The Construction and Application of Work and Production Resuming Behavior System Dynamics Evolutionary Game Regulation and Control Model

The resumption of production after the "suspension” caused by the COVID-19 has emerged as an urgent problem for many enterprises and the government. The resumption of production is actually a dynamic evolution problem from 0 to 1 (100%). This paper constructs a general game model and a dynamic replication system for the resumption of production and government support, and gives theorems for the construction of the model. It analyzes the evolution mechanism and scenario conditions for the convergence of enterprise strategies to the "resumption of production” strategy, takes the resumption of production of hog farmers as an example to carry out a study on the regulation of countermeasures to resume hog production, and explores systemic countermeasures and suggestions for the rapid convergence of farmers' strategies to the "resumption of work and production” strategy. The study found that the production resuming behavior system dynamics evolution game regulation model provides a systematic model and method for the study of resumption countermeasures, a general regulation model for the resumption ratio from 0 to 1 (100%), and a systematic idea, method and model for exploring the "precise strategy” system to promote the rapid resumption of production. © 2023, Science Press (China). All rights reserved.

Research on the spread of infectious diseases based on computer simulation

The virus has a big impact on the whole world. The new Coronavirus has a great impact on everyone's life and will even lead to changes in the world pattern. Because of the virus, society is not functioning properly, the recession, people's expectations of economic development are falling. Trains and planes were suspended in some areas. In this paper, computer is used to simulate SIR model, based on system dynamics, to study the spread of infectious diseases. The SIR model passes reality and limit tests. On the basis of the original model, supplementing the original model, isolation and vaccination can effectively stop the spread of the virus. It can slow the outbreak of the virus and reduce the number of infected people. Panic comes from the unknown, and our confidence in defeating the 2019-nCoV virus comes from our scientific base. © 2022 IEEE.

Hybrid manufacturing-remanufacturing system of essential commodity in the context of COVID-19

PurposeThis paper aims to assess the feasibility of a hybrid manufacturing and remanufacturing system (HMRS) for essential commodities in the context of COVID-19. Specifically, it emphasises using HMRS based on costs associated with various manufacturing activities.Design/methodology/approachThe combination of mathematical model and system dynamics is used to model the HMRS system. The model was tried on sanitiser bottle manufacturing to generalise the result.FindingsThe remanufacturing cost is higher because of reverse logistics, inspection and holding costs. Ultimately remanufacturing costs turn out to be lesser than the original manufacturing the moment system attains stability.Practical implicationsThe study put forth the reason to encourage remanufacturing towards sustainability through government incentives.Originality/valueThe study put forth the feasibility of the HMRS system for an essential commodity in the context of a covid pandemic. The research implemented system dynamics for modelling and validation.

Projecting the Electricity Demand of the Residential Sector in Thailand During and After the COVID-19 Pandemic

The residential sector in Thailand has been a fast-growing energy consumption sector since 1995 at a rate of 6% per year. This sector makes a significant contribution to Thailand's rising electricity demand especially during the COVID-19 pandemic. This study projects Thailand's residential electricity consumption characteristics and the factors affecting the growth of electricity consumption using a system dynamics (SD) modeling approach to forecast long-term electricity consumption in Thailand. Furthermore, the COVID-19 pandemic and the lockdown can be seen as a forced social experiment, with the findings demonstrating how to use resources under particular circumstances. Four key factors affecting the electricity demand used in the SD model development include (1) work and study from home, (2) socio-demographic, (3) temperature changing, and (4) rise of GDP. Secondary and primary data, through questionnaire survey method, were used as data input for the model. The simulation results reveal that changing behavior on higher-wattage appliances has huge impacts on overall electricity consumption. The pressure to work and study at home contributes to rises of electricity consumption in the residential sector during and after COVID-19 pandemic. The government and related agencies may use the study results to plan for the electricity supply in the long term. © 2023 World Scientific Publishing Co.

