Academic articles

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.

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.

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.

Fashion is one of the world’s most important industries, driving a significant part of the global economy representing, if it was a country, the seventh-largest GDP in the world in terms of market size. Focusing on the footwear industry, assembly line balancing and sequencing represents one of the most significant challenges fashion companies have to face.

This paper presents the results of a simulation-based optimization framework implementation in the fashion industry. It also highlights the benefits of the use of simulation together with a finite capacity scheduling optimization model. Production planning and scheduling optimization include a scenario analysis that compares production KPIs (in terms of average advance, delay, and resource saturation) related to different scenarios.

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.