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.

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.

The occurrence of natural or man-made emergencies can be quite complex and demand flawless preparedness, through tested strategies, in order to ensure the safety of the individuals. For large-scale infrastructures, whether commercial or residential, a reliable evacuation strategy is crucial. In this paper, the researchers propose a Crowd Evacuation Simulation and Analysis framework for the formulation and evaluation of effective evacuation strategies in large buildings, using real-scale building structures and agent based approach.

The goal of this study is to contribute to commercialization of the second-generation cellulosic biofuels (SGCBs) by reducing its operational cost. A hybrid simulation-based optimization approach is devised to design a cost-effective SGCB supply chain model. The approach includes feedstock yield estimation and location-allocation of feedstock storages between farms and refineries. An agent-based simulation (ABS) implemented in AnyLogic is utilized to estimate operational cost of a SGCB supply chain. The simulation-based optimization with adaptive replication (AR) is devised to find an appropriate SGCB network design in terms of operational cost. The approach is applied to a SGCB transportation network design problem in Southern Great Plains of U.S.

This paper addresses the problem of controlling the Work-In-Process (WIP) in semiconductor manufacturing by using a global scheduling approach and manufacturing scheduling software. A WIP balancing strategy is proposed to minimize the product mix variability in terms of throughput and cycle time. This strategy is enforced using a global scheduling optimization model which is formulated as a linear programming model. The global scheduling model is coupled with a generic multi-method simulation model built with manufacturing scheduling software for evaluation purpose.

The ever-increasing demand for fresh and healthy products initiated an urgency for transportation system analysis and effective planning for Agri-Fresh Produce Supply Chains (AFPSC). However, AFPSC faces many challenges, including product vulnerability to market disruption and limited shelf-life. In case of a no-deal Brexit (i.e., the UK leaving the EU without an agreement), trade between Ireland and the UK will most probably be subjected to customs control. In effect, transportation delays and products deterioration rates will increase.

Based on interviews with an Irish AFPSC forwarder, a simulation model was developed to investigate different systems’ dynamics and operating rules under different delay patterns on the (yet non-existent) inner-Irish border.

This study was designed to evaluate innovative last-mile delivery concepts involving autonomous parcel robots with simulation and optimization. In the proposed concept, the last mile of parcel delivery is split into a two-tiered system, where parcels are first transported to a transfer point by conventional trucks and then delivered with parcel robots on customer demand.

The purpose of this publication is to compare different time slot selection options for customers, namely due window and on demand selection, in the context of city logistics measures such as access regulations and driving bans for city centers. The researchers use AnyLogic as delivery optimization software. They build an agent-based simulation model, including a Geographic Information System environment and optimization algorithms for allocation and scheduling of delivery robots.

A common challenge in field service planning is to evaluate different design choices, related to staffing decisions, technician scheduling strategies, and technological improvements in order to make the system more efficient. This work provides a simulation-based optimization tool to support decision makers in tackling this challenging problem. The proposed framework relies on an optimization engine for the generation of the daily plans.

The study uses AnyLogic as field service planning software to evaluate the applicability of such plans by taking into account the stochastic factors. Furthermore, an interface manages the communication between these two components and allows a feedback loop between the simulator and the optimizer to achieve more robust plans. The applicability of the framework is demonstrated through a business case to evaluate different staffing decisions.