers. This paper presents a novel design of a soft robotic fish actively actuated by a newly developed kind of artificial muscles—super-coiled polymers (SCP) and passively propelled by a caudal fin. Besides SCP exhibiting several advantages in terms of flexibility, cost and fabrication duration, this design benefits from the SCP’s significantly quicker recovery due to water-based cooling. The soft robotic fish is approximated as a 3-link representation and mathematically modeled from its geometric and dynamic perspectives to constitute the combined system dynamics of the SCP actuators and hydrodynamics of the fish, thus realizing two-dimensional fish-swimming motion. The nonlinear dynamic model of the SCP driven soft robotic fish, ignoring uncertainties and unmodeled dynamics, necessitates the development of robust/intelligent control which serves as the motivation to not only mimic the bio-mechanisms, but also mimic the cognitive abilities of a real fish. Therefore, a learning-based control design is proposed to meet the yaw control objective and study its performance in path following via various swimming patterns. The proposed learning-based control design employs the use of deep-deterministic policy gradient (DDPG) reinforcement learning algorithm to train the agent. To overcome the limitations of sensing the soft robotic fish’s states by designing complex embedded sensors, overhead image-based observations are generated and input to convolutional neural networks (CNNs) to deduce the curvature dynamics of the soft robot. A linear quadratic regulator (LQR) based multi-objective reward is proposed to reinforce the learning feedback of the agent during training. The DDPG-based control design is simulated and the corresponding results are presented.

Disabled employees on the manufacturing line: Simulations of impact on performance and benefits for companies

People with disabilities can be very valuable employees if their work is well organized and planned. However, because such employees have certain limits and additional rights, the organization and planning are more complex. This article aims to show how the employment of people with disabilities (PWDS) could influence the operation of a manufacturing line and what factors should be considered before assigning them manufacturing tasks. The analysis is supported by a simulation using combined System Dynamics and Discrete Event Simulation (respectively SDS and DES). A near-realistic case study is exploited, with data derived from existing manufacturing systems, for the sake of demonstration.

Combining System Dynamics and Agent-Based Simulation to Study the Effects of Public Interventions on Poverty

Poverty is a multidimensional social phenomenon that occurs in every economy around the world. Counteracting poverty is one of the tasks of public administration units. Under many programs financed from public funds, various tools and instruments can be used to combat poverty, but their implementation should be preceded by an in-depth analysis of the effects generated by their use. This is not easy, because the phenomenon of poverty is very complex, results from the arrangement of many interrelated heterogeneous elements, and the effects of actions are visible only after a long time. Hence, research in this area requires the use of an approach that can cope with the complexity of this phenomenon in dynamic terms. The aim of the article is to present the concept of a hybrid simulation model for studying the impact of public intervention on the level of poverty at local, regional and national level. The model is a hybrid of two computer simulation methods: System Dynamics (SD) and Agent-Based Simulation (ABS). SD method is used to model macroelements of the examined system (e.g. GDP level, labor market) and microelements (e.g. households and their members) are modeled using ABS. The article also shows the results of verification and validation of the proposed solution performed using the model for a case study. The presented solution can be used both by public administration units at various levels as well as by scientists - to conduct socio-economic research.

Back to basics: fundamental principles of system dynamics and queueing theory

Back to basics: fundamental principles of system dynamics and queueing theory

Combat process simulation and attrition forecasting based on system dynamics and Multi-agent modeling

The purpose of this research is to develop a tool for all medical support and war rescue researchers that can predict and analyze the total number of attritions in combat, the temporal and spatial distribution of attritions, and the composition of the wounded. All the parameters in the tool are adjustable for the users to personalize the settings and to simulate different combat environments according to their needs. This research adopts a method that combines system dynamics and intelligent agent simulations. First, a combat process simulation and an attrition prediction model are constructed using system dynamics methods. The model converts the degrees of combat damage of the various targets of the red and blue forces into attrition data by combining specific combat missions, analyzing the factors influencing the combat, weapon performance, and protection capability of the two forces, and constructing a causal loop and stock flow relationship for the engagement process. On this basis, an intelligent-agent modeling method is used to obtain the total attrition from the combat attrition prediction model, and to decompose the total attrition, which includes only time and quantity, into each specific individual combatant. By establishing a correspondence relationship between the degree of damage to the combat target and the various types of combat injuries, these individual combatants can be assigned with specific combat injury information according to specific proportions. By simulating each individual combatant of the attrition personnel and assigning a specific injury status, the attrition flow is converted into a casualty flow. In the process, a scientific basis is provided for formulating a medical support plan, raising logistic personnel and resources, properly allocating medical resources, and effectively implementing injury treatment.

