Energy-Aware Coordination of Manufacturing Equipment in Flow Shop and Job Shop Production Environments with Stochastic Job Arrival

A manufacturing company's production-related decision-making is to a large extent characterized by machine scheduling and support device operations management. All these industrial equipment types consume energy, often in the form of electricity. This electricity is more and more provided by renewable energy sources such as wind and solar power. The volatility of these power sources can lead to peak periods where feed-in management is required to stabilize a power grid. In this paper, we suggest to increase local industrial energy consumption in such periods to relieve the power grid. For this, we use models that are capable of synchronizing machine scheduling activities and support device charging operations with the availability of renewable energy. We then use a decentralized decision-making platform to coordinate the decision-making of various types of production-related equipment. By integrating a forecast for the occurrence of excessive renewable energy into this coordination platform, an opportunity is given to support environmentally oriented production decisions that avoid feed-in management actions in the power grid that surrounds the company. In a simulation study based on real-world data, we compare flow shop and job shop production environments, both under stochastically arriving jobs in such an energy-oriented setting. We furthermore introduce and examine the impact of machine-specific due date adjustment methods to achieve high processing rates and low job tardiness next to the energy-related goal. The presented approach is computationally analyzed with respect to the trade-offs of these conflicting goals in both types of production environments