SYSTEMS DYNAMICS TO ANALYZE BIODIESEL CONSUMPTION AND ITS RELATION TO CO2 EMISSION FROM PUBLIC PUBLIC TRANSPORTATION WITHOUT FARES IN A MUNICIPALITY OF RIO DE JANEIRO

The present work aims to develop and evaluate a proposal of computational simulation submodels to evaluate the use of cooking oil in biodiesel generation to be used in selective waste collection trucks in some municipalities of the central region of Rio Grande do Sul. For the development of the models, the System Dynamics method was used. Mathematically, a System Dynamics model is a system of linear equations. In general, this system is too complex to be analytically solved, so numerical integration is used. Through Vensim, it was possible to develop, document, simulate and analyze the models, noting the environmental and social impact caused by the collection in the seven municipalities studied. Regarding the implementation in the Vensim simulator, historical data were used to verify the integration between the model component modules, as well as the results generated, since the outputs produced by the simulation model from real data provided to them were evaluated. The results were satisfactory and met the designers' expectations.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System Dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. SD methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity and focuses on feedback processes arising from endogenous system elements. A variety of data sources may be used in SD model development, and parametric sensitivity analysis with SD models can determine priority information needs in feedback-rich systems when data are lacking. Although numerous applications of SD modeling to agriculture exist, the approach is underutilized as a useful tool for research, instruction and programmatic development. This presentation highlights key elements of SD modeling using two examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model, and illustrates the benefits of the SD approach in animal nutrition research and for farm-level nutritional management decisions. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The presentation concludes with recommendations to increase awareness and training is SD methods to enhance its appropriate use in research and instruction.

This paper aims to understand, develop and execute computational models to analyse the cost benefit of the use of different percentages of biodiesel in urban solid waste collection and to verify the biodiesel production viability by organisations. For the models' development, the system dynamics method was used. Mathematically, System Dynamics model is a system of linear equations. Through Vensim software, it was possible to develop, document, simulate, and analyse the models, noting the financial impact caused by waste collection in seven municipalities in southern Brazil. For the modelling, nine scenarios were developed and divided into 3 groups: simulation with different percentages of biodiesel, simulation using BIOBOT 20 processor, and simulation through a plant construction by the company. As a result, it was found that, in the long term, the best scenario for the company analysed is the use of BIOBOT 20 processor with 15% biodiesel percentage, followed by the construction of its own plant, also using 15% biodiesel. Results were satisfactory and met the designers' expectations.

Many problematic outcomes in agricultural and food systems have important dynamic dimensions and arise due to underlying system structure. Thus, understanding the linkages between system structure and dynamic behavior often is important for the design and implementation of interventions to achieve sustained improvements. System dynamics (SD) modeling represents system structure using stock-flow-feedback structures expressed as systems of differential equations solved by numerical integration methods. System dynamics methods also encompass a broader methodological approach that emphasizes model structural development and data inputs to replicate one of a limited number of problematic behavioral modes, anticipates dynamic complexity, and focuses on feedback processes arising from endogenous system elements. This paper highlights the process of SD modeling using 2 examples from animal agriculture at different scales. A dynamic version of the Cornell Net Carbohydrate and Protein System (CNCPS) that represents outcomes for an individual dairy cow is formulated as an SD model illustrates the benefits of the SD approach in modeling rumen fill and animal performance. At a very different scale, an SD model of the Brazilian dairy supply chain (farms, processing, and consumers) illustrates the country-level impacts of efforts to improve cow productivity and how impacts differ if productivity improvement occurs on small farms rather than large farms. The paper concludes with recommendations about how to increase awareness and training in SD methods to enhance their appropriate use in research and instruction.