A Scenario-Based Management of Water Resources and Supply Systems Using a Combined System Dynamics and Compromise Programming Approach

Long-term sustainability in water supply systems is a major challenge due to water resources depletion, climate change and population growth. This paper presents a scenario-based approach for performance assessment of intervention strategies in water resources and supply systems (WRSS). A system dynamics approach is used for modelling the key WRSS components and their complex interactions with natural and human systems and is combined with a multi-criteria decision analysis for sustainability performance assessment of strategies in each scenario. The scenarios combine population growth rates with groundwater extraction limits against two types of intervention strategies. The methodology was demonstrated on a real-world case study in Iran. Results show scenario-based analysis can provide suitable strategies leading to long-term sustainability of water resources for each scenario externally imposed on the water systems. For scenarios with either no threshold or one threshold of groundwater extraction limit, the only effective strategies for sustainable groundwater preservation are those involving agricultural water demand decrease with an average recovery rate of 130% for groundwater resources while other strategies of agricultural groundwater abstraction (constant/increase rates) fail to sustainably recover groundwater resources. However, all analysed strategies can provide sustainability of water resources with an average recovery rate of 33% for groundwater resources only when scenarios with two threshold limits are in place. The impact of scenarios with population growth rates on groundwater conservation is quite minor with an average recovery rate of 11% compared to scenarios of groundwater extraction limits with an average recovery rate of 79% between no threshold and two threshold limits.

The Analysis of Mobile Phone Consumers’ Behavior by Combining System Dynamics (SD) and Agent-based Modelling (ABM)

The purpose of this paper is to prepare a hybrid simulation model of system dynamics (SD) - agent-based modeling (ABM) to investigate the mobile service consumers’ behavior in the country. By using the suggested in this article, first the necessity to analysis consumers’ behavior is explained and then the effective factors on mobile ecosystem which influence the consumers’ behavior are explained.For each factor affecting on the ecosystem, related simulations were performed and then by combining system dynamics modeling - agent -based modeling, the behavior of mobile service consumers in the first operator of the country (Hamrahe Aval Company) was examined and finally the income affected by that behavior is analyzed.Results indicated that if no funds by the operator are allocated to the development of native applications and digital platform, after 2 years, the number of active customers of the operator will decrease because of the activities of cultural organizations. By the operator entering into the field of native application production and digital transformation, consumers tended to use more data services instead of voice calls, but because of different data and voice tariffs, the operator's income will not change much in the next 2 years. With the increase of marketing and advertising activities, despite the greater consumer inclination to use data, the main income of the operator decreases, which also needs more attention of policymakers in this area.

Predicting delays in prefabricated projects: SD-BP neural network to define effects of risk disruption

PurposePrefabricated construction is often hindered by scheduling delays. This paper aims to propose a schedule delay prediction model system, which can provide the key information for controlling the delay effects of risk-related factors on scheduling in prefabricated construction.Design/methodology/approachThis paper combines SD (System Dynamics) and BP (Back Propagation) neural network to predict risk related delays. The SD-based prediction model focuses on dynamically presenting the interrelated impacts of risk events and activities along with workflow. While BP neural network model is proposed to evaluate the delay effect for a single risk event disrupting a single job, which is the necessary input parameter of SD-based model.FindingsThe established model system is validated through a structural test, an extreme condition test, a sensitivity test, and an error test, and shows an excellent performance on aspect of reliability and accuracy. Furthermore, 5 scenarios of case application during 3 different projects located in separate cities prove the prediction model system can be applied in a wide range.Originality/valueThis paper contributes to academic research on combination of SD and BP neural network at the operational level prediction, and a practical prediction tool supporting managers to take decision-making in a timely manner against delays.

Research on coupled vibrations of driveshaft–rear axle system based on nonlinear hybrid model

The transmission shaft–rear axle system is consisted of universal joint, hypoid gear pair, tapered roller bearing, and half shaft. In this article, as a combination of system dynamics and structure dynamics, a hybrid dynamic model of the bending–torsional–shaft vibration of the driveshaft main reducer was established to study its noise vibration harshness performance. Through dynamic frequency sweep analysis, it is found that the natural frequency of torsional vibration of the system is 53.96 Hz. Getting the natural frequency of the torsional vibration of the systems being 57 Hz by the bench tests and Adams simulations. The correctness of the nonlinear hybrid model of the driveshaft–rear axle system is proved. This research will be beneficial to the design, monitoring, and fault detection of the automobile transmission system.