Organizational decision‐making during COVID‐19: A qualitative analysis of the organizational decision‐making system in the United States during COVID‐19

This study sought to understand COVID‐19‐related organizational decisions were made across sectors. To gain this understanding, we conducted semi‐structured interviews with organizational decision‐makers in North Carolina about their experiences responding to COVID‐19. Conventional content analysis was used to analyse the context, inputs, and processes involved in decision‐making. Between October 2020 and February 2021, we interviewed 44 decision‐makers from the following sectors: business (n = 4), community non‐profit (n = 3), county government (n = 4), healthcare (n = 5), local public health (n = 5), public safety (n = 7), religious (n = 6), education (n = 7) and transportation (n = 3). We found that during the pandemic, organizations looked to scientific authorities, the decisions of peer organizations, data about COVID‐19, and their own experience with prior crises. Interpretation of inputs was informed by current political events, societal trends, and organization mission. Decision‐makers had to account for divergent internal opinions and community behaviour. To navigate inputs and contextual factors, organizations decentralized decision‐making authority, formed auxiliary decision‐making bodies, learned to resolve internal conflicts, learned in real time from their crisis response, and routinely communicated decisions with their communities. In conclusion, aligned with systems and contingency theories of decision‐making, decision‐making during COVID‐19 depended on an organization's 'fit' within the specifics of their existing system and their ability to orient the dynamics of that system to their own goals. [ FROM AUTHOR] Copyright of Journal of Contingencies & Crisis Management is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Are smart contracts too smart for Supply Chain 4.0? A blockchain framework to mitigate challenges: IMS

PurposeSmart contracts are self-executing computer programmes that have the potential to be used in several applications instead of traditional written contracts. With the recent rise of smart systems (e.g. Internet of things) and digital platforms (e.g. blockchain), smart contracts are gaining high interest in both business and academia. In this work, a framework for smart contracts was proposed with using reputation as the system currency, and conducts currency mining through fulfilling the physical commitments that are agreed upon.Design/methodology/approachA game theory model is developed to represent the proposed system, and then a system dynamics simulator is used to check the response of the blockchain with different sizes.FindingsThe numerical results showed that the proposed system could identify the takeover attacks and protect the blockchain from being controlled by an outsider. Another important finding is that careful setting of the maximum currency amount can improve the scalability of the blockchain and prevent the currency inflation.Research limitations/implicationsThis work is proposed as a conceptual framework for supply chain 4.0. Future work will be dedicated to implement and experiment the proposed framework for other characteristics that may be encountered in the context of supply chain 4.0, such as different suppliers' tiers, different customer typologies and smart logistics applications, which may reveal other challenges and provide additional interesting insights.Practical implicationsBy using the proposed framework, smart contracts and blockchains can be implemented to handle many issues in the context of operations and supply chain 4.0, especially in times of turbulence such as the COVID-19 global pandemic crisis.Originality/valueThis work emphasizes that smart contracts are not too smart to be applied in the context of supply chain 4.0. The proposed framework of smart contracts is expected to serve supply chain 4.0 by automating the knowledge work and enabling scenario planning through the game theory model. It will also improve online transparency and order processing in real-time through secured multitier connectivity. This can be applied in global supply chain functions backed with digitization, notably during the time of the pandemic, in which e-commerce and online shopping have changed the rules of the game.

Model‐informed health system reorganization during emergencies

The COVID‐19 pandemic presented the world to a novel class of problems highlighting distinctive features that rendered standard academic research and participatory processes less effective in properly informing public health interventions in a timely way. The urgency and rapidity of the emergency required tight integration of novel and high‐quality simulation modeling with public health policy implementation. By introducing flexibility and agility into standard participatory processes, we aligned the modeling effort with the imposed reality of the emergency to rapidly develop a regional system dynamics (SD) model integrating diverse streams of data that could reliably inform both health system restructuring and public health policy. Using Lombardy data, our SD model was able to generate early projections for the diffusion of the pandemic in neighbor Ticino. Later, it projected the timing and size of peak patient demand. Our work also supported the need for reorganization of the healthcare system and volume flexibility strategies increasing hospital capacity (e.g., intensive care unit [ICU] and ward beds, medical and nursing staff, and oxygen supply) in Ticino. Counterfactual analyses quantify the impact of the decisions supported by our interventions. Our research contributes to our understanding of volume flexibility strategies used by healthcare organizations during emergencies, highlighting the critical role played by available response time in the deployment of strategies that either prioritize critical services or leverage available resources. It also contributes to the literature on participatory systems modeling by describing a flexible and agile participatory process that was successfully deployed in a rapidly evolving high‐stakes emergency.