System dynamics perspectives and modeling opportunities for research in operations management

Management of freshwater supply and demand systems in coastal areas faces many challenges given the high levels of uncertainty and complexity which follow from dynamic interactions and feedbacks amongst multiple climatic and non-climatic drivers such as sea level rise, changes in precipitation and river flows, and socio-economic development. Temporal and spatial variation among these driving factors further contributes to the highly complex management challenge. These issues are prevalent in coastal areas of developing countries which typically experience high rates of population growth and urbanization. To help inform management of coastal freshwater systems under conditions of high uncertainty and complexity, this thesis developed a coupled top-down and bottom-up modelling framework for a case study setting in close consultation with local stakeholders. A system dynamics (SD) model was applied as a top-down approach to assess the vulnerability of the system under climatic and non-climatic changes, and a Bayesian decision network (BDN) model was employed as a bottom-up approach to identify cost-effective adaptation options in the face of the same climatic and non-climatic changes. This decision-making framework was developed with an understanding of the strengths and weaknesses of top-down and bottom-up approaches, and SD and BDN models as well as in the light of the dynamics and uncertainties inherent in coastal freshwater supply and demand systems. A global systematic quantitative literature review found that Bayesian networks (BNs) have rarely been coupled with SD models in water resource management, and also that BNs have rarely been applied to prioritize cost-effective adaptation measures for managing water supply and water demand under climate change in developing countries and tropical regions. Equally importantly, the literature view found that only in very few instances has the performance of BN models been tested against other modelling approaches for cross-examining model types and outputs. The freshwater supply and demand system in the Da Do Basin in Hai Phong City, Vietnam was used as a case study in this thesis to develop the coupled top-down and bottom-up modelling framework. In addition to historical data collection, causal loop diagrams (CLDs) for the system were constructed during workshops with local stakeholders to better understand how interactions among climatic and non-climatic drivers affect system operation. Stakeholder consultations at these workshops were also used to identify key climatic and non-climatic drivers for inclusion in SD and BDN models of the system, and to select a short list of potential adaptation options to counteract adverse changes in these key drivers. The SD model was developed, calibrated and tested using historical data and stakeholder knowledge. SD simulations indicated that freshwater availability is sufficient to meet existing domestic, industrial and agricultural demands during the six-month dry season under representative current conditions, but that freshwater availability could collapse under some plausible future scenarios. Upstream flow decline was identified as the strongest threat to the system‘s vulnerability, with the consequent reduction in river water level and increase in salinity level severely restricting opening hours for the sluice gates which supply freshwater to the system. The BDN model was developed in close consultation with stakeholders to identify cost-effective adaptation options to counteract climatic and non-climatic changes in key drivers. The BDN model indicated that the cost-effectiveness of adaptation options differed depending on which future scenarios were considered. Building pumping stations individually, or in conjunction with increasing water prices, were identified as the most cost-effective adaptation options to counteract climatic and non-climatic changes in combination. Subsequent simulation of these options in the SD model showed that they should be effective and robust in increasing water availability and recovering system collapse during the dry season. The ultimate objective of this coupled top-down SD and bottom-up BDN modelling approach was to provide a learning tool for stakeholders to assess system vulnerability and identify appropriate adaptation options for this complex coastal freshwater supply and demand system subject to multiple threats. Subsequent applications of this approach are likely to be highly relevant for water resource management in other basins in Hai Phong City, as well as in urban estuarine settings elsewhere in the developing and developed world.

168 Impact of Connecticut’s Good Samaritan Laws in Preventing Opioid Overdose Deaths – An Applied System Dynamics Approach

This paper provides a review on modeling electricity markets with System Dynamics (SD) focusing on deregulated electricity market models. First the SD method is classified within the wide field of electricity market modeling. Then all distinctive properties of the SD method in this context are elaborated. After an overview of first SD models in energy economics, a comprehensive review of models of deregulated electricity markets is presented. The review captures more than 80 publications in the field of SD energy market modeling. Some tendencies could be identified: Firstly SD models are more and more combined with other methods like generic algorithms, experimental economics or analytical hierarchy processes. Secondly, stochastic variables are considered increasingly. Thirdly, models show a higher level of detail and increasingly evaluate aspects such as new markets designs or new market components and their interdependencies.

