# Switch-Off Policies in Job Shop Controlled by Workload Control Concept
## 2 Literature Review  
Numerous works have been proposed in the literature on workload control and the switch-off in production lines. The following discussion of the literature concerns the proposed switch-off policies and applications in job-shop systems. The switch-off policies have mainly been proposed in flow line systems [7; 12]. These approaches work on three decision evaluations: supervising the upstream buffer level; supervising the downstream buffer level; and supervising the upstream and downstream buffer levels together. These approaches reduce the energy consumed in the idle state of the machines. The effectiveness of these models has also been tested in pull control systems of flow lines [8]. The development of mathematical models can support the introduction of switch-off policies, but this approach can increase computational complexity with the problem of applying these models in real industrial applications [13]. To reduce the computational complexity, a fuzzy controller that supports each machine that collects the real-time data to switch off/on the machines was proposed in the flow line [14; 15]. Ref. [16] studied the switch-off policy introduction in the design model of the flow line. This design model introduces a processing time distance for each couple of stations to facilitate the switch-off of the machines. The numerical results show how this approach leads to a significant reduction in energy consumption, limiting production loss. Few works have been developed in the field of job shop systems. The authors of [17] include the switch-off strategy using a mixed-integer linear programming model to reduce energy consumption in a flexible job-shop system. The model proposed cannot identify energy-efficient production schedules for real industrial applications. Ref. [18] studied the scheduling problem for flexible job shops considering the switch-off and speed processing time to save energy. A genetic algorithm is developed to solve the mathematical problem to optimize the makespan, the energy consumption, and the number of turning-on/off machines simultaneously. Ref. [19] proposed a mixed-integer programming mode with a genetic algorithm to optimize the makespan and reduce energy consumption. The numerical results show a potential energy consumption reduction, but the genetic algorithm can lead to an increase in the computational complexity for industrial cases. Ref. [9] proposed a model that combines the direct and indirect workload of the stations of a job shop. The numerical results highlight that it is possible to obtain a compromise between energy reduction consumption and production loss. Ref. [20] proposed a novel mathematical formulation that includes switching off the machines to reduce energy consumption in flexible job shop scheduling problems. They introduced a decomposition approach to allow the application of the proposed model for large-scale problems. Ref. [21] designed a fuzzy controller to switch off the machine while considering the upstream buffer level and the required production rate. The simulation experiments on a machine highlighted that a large amount of energy could be saved without affecting the throughput significantly. This approach could be extended to job shop systems with more than one machine. Ref. [22] discussed the resilience of integrated energy systems that can be affected by the shutdown of the machines of a complete manufacturing system. The authors [23,24] highlighted how the Internet of Things (IoT) technologies and cloud computing improve the monitoring and operations of energy management systems. The literature review analysis highlights the following limits:  
* Few works have introduced switch-off policies in job-shop manufacturing systems. The majority of papers developed mathematical models that can be difficult to apply in real industrial applications. * The introduction of switch-off policies in manufacturing systems controlled by a workload mechanism has not been studied in the literature.