Change time estimation uncertainty in nonlinear dynamical systems with applications to COVID‐19

The impact that each individual non‐pharmaceutical intervention (NPI) had on the spread rate of COVID‐19 is difficult to estimate, since several NPIs were implemented in rapid succession in most countries. In this article, we analyze the detectability of sudden changes in a parameter of nonlinear dynamical systems, which could be used to represent NPIs or mutations of the virus, in the presence of measurement noise. Specifically, by taking an agnostic approach, we provide necessary conditions for when the best possible unbiased estimator is able to isolate the effect of a sudden change in a model parameter, by using the Hammersley–Chapman–Robbins (HCR) lower bound. Several simplifications to the calculation of the HCR lower bound are given, which depend on the amplitude of the sudden change and the dynamics of the system. We further define the concept of the most informative sample based on the largest ℓ2 distance between two output trajectories, which is a good indicator of when the HCR lower bound converges. These results are thereafter used to analyze the susceptible‐infected‐removed model. For instance, we show that performing analysis using the number of recovered/deceased, as opposed to the cumulative number of infected, may be an inferior signal to use since sudden changes are fundamentally more difficult to estimate and seem to require more samples. Finally, these results are verified by simulations and applied to real data from the spread of COVID‐19 in France. [ FROM AUTHOR] Copyright of International Journal of Robust & Nonlinear Control is the property of John Wiley & Sons, Inc. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Collaboration-competition dilemma in flattening the COVID-19 curve.

Testing for COVID-19 is a key intervention that supports tracking and isolation to prevent further infections. However, diagnostic tests are a scarce and finite resource, so abundance in one country can quickly lead to shortages in others, creating a competitive landscape. Countries experience peaks in infections at different times, meaning that the need for diagnostic tests also peaks at different moments. This phase lag implies opportunities for a more collaborative approach, although countries might also worry about the risks of future shortages if they help others by reallocating their excess inventory of diagnostic tests. This article features a simulation model that connects three subsystems: COVID-19 transmission, the diagnostic test supply chain, and public policy interventions aimed at flattening the infection curve. This integrated system approach clarifies that, for public policies, there is a time to be risk-averse and a time for risk-taking, reflecting the different phases of the pandemic (contagion vs. recovery) and the dominant dynamic behavior that occurs in these phases (reinforcing vs. balancing). In the contagion phase, policymakers cannot afford to reject extra diagnostic tests and should take what they can get, in line with a competitive mindset. In the recovery phase, policymakers can afford to give away excess inventory to other countries in need (one-sided collaboration). When a country switches between taking and giving, in a form of two-sided collaboration, it can flatten the curve, not only for itself but also for others.

System Dynamics Modeling for Assessing the Impact of COVID-19 on Food Supply Chains: A Case Study of Kenya and Rwanda

