Sustainability assessment is a multi-faceted, dynamic, and complex paradigm in the context of buildings with several social, economic, and environmental interactions. Currently, the building sector lacks a sustainability evaluation and benchmarking mechanism. So, a system-thinking approach that can solve these challenges due to its ability to evaluate complex systems was developed.
The Covid-19 virus has substantially transformed many aspects of life, impacted industries, and revolutionized supply chains all over the world. System dynamics modeling can aid in predicting future outcomes of the pandemic and generate key learnings. 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.
In this paper, researchers introduce two case studies that show how simulation and optimization help in overcoming many of the challenges associated with data science techniques. The methodology is easily extended to a wide range of industries and use cases and enables an organization to improve its decision-making and generate business value.
Based on an authentic model of system dynamics, the authors propose a new approach to simulation modeling of the dissemination of electric vehicles in a given region. The proposed system dynamics model made it possible to identify measures required to stimulate EV demand in a region with a low level of electric transport development.
The storage of electrical energy is becoming increasingly important to satisfy the demand through renewable energy sources. In this paper, a continuous and discrete simulation of a pumped thermal energy storage (PTES) system are compared with respect to their computational time and accuracy.
The COVID-19 pandemic is an unprecedented public health and economic crisis, that dramatically impacted different industries, and presented an unforeseen challenge to the automotive industry and its supply chain. Researchers modeled a system dynamics simulation to demonstrate the behavior of a multi-echelon supply chain responding to different end market scenarios.
The Regional Biorefinery Impact Assessment (REBIA) simulation tool allows easy impact assessment for waste stream sources and biorefinery locations. REBIA is based on a supply chain model, where waste is transported via the road network from its various sources to the biorefinery.
Due to numerous uncertainties such as bad weather conditions, frequent changes in the schedules of vessels, breakdowns of equipment, port managers are aiming at providing adaptive and flexible strategic planning of their facilities, especially intermodal terminals (dry ports).
This research shows that the combination of the agent-based modeling with other simulation approaches simplifies the process of designing simulation models and increases their visibility. The developed set of models allows the researchers to compute the balanced values of the parameters. Consequently, it helps achieve effective operation of a seaport – intermodal terminal system. The provided case study on one of the busiest ports in China proves the adequacy and validity of the developed simulation models.
Strategic management simulation is an increasing field of practice mostly driven by the big consulting firms. While System Dynamics (SD) is a widely used simulation method in given its advantage on global aggregates and deterministic model, hybrid modelling can achieve similar popularity. This paper presents some suggestions on how to use hybrid modelling with AnyLogic strategic management software.
This paper focuses on the influence of different order placement behavior of users on the allocation of common resources inside a multi-user warehouse. Furthermore, the interdependencies between one user’s resource usage on other users’ order processing time is investigated. For this objective, an agent-based simulation model has been developed, depicting a rectangular warehouse with two users and one order picker. Results show that different order placement behavior and resource usage of one user have a strong influence on order processing times of other users. Furthermore, by simulating uneven order placement by one user, it can be shown that peaks in order demand influence other user’s order processing times with a delay of up to two hours after the peak occurred. Thus, the results highlight the need for coordinated order placement of partners inside a multi-user warehouse.