The switch-off approach is one of the practices that supports sustainable manufacturing systems by reducing energy consumption. Manufacturing systems are characterized by two sources of fluctuations: workload due to customer demand and energy price variability. This work proposes a switch-off policy that addresses these fluctuations in job-shop systems using multi-agent architecture. The proposed approach is tested through simulation models and compared to benchmarks in the literature as well as scenarios without a switch-off policy. The performance indices evaluated include throughput, average time in the system, standard deviation of average time in the system, work in process, average maximum items in queues, total waiting time for machines, and total energy costs under different scenarios. The numerical results highlight that the switch-off policy, when considering energy prices and a combination of direct and indirect workload, achieves significant energy savings with minimal degradation of manufacturing performance. These results are particularly relevant under dynamic working conditions of the manufacturing system.

The research discusses in this paper concerns the improvement allocation policies to reduce the process time in job-shop manufacturing systems. The policies proposed are based on the evaluation of the workload control of the entire manufacturing system. Three policies are proposed: centralized, distributed and proportional. A simulation model is used to test the proposed policies under different conditions as: static and dynamic demand; introduction of machine breakdowns; different level of average manufacturing system utilization. The performance measures are compared to a manufacturing system without policies. The simulation results show that the improvement allocation allows to improve the performance with limited investment (average reduction of process time needed) and how the machine breakdowns and demand changes lead to different better policy. The decision maker can use these results to decide the better policy to use.