Disruptions in the food supply chains caused by the COVID-19 pandemic have destabilized the balance between production, supply, transport, distribution, and consumption. Consequently, these disruptions have affected food and nutritional security all over the world. This study proposes a framework for investigating the impact of COVID-19 on food supply chains, considering Eastern Africa as a focus region with Kenya and Rwanda as case studies. A systems thinking approach with three systemic components (food and nutrition, COVID-19 contagion, and human health) was applied. The contagion component was characterized by the susceptible, exposed, infected, recovered, and deceased (SEIRD) epidemiological modeling method. We then applied a causal loop diagram and stock and flow diagrams to map the links and interactions between variables from the contagion, health, and food supply chain components of the whole system. The results reveal that COVID-19 has adversely affected food and nutritional security in Eastern African countries. Key response measures to COVID-19 such as lockdowns, closure of borders, isolation, and quarantining have resulted in labor shortages, increased unemployment rates, loss of income, and the subsequent contraction of economies. The disruption of the food supply chain has negatively impacted the main pillars of food and nutrition security, which are availability, accessibility, utilization, and stability. We suggest direct food supply from local producers to the consuming communities to shorten the food supply chain and therefore enhance food self-sufficiency to reduce the severe effects of COVID-19 on food and nutrition security. Overall, our study provides a useful framework to help design better policies and build more resilient and inclusive food systems during COVID-19 and similar pandemics in the future.

Systemic risks in supply chains: a need for system-level governance

PurposeGlobal and interconnected supply chains are increasingly exposed to systemic risks, whereby individual failures propagate across firms, sectors and borders. Systemic risks have emerged from the decisions of individual firms, e.g., outsourcing and buffer reduction, and are now beyond their control. This paper aims to identify appropriate approaches to mitigating those risks.Design/methodology/approachSystemic risks require analyzing supply chains beyond a dyadic perspective. This study approaches the problem through the lenses of complex systems and network theories. Drawing on the lessons learned from other systemic-risk-prone systems, e.g. energy and financial networks, both in research and practice, this study analyzes the adequate level of governance to monitor and manage systemic risks in supply chains.FindingsThe authors argue that governance institutions should be mandated to overview and reduce systemic risks in supply chains from the top down, as central bankers do for the financial system. Using firm-level data and tools from network analysis and system dynamics, they could quantify systemic risks, identify risk-prone interconnections in supply chains and design mitigating measures. This top-down approach would complement the bottom-up supply chain management approach and could help insurers design policies for contingent business interruptions.Originality/valueInstead of looking at supply chains purely from the firms' angle, the perspective of insurers and governments is brought in to reflect on the governance of risks.

Influence of digital divide in the entrepreneurial motor of a digital economy: a system dynamics approach

The creation of digital ventures in developing countries is an alternative to the generation of jobs in the ICT sector, but the lack of qualified individuals in ICT-related skills inhibits the growth of new companies, a gap that is analyzed with the digital divide theory. This research aims to understand the dynamics using a simulation model that combines aspects of the adoption of ICTS, internet availability, skills, with the entrepreneurial motor of innovation systems. The methodology is developed with data from a national ICT survey and organizations in Colombia. Different scenarios are analyzed regarding ICT education and inclusive policies. Results show that Covid-19 pandemic consequences could have a negative effect during five years and that under a scenario of accelerated growth of ICT, the sector could demand up to 400.000 ICT- related jobs by 2035. The main contribution of this research is the understanding of the ICT systems from an inclusive perspective, identifying the key variables that determine the growth of ventures and the development of digital skills among individuals. © 2023

Measuring the performance of SMEs during the pandemic situation using system dynamic

