Through simulation modeling, this research highlighted the interactions of key system parameters in a disruption phase under different scenarios. A multi-period, multi-echelon serial supply chain was studied with agent-based and discrete-event simulation.
The semiconductor industry is facing pressure to reduce its extensive energy consumption, which requires transparency on the relationship between energy efficiency and original planning objectives. This paper aims to develop an extension to the existing Operating Curve concept by investigating the effect of utilization on energy efficiency. It uses the results of discrete-event simulation on a fab level to verify the novel concept.
Survey-based research methodology is commonly used in various disciplines, ranging from social sciences to healthcare. However, it is difficult to provide real-world experience of survey sampling methodologies to students and novice researchers. In this paper, the researchers proposed the development of a virtual learning environment based on agent-based modeling to help learn about different aspects and challenges of survey-based research.
As widely reported in the literature, the leather luxury accessories industry is characterized by a highly fragmented supply chain. Frequent changes in the production mix had to be managed, often requiring the re-optimization or even re-design of production flows. The objective of this paper was to propose a data-driven simulation model for production balancing and optimization in this sector.
This study aimed to evaluate the integration of connected and autonomous vehicles (CAVs) into existing transportation networks, comprising highways and urban roads. To quantify their impact, agent-based simulation models were developed and validated.
Maritime traffic in winter in the Baltic Sea (particularly the northern part) is challenged by heavy ice formation. This work presented an integration of ice characteristics, operational-level details of ships, and system-level details such as traffic flows and icebreaker scheduling through a simulation framework.
Using simulation models for manufacturing facilities is a common approach for planning, optimizing, and testing different machine configurations and positioning before the actual construction. This paper presented a proof of concept for gradually migrating a master simulation model for shop floor layouts of machines into a product line of different simulation models to explore and find suitable solutions.
This paper presented a concept for simulation-based optimization of the system design of modular production systems using a classical Genetic Algorithm (GA) and the NSGA-II algorithm, which supported a good activity assignment to the individual manufacturing cells.
The job-shop scheduling problem was considered with sequence-dependent setup times and preventive maintenance constraints. A hybrid model combining discrete-event simulation and an optimization algorithm in Python was applied to simulate the production process and solve the job-shop problem.
An animated, data-driven and data-generated simulation model was developed to support the development of both the design and configuration methodology and operational control strategy of Shuttle-based Storage and Retrieval Systems.