Developing an agent-based simulation model of software evolution

As the number of aged infrastructures increases every year, a systematic and effective asset management strategy is required. One of the most common analysis methods for preparing an asset management strategy is life cycle cost analysis (LCCA). Most LCCA-related studies have focused on traffic and energy; however, few studies have focused on school buildings. Therefore, an approach should be developed to increase the investment efficiency for the performance improvement of school buildings. Planning and securing budgets for the performance improvement of school building is a complex task that involves various factors, such as current conditions, deterioration behavior and maintenance effect. Therefore, this study proposes a system dynamics (SD) model for the performance improvement of school buildings by using the SD method. In this study, an SD model is used to support efficient decision-making through policy effect analysis, from a macro-perspective, for the performance improvement of school buildings.

Purpose – This study analyzes the potential of zakat for poverty alleviation using a system dynamics approach. Indonesia has excellent zakat potential as a country with the largest Muslim population. However, the poverty rate in Indonesia is still high at approximately 10.14% of the total population.Methodology – This study used system dynamics to model how zakat can reduce poverty in Indonesia. The system dynamics method was chosen because of its capability to model the complexity of the system. Findings – The model indicates that increasing the percentage of productive zakat allocation and decreasing the delay of the conversion program can reduce the poverty alleviation time from 200 to 120 years using the zakat nisab standard and from 32 years using the Central Statistics Agency (BPS) poverty standard. However, if Zakat faces limited funds, the focus should be on decreasing the conversion delay. Implications –This research can encourage people to become muzakki (zakat payers) because, as indicated in the simulation models, muzakki growth of muzakki has a role in poverty alleviation. In addition, Zakat institutions can use the developed model to simulate the best policy for poverty alleviation before implementing the program.Originality – Although the system dynamics approach has shown promising findings in modeling poverty, the number of studies utilizing system dynamics to analyze the effect of zakat on poverty reduction is limited. Therefore, this study aims to evaluate the effectiveness of zakat in alleviating poverty by implementing a system-based approach and simulating system dynamics.

Purpose Both system dynamics (SD) and agent-based modeling (ABM) have been used in simulation-based group dynamics research. To combine the advantages of both simulation approaches, the concept of SD-ABM hybrid simulation has been proposed. However, research efforts to compare the effectiveness of modeling approaches between the hybrid and non-hybrid models in the context of group dynamics study are rare. Against this background, this study aims to propose an agent-embedded SD (aeSD) modeling approach and demonstrate its advantages when compared to pure SD or ABM modeling approaches, based on a research case on construction workers’ social absenteeism. Design/methodology/approach The authors introduce an aeSD modeling approach to incorporate individual attributes and interactions among individuals in an SD model. An aeSD model is developed to replicate the behavior of an agent-based model previously developed by the authors to study construction workers’ group behavior regarding absenteeism. Then, the characteristics of the aeSD model in comparison with a pure ABM or SD model are demonstrated through various simulation experiments. Findings It is demonstrated that an aeSD model can capture the diversity of individuals and simulate emergent system behaviors arising from interactions among heterogeneous agents while holding the strengths of an SD model in identifying causal feedback loops and policy testing. Specifically, the effectiveness of the aeSD approach in policy testing is demonstrated through examples of simulation experiments designed to test various group-level and individual-level interventions to control social absence behavior of workers (e.g. changing work groupings, influencing workgroup networks and communication channels) under the consideration of the context of construction projects. Originality/value The proposed aeSD modeling method is a novel approach to how individual attributes of agents can be modeled into an SD model. Such an embedding-based approach is distinguished from the previous communication-based hybrid simulation approaches. The demonstration example presented in the paper shows that the aeSD modeling approach has advantages in studying group dynamic behavior, especially when the modeling of the interactions and networks between individuals is needed within an SD structure. The simulation experiments conducted in this study demonstrate the characteristics of the aeSD approach distinguishable from both ABM and SD. Based on the results, it is argued that the aeSD modeling approach would be useful in studying construction workers’ social behavior and investigating worker policies through computer simulation.