Purpose: This study aims to create a system dynamics simulation model to forecast the performance of small and medium-sized enterprises (SMEs) if some decision-making is executed to reduce the negative of the coronavirus disease 2019 (COVID-19) pandemic. In particular, this study will focus on SMEs that belong to the furniture industry because the furniture industry is one of the leading industries in Indonesia. Design/methodology/approach: The study develops a system dynamics-based model by using three subsystems, i.e. the "production subsystem,” "demand and revenue subsystem” and "raw material (or wood supply) subsystem.” Findings: The best scenario is the third scenario which increases the capacity to the normal situation and government subsidy during and after the pandemic. This scenario gives the best performance for industry revenue and gross domestic product (GDP). However, for the government, the most significant expenditure occurs in the third scenario. This seems a trade-off for the government whether to save the wooden-based furniture industry by encouraging the industry to continue operating during the pandemic accompanied by high subsidies or limiting the activities of the wooden-based furniture industry to prevent the spread of COVID-19 by providing low subsidies. Research limitations/implications: First, this study does not try to combine the system dynamics (SD) methodology with the other method or use a multi-methodology since SD has several limitations and the other method may have several advantages compared to SD. Second, the models used in this study do not consider the decline in forest area and quality. Third, the demand for wooden-based furniture is obtained from historical data on domestic and foreign sales and fourth, the model does not include the government budget as a constraint to make any subsidy to help the SMEs. Practical implications: This study provides essential insights into implementing the policies in the world pandemic situation when SMEs face lockdown policy. Social implications: The study revealed that relevant policy scenarios could be built after simulating and analyzing each scenario's effect on SMEs' performance during the pandemic. Originality/value: This study will enrich the previous study on the impact of the pandemic on SMEs and the dynamic system modeling on SMEs. The previous study discussed the pandemic's impact on SME performance and the impact's analysis in isolation from the dynamic nature of SME owners' decisions or government policy. In this study, the impact generated from the pandemic situation could be different depending on the decision and policies taken by managers from SMEs and the government. © 2023, Emerald Publishing Limited.

Thermal Comfort Analysis Using System Dynamics Modeling—A Sustainable Scenario Proposition for Low-Income Housing in Brazil

As a riveting example of social housing in Brazil, the Minha Casa Minha Vida program was set in 2009 to diminish the 6-million-home housing deficit by offering affordable dwellings for low-income families. However, recurrent thermal discomfort complaints occur among dwellers, especially in the Baltimore Residential sample in Uberlândia City. To avoid negative effects of energy poverty, such as family budget constraints from the purchase of electric appliances and extra costs from power consumption, a simulation based on system dynamics modeling shows a natural ventilation strategy with a mixed combination of sustainable and energy-efficient materials (tilting window with up to 100% opening, green tempered glass, and expanded polystyrene wall) to observe the internal room temperature variation over time. With a 50% window opening ratio combined with a 3 mm regular glass window and a 12.5 cm rectangular 8-hole brick wall, this scenario presents the highest internal room temperature value held during the entire period. From the worst to the best-case scenario, a substantial reduction in the peak temperature was observed from window size variation, demonstrating that natural ventilation and constructive elements of low complexity and wide availability in the market contribute to the thermal comfort of residential rooms.

Developing an agent-based model to minimize spreading of malicious information in dynamic social networks.

This research introduces a systematic and multidisciplinary agent-based model to interpret and simplify the dynamic actions of the users and communities in an evolutionary online (offline) social network. The organizational cybernetics approach is used to control/monitor the malicious information spread between communities. The stochastic one-median problem minimizes the agent response time and eliminates the information spread across the online (offline) environment. The performance of these methods was measured against a Twitter network related to an armed protest demonstration against the COVID-19 lockdown in Michigan state in May 2020. The proposed model demonstrated the dynamicity of the network, enhanced the agent level performance, minimized the malicious information spread, and measured the response to the second stochastic information spread in the network.

Multi-method simulation approach: multiple restaurants to multiple customers on-demand food delivery services in Dubai

Since early 2020, the COVID-19 outbreak has disrupted various supply chains including the on-demand food delivery sector. As a result, this service industry has witnessed a tremendous spike in demand that is affecting its delivery operations at the downstream level. Previous research studies have explored one-to-one and many-to-one solutions to the virtual food court delivery problem (VFCDP) to optimize on-demand food delivery services in different cities. However, research efforts have been limited to multiple restaurant orders from only one customer which does not apply to traditional systems where multiple customers request on-demand food delivery from multiple restaurants. This study rigorously analyses multiple restaurants to multiple customers (Many-to-many) food delivery simulation models in ideal weather conditions that are constrained with multiple key performance indicators (KPIs) such as delivery fleet utilization (the number of couriers utilized over the fleet size), average order delivery time, and fuel costs. This research also benchmarks the on-demand food delivery queueing methodologies using system dynamics and agent-based simulation modeling where three on-demand food delivery routing methodologies are simulated including First-in-First-Out (FIFO), Nearest, and Simulated Annealing using AnyLogic. The results suggest that the Many-to-many (Nearest) method outperforms other delivery routing methods which would have positive implications on optimizing existing food delivery systems and managerial decisions.