Investigating the Investments Required to Transition New Zealand’s Heavy-Duty Vehicles to Hydrogen

Reducing greenhouse gas emissions in the transport sector is known to be an important contribution to climate change mitigation. Some parts of the transport sector are particularly difficult to decarbonize; this includes the heavy-duty vehicle sector, which is considered one of the “hard-to-abate” sectors of the economy. Transitioning from diesel trucks to hydrogen fuel cell trucks has been identified as a potential way to decarbonize the sector. However, the current and future costs and efficiencies of the enabling technologies remain unclear. In light of these uncertainties, this paper investigates the investments required to decarbonize New Zealand’s heavy-duty vehicle sector with green hydrogen. By combining system dynamics modelling literature and hydrogen transition modelling literature a customized methodology is developed for modelling hydrogen transitions with system dynamics modelling. Results are presented in terms of the investments required to purchase the hydrogen production capacity and the investments required to supply electricity to the hydrogen production systems. Production capacity investments are found to range between 1.59 and 2.58 billion New Zealand Dollars, and marginal electricity investments are found to range between 4.14 and 7.65 billion New Zealand Dollars. These investments represent scenarios in which 71% to 90% of the heavy-duty vehicle fleet are replaced with fuel cell trucks by 2050. The wide range of these findings reflects the large uncertainties in estimates of how hydrogen technologies will develop over the course of the next thirty years. Policy recommendations are drawn from these results, and a clear opportunity for future work is outlined. Most notably, the results from this study should be compared with research investigating the investments required to decarbonize the heavy-duty vehicle sectors with alternative technologies such as battery-electric trucks, biodiesel, and catenary systems. Such a comparison would ensure that the most cost effective decarbonization strategy is employed.

Comparison Research on Different Mitigation Strategies of Supply Chain under Supply Disruption Scenarios

With the rapid development of economic globalization, global procurement and supply, lean on-time production and other methods have been widely used. The single competition between enterprises turns into the competition between supply chain and supply chain. However, the occurrence of natural disasters, terrorist attacks and the game and coupling of the internal structure of the supply chain make the supply chain encounter the interruption crisis in the process. Therefore, studying different mitigation strategies for the interruption of node locations in the supply chain is particularly important. In this paper, supply interruption cases are designed, and the simulation models of the dual-chain one-way coordination strategy mode and the dual-chain multi-node two-way coordination strategy mode are constructed by combining system dynamics. The dynamic operation mechanism of supply chain system with different mitigation strategies is deduced by two indexes of “inventory level” and “order accumulation rate”. The results show that the “dual-chain multi-node two-way coordination strategy” has the best performance in improving the defense ability of node enterprises.

A neural system dynamics modeling platform and its applications in randomized controlled trial data analysis

Conventional statistical methods used in clinical trials lack the ability to predict patient specific risk and very often do not consider the effects of time varying interventions. The aim of this study is to test a novel artificial neural network based model for clinical trial analysis to represent the continuous time evolution of risk. The novel methodology tested utilizes system dynamics and artificial neural networks. This methodology was applied to analyze data from 2,221 patients with acute myocardial infarction enrolled in a well characterized randomized study, the HORIZONS-AMI trial. Outcomes analyzed included: (1) target lesion revascularization (TLR) and (2) stent thrombosis. The proposed neural system dynamics (NSD) model was compared against traditional Cox Proportional Hazards (Cox PH) and Cox neural network (NN) models. Model performance was evaluated using C-statistic at 1, 2 -and 3- years follow-up and model-based simulation studies were performed to examine the effect of different variables on the predicted risk of TLR and stent thrombosis. The NSD model achieved comparable performance to Cox models for TLR. For stent thrombosis, the NSD model outperformed Cox models (1-year C-statistic: Stent thrombosis – Cox PH: 0.60, Cox NN: 0.66, neural SD model: 0.69). The neural SD model identified clinically relevant variables such as stent count, stent type and multiple lesions treated as significant predictors for TLR; and stent count and peak platelet count for stent thrombosis. Simulations illustrated change in predicted TLR risk maximal for the neural SD model, compared to other models. For stent thrombosis, simulation scenario illustrated predicted event rate doubling when clopidogrel is discontinued at 6 months compared to extended use. We have demonstrated an alternative analytical methodology that combines system dynamics and artificial neural networks to analyze results from a randomized trial. This novel approach can incorporate patient-specific longitudinal data and provide personalized risk prediction. • Incorporation of non-linear interactions and time-varying data improves personalized risk prediction. • Neural System Dynamics (NSD) model combines neural networks with system dynamics. • NSD model represents continuous time evolution of risk. • NSD model captures clinically relevant risk predictors of Target Lesion Revascularization and Stent Thrombosis. • NSD model based simulations provide more personalized risk trajectories with actionable insights using follow-up intervention information.

