Academic articles

Operations and maintenance (O&M) expenses can vary greatly from one energy solution to another. While a solar farm or geothermal system may need minimal ongoing maintenance, wind turbines require a skilled crew to keep them operating efficiently.

In this research, the authors use a scaled-down wind farm case study to demonstrate the potential of Reinforcement Learning (RL) in identifying an optimal O&M policy and to show the ease of use of AnyLogic simulation software and Pathmind reinforcement learning tool.

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.

Forest equipment planning and availability depend on forest management and harvesting regimes in addition to the market demand. This project aims to support the equipment planning process by estimating the future need of forest equipment with different forest management options. The number of required machinery depends on how much feedstock is available. It also depends on how much biomass was processed by previous machines in the system. The number of products that the machine in question has to process varies based on the supply chain structure.

An incisional hernia (IH) is a ventral hernia that develops after surgical trauma to the abdominal wall, a laparotomy. IH repair is a common surgery that can generate chronic pain, decreased quality of life, and significant healthcare costs caused by hospital readmissions. The goal of this study is to analyze the clinical pathway of patients having an IH using a medico-administrative database and predictive modeling. Predictive modeling in healthcare is used, among other things, to understand the times of occurrence of complications and associated costs. It enables the simulation of what-if scenarios to propose an improved care pathway for patients who are the most exposed.

Due to various production and market factors, flexibility is a key point in semiconductor manufacturing supply chain design. However, the increased complexity associated with this flexibility must be effectively managed to leverage the benefits that flexibility provides. The product structure is one of the main factors for enabling the desired result. Product structure representations in the supply chain design include linear, tree, and network. In this paper, the researchers explain the problem by a real case merger where risk and opportunities based on the choice of product structure representation in the supply chain design were relevant and no final solution initially was determined.

Elevator traffic affects people who use elevators in high-rise buildings. This happens because elevators transport a number of passengers above its planned capacity. The next set of passengers still needs to wait approximately four minutes before they are serviced even if the elevators implement a static zoning division to reduce waiting time during peak hours. Therefore, there is a need to improve the current elevator system. And to better understand how the system works along with its pitfalls, the environment, and the passenger flow will be simulated using agent-based modeling. The simulation will be modeled using data gathered from ID scans and CCTV footage.

The researchers developed an agent-based simulation model of a social process, the Integrated Disability Evaluation System (IDES), that replicates every step of the system and simulates the associated human actions. Analysis of the model outputs shows that the performance metrics of individual agents in the social process simulation are similar to their real-world counterparts. The success of this agent-based social process simulation model allows for increased confidence in the predictive accuracy of what-if analysis conducted on human processes. In addition, process changes may be modeled to inform policy recommendations.

Multi-resolution simulation models of a manufacturing system, such as a virtual factory, coupled with simulation-based analytics offer exciting opportunities to manufacturers to exploit the increasing availability of data from their corresponding real factory at different hierarchical levels. A virtual factory simulation model can be maintained as a live representation of the real factory and used to highly accelerate learning from data using simulation-based analytics applications.

This paper proposes a shared infrastructure for a virtual factory simulation-based analytics that can be employed by small and medium enterprises.

Production planning is usually performed based on customer orders or demand forecasts. The demand forecasts in production systems can either be generated by manufacturing companies themselves, i.e. forecast prediction or they can be provided by customers. For both alternatives, forecast prediction, as well as the customer-provided forecasts, the quality of those forecasts is critical for success. In this paper, predictive analytics simulation modeling is used to generate forecast data that mimic different forecast behaviors.

In many industrial manufacturing companies, energy has become a major cost factor. Energy aspects are included in the decision-making system of production line planning and control to reduce manufacturing costs. For this priority, the simulation of production line processes requires not only the consideration of logistical and technical production factors but also the integration of time-dependent energy flows. A hybrid (multimethod) simulation shows the complex interactions between material flow and energy usage in production close to reality. This paper presents a simulation approach combining System Dynamics, Discrete-Event and Agent-Based Simulation for energy efficiency analysis in production, considering the energy consumption in the context of planning and scheduling operations.