Building a system dynamics model to analyze scenarios of COVID-19 policymaking in tourism-dependent developing countries: A case study of Cambodia

To control the COVID-19 pandemic, various policies have been implemented to restrict the mobility of people. Such policies, however, have resulted in huge damages to many economic sectors, especially the tourism sector and its auxiliary services. Focusing on Cambodia, this study presents a system dynamics (SD) model for assessing and selecting effective policy responses to contain the spread of COVID-19, while maintaining tourism development. Policies targeted in this study include international and domestic transportation bans, quarantine policy, tourist-centered protection measures, and enterprise-led protection measures. Two types of scenario analyses are conducted: one targets each policy separately and the other combines different policies. Among all scenarios, quarantine policy is evaluated to be the most effective policy as it balances the containment of the spread of COVID-19 and support for tourism development. This study provides a new way of guiding COVID-19 policymaking and exploring effective policies in the context of tourism.

A Tutorial on How to Set Up a System Dynamics Simulation on the Example of Covid-19 Pandemic

The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling, which incorporates systems thinking to understand and map complex events as well as correlations, can aid in predicting future outcomes of the pandemic and generate key learnings. As system dynamic modeling allows for a deeper understanding of the manifestation and dynamics of disease, it was helpful when examining the implications of the pandemic on the supply chain of semiconductor companies. This tutorial describes how the system dynamics simulation model was constructed for the Covid-19 pandemic using AnyLogic Software. The model serves as a general foundation for further epidemiological simulations and system dynamics modeling. © 2022 IEEE.

Packaging plastic waste management in the cosmetic industry

PurposeThis paper aims to explore the relationship between the various variables present in the packaging plastic waste management system in the cosmetics industry.Design/methodology/approachIn this paper, the authors deal with plastic packaging waste in the cosmetic industry with the help of system dynamics. The model broadly divides the system into six sections – Cosmetic Packaging, Waste Generation, Waste Collected, Waste Sorted, Waste Treated and Waste Dumped. Businesses have been investing in each section depending on their progress and targets. The authors are looking at case studies of two leading cosmetic brands, L'Oréal and L'Occitane en Provence, to validate the industry practices against our model.FindingsFrom a business perspective, using the case study methodology for L'Oréal and L'Occitane, the authors inferred that out of the various investment vehicles available, companies are targeting technological advancement and third-party collaborations as they have the potential to offer the greatest visible change. However, most of these investments are going toward the treatment subsection. Still, there is a scope for improvement in the collection and sorting subsystems, increasing the efficiency of the whole chain.Originality/valueThere has been a lot of research on packaging plastic waste management in the past, but only a few of them focused on the cosmetic industry. This study aims to connect all the possible variables involved in the cosmetic industry's packaging plastic waste management system and provide a clear output variable for various businesses looking to manage their packaging waste because of their products efficiently.

A System Dynamics Model for Studying the Resiliency of Supply Chains and Informing Mitigation Policies for Responding to Disruptions

Economic shocks are unanticipated events that have widespread impact on an economy and can lead to supply chain disruptions that propagate from one region to another. The COVID-19 pandemic is a recent example. Simulations have been applied to study the impact of COVID-19 shocks on supply chains at the macro level using various approaches. This research has developed a hybrid System Dynamics and Input/Output simulation to model the economic impact of various types of supply chain disruptions. The hybrid model provides results that match historical performance of the U.S. economy under COVID-19 shocks and provides reasonable results when applied to investigate U.S. dependence on foreign trade. Its graphical nature also supports a decision support tool that will allow policymakers to explore the costs and benefits of various policy decisions designed to mitigate the impact of a broad set of potential supply chain disruptions. © 2022 IEEE.