This paper presents a framework that combines simulation with mathematical optimization to plan the supply of raw materials for producing specialty chemicals. A real-world use case was introduced to validate the developed framework, demonstrating the application of simulation in chemical industry logistics.
Efficient material replenishment in assembly plants can be optimized using autonomous mobile robots. This research analyzes the Tiger Motors assembly line at Auburn University, where a Stretch RE1 robot is integrated into the production process. A multimethod simulation model combining discrete-event and agent-based simulation modeling in AnyLogic evaluates different replenishment strategies. It identifies the most effective approach based on payload capacity, travel optimization, and real-time demand response.
Efficient production scheduling is crucial for maximizing resource utilization in eco-industrial parks, where factories share energy and materials. However, balancing energy distribution and job scheduling is challenging due to fluctuating power availability, production constraints, and inter-factory dependencies. This study uses a constraint programming model in AnyLogic combining agent-based and discrete-event simulation modeling to optimize production in an industrial park.
This article presents a discussion of manufacturing process optimization in the ready-made garment industry through making the management of production lines more effective using AnyLogic software. It shows how simulation of various scenarios can pinpoint effective strategies for maintaining efficiency despite changes in market demand.
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.
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.
This paper focuses on optimizing the production of a real-world pre-assembly facility in a high-volume and high-mix semiconductor wafer fab. In simulation software, the researchers built an in-depth, deterministic discrete-event model. With it, they tested release policies in two stages, using real production data and demands.
The overhead crane scheduling problem has been of interest to many researchers. While most approaches are optimization-based or use a combination of simulation and optimization, this research suggests a combination of dynamic simulation and reinforcement learning-based AI as a solution.
The goal of this steel plant simulation project was to minimize the crane waiting time at the LD converters by creating a better crane schedule.