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.
This article introduces an innovative approach of risk and opportunity management to help managers in their decision-making processes. The proposed “physics of decision” approach enables managers to deal with the considered system’s performance trajectory by viewing and assessing the impact of potentialities (risks and opportunities).
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.
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.
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.
According to a World Health Organization (WHO) report in 2018, 1.35 million people die each year due to road accidents globally. In a country like India, it is becoming increasingly difficult to provide post-accident services on time with an increase in congestion. In this paper, the researchers propose a system which decreases the post-accident response time of Emergency Medical Services (EMS) in India by adding another layer of the patient transport vehicle. Based on a transportation system analysis, the paper discusses a new algorithm and a system design with a transportation optimization simulation model.
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.
The ever-increasing importance of airport retail has encouraged both industry and academics to look into ways to increase airport retail revenue. Despite the growing interest in this topic, there is a lack of passenger shopping behavioral model. This paper aims to fill this gap and enhance our understanding of how the location of the shop affects passenger decision. This paper first investigates the possible passenger shopping behavioral model through an exploratory Eye-tracking exercise. Data was collected to calibrate and validate the behavioral model through the use of an Agent-Based Simulation Model.
Aircraft ground handling involves all services to an aircraft (e.g. passenger boarding/disembarking, re-fuelling, deicing) between its arrival and immediately following departure. The aircraft, parked at its stand, witnesses a number of service providers move around it to perform their duties. Inter-dependencies among service providers abound, and knock-on effects at disrupted times are rife. Coordination from the side of the airport operator is difficult.
The research team proposes a tactical robust scheme by which ground handlers and the airport operator cooperate, although indirectly, in the development of plans for the next day that are less likely to be impacted by at least the more frequent operational disruptions. The scheme is based on a simheuristic approach which integrates ad-hoc heuristics with a hybrid simulation model (agent-based/discrete-event).
Simulation modelling has long been used as a decision support tool in the mining industry. This is typically done to address issues on the strategic time horizon, with a heavy focus on experimentation and sensitivity analysis. These issues include mining equipment selection, pit optimization, design and operation of the mine-plant interface, testing the robustness of a mine plan and blending.
Mining simulators can be used to forecast production in the short term to test the quality of truck dispatch decisions (allocation of trucks to loaders) and evaluate the value of alternate scheduling rules. It can also be used to produce a forecast of the likelihood of achieving a shift target and allow operators to test what-if options to reduce the risk of production loss or reduce costs by putting excess equipment on standby. Being able to make these decisions with confidence helps to drive improvements in operations efficiency.