Abstract

The scheduling of a job shop production system occurs using models to plan operations for a given period while minimizing the makespan. However, since the resulting mathematical models are computationally demanding, their implementation in the work environment is impractical, a difficulty that increases as the scale problem grows. An alternative approach is to address the problem in a decentralized manner, such that real-time product flow information feeds the control system to minimize the makespan dynamically. Under the decentralized approach, we use a holonic and multiagent systems to represent a product-driven job shop system that allows us to simulate real-world scenarios. However, the computational performance of such systems to control the process in real-time and for different problem scales is unclear. This paper presents a product-driven job shop system model that includes an evolutionary algorithm to minimize the makespan. A multiagent system simulates the model and produces comparative results for different problem scales with classical models. One hundred two job shop problem instances classified as small, medium, and large scale are evaluated. The results suggest that a product-driven system produces near-optimal solutions in short periods and improves its performance as the scale of the problem increases. Furthermore, the computational performance observed during the experimentation suggests that such a system can be embedded in a real-time control process.