Portable metabolic units afford a practical utility for field measurements of energy expenditure (EE). This methodology has proven useful to assess EE related to terrain, intensity, and duration during a single event. Similarly, system dynamics (SD) modeling has been used to describe the relationship between exercise and obesity as it relates to EE. However, there is paucity of literature that report SD to predict EE in real time. PURPOSE: To compare actual EE from a portable metabolic unit to predicted EE from a System Dynamics model. METHODS: Seven subjects (4 males, 3 females; 24.4 +/- 1.71) walked selected routes of varied terrain paced by a metronome at 2.7 mph. EE was measured using a Cosmed K4b2 portable metabolic unit with each subject completing four trials per route. An integrated GPS receiver recorded latitude and longitude coordinates of each route. The modeling software STELLA was used to design the SD model which incorporates subjects’ weight, walking pace, route elevation profile and distance. Pandolf’s et al (1977)) prediction equation for EE was run in the model to compare with the real-time K4b2 data. RESULTS: In simulation modeling parameters (stocks and flows) are adjusted to increase accuracy. Model parameters were adjusted to provide agreement for EE to within +/- 1% of the actual total EE as measured by the Cosmed K4b2 unit. A paired t-test comparing the actual versus the SD model predictions of total EE were not significantly different (p = .034). CONCLUSION: It appears that SD modeling can be an effective tool to predict EE of individuals walking on varied terrain. Once user parameters have been entered, simulation modeling can provide feedback on EE with suitable accuracy of a selected route. Compared to a single event measurement, SD allow users to compare EE of multiple defined routes simultaneously. Feedback has been identified as a critical component of adherence and motivation for physical activity. In this case of SD modeling, accurate feedback and route selection may encourage users to engage in regular physical activity. Funded by James Madison University Office of Public Safety.

Cement is one of the most produced materials globally. Population growth and urbanization cause an increased demand for the cement needed for expanding infrastructures. As a result of this circumstance, the cement industry must find the optimum compromise between increasing cement production and reducing the negative environmental impact of that production. Since cement production uses a lot of energy, resources and raw materials, it is essential to assess its environmental impact and determine methods for the sector to move forward in sustainable ways. This paper uses an integrated life cycle assessment (LCA) and a system dynamics (SDs) model to predict the long-term environmental impact and future dynamics of cement production in South Africa. The first step used the LCA midpoint method to investigate the environmental impact of 1 kg of Portland cement produced in South Africa. In the cement production process, carbon dioxide (CO2), nitrogen oxides (NOx), sulphur dioxide (SO2), methane (CH4) and particulate matter (PM) were the major gases emitted. Therefore, the LCA concentrated on the impact of these pollutants on global warming potential (GWP), ozone formation, human health, fine particulate matter formation and terrestrial acidification. The system dynamics model is used to predict the dynamics of cement production in South Africa. The LCA translates its results into input variables into a system dynamics model to predict the long-term environmental impact of cement production in South Africa. From our projections, the pollutant outputs of cement production in South Africa will each approximately double by the year 2040 with the associated long-term impact of an increase in global warming. These results are an important guide for South Africa’s future cement production and environmental impact because it is essential that regulations for cement production are maintained to achieve long-term environmental impact goals. The proposed LCA–SD model methodology used here enables us to predict the future dynamics of cement production and its long-term environmental impact, which is the primary research objective. Using these results, a number of policy changes are suggested for reducing emissions, such as introducing more eco-blended cement productions, carbon budgets and carbon tax.

Cascade reservoirs adaptive refined simulation model based on the mechanism-AI coupling modeling paradigm