Greenhouse gas emission mitigation potential from municipal solid waste treatment: A combined SD-LMDI model

Fast urbanization and economic prosperity generate huge amount of municipal solid waste (MSW). It is therefore critical to identify the determinants of greenhouse gas (GHG) emissions from MSW treatment and prepare potential GHG mitigation measures. A combined System Dynamics - Logarithmic Mean Divisia Index model is developed to identify the driving forces of GHG emission generated from MSW treatment and explore the mitigation potentials. Shanghai, a typical megacity in China is selected as a case study. Results showed that economic development, population scale and emission intensity were driving forces to induce GHG emissions from MSW treatment, while generation intensity and treatment structure were the factors to mitigate GHG emissions from MSW during 2000–2017. Scenario analysis further revealed that landfill gas utilization and MSW separation improvement were the most effective measures in reducing GHG emissions from MSW treatment, leading to about 88.07% and 85.48% of reduction compared with the business-as-usual scenario in 2050. Scenarios of improving incineration rate, reducing per capita MSW generation and restricting population growth will reduce GHG emissions by 72.29%, 30.06% and 0.30%, respectively. Utilizing landfill gas, improving MSW separation and promoting green behaviors are suggested to mitigate GHG emissions from MSW treatment.

A Measurement Model of Value of Data for Decision Making in the Digital Era

Despite burgeoning opportunities for data-driven decisions, research shows that decision makers are failing to make sense of data within a broader context of organisational change which presents the following pertinent questions: 1) How can decision makers measure the value of data by giving a holistic account?; 2) How should the organisation-specific blending of machine and human rationality be factored in the measurement model? This study tackles these questions by proposing a novel approach that combines system dynamics and the ability to incorporate data science methods. In addition to a conceptual description, this paper also describes a feasibility test conducted on a small-scale prototype set in a supply chain context. The results show that the use of sophisticated models that have a local scope ('locally rational') might have unintended global consequences. It underscores the need for a holistic model in the decision makers' toolkit providing the ability to run targeted simulations to assess digital investments.

Tourism, transport, and land use: a dynamic impact assessment for Kaohsiung’s Asia New Bay Area

ABSTRACT This paper proposes a hybrid methodology for analysing the causal relations between public transportation development, tourism and land use by combining System Dynamics (SD) with Geographical Information Systems (GIS) and agent-based modelling (ABM). It is applied to illustrate the quantitative and spatial effect of the two phases Light Rail Transit (LRT) development in Asia New Bay Area, Kaohsiung, Taiwan. This paper also furthers the application of the ABM spatial information as interactive variables in the stocks-flow model. The simulation results support that development policies of LRT are significant to the future tourism in Asia New Bay Area, while the under debate second phase of LRT is estimated to raise the number of visitors in long term by alleviating the deteriorating road traffic congestion. This policy-oriented simulation can serve as a reference to the decision-makers towards the future management of LRT.

An ecological macroeconomics model: The energy transition in the EU

The Energy Roadmap 2050 (ER2050) is committed to achieve the European Union's emissions mitigation goals by reducing energy use and a transition to renewables. The macroeconomic impacts of the Ref16 and ‘EUCO’ scenarios of this strategy have been reported to entail an absolute decoupling between GDP growth and energy use. The aim of this paper is assessing the ER2050 targets with a novel modelling methodology based on Post-Keynesian Economics, i.e. demand-led economic growth and Ecological Economics, i.e. taking into account absolute biophysical (energy availability) constrains to economic growth. Thus, this article presents the Economy module of the Integrated Assessment Model MEDEAS-Europe, combining System Dynamics and Input-Output analysis, and evaluates the ER2050 targets under different scenarios regarding primary income distribution, foreign trade, labour productivity, industrial policy and working time reduction. Our results show that GDP growth and employment creation may be halted due to energy scarcity if the ER2050 targets are met even considering great energy efficiency gains. In addition, the renewables share would increase enough to reduce the energy imports dependency, but not sufficiently to meet the emissions targets. Only a Post-Growth scenario would be able to meet the climate goals and maintain the level of employment.