## 2 Manufacturing System Context
### Flow Shop  
In the flow shop context, scheduling is still a good strategy for energy saving and efficiency. Flow shop with specific characteristic are grouped in Sections 2.4.1 and 2.4.2.  
[PERSON] et al. (2019) provided a time-indexed integer programming with the aim of minimizing electricity cost and CO\({}_{2}\) emissions, without reducing productivity. The authors argued that shifting the greatest energy demands during off and mid peak hours leads to a reduction in electricity costs. However, CO\({}_{2}\) reduction does not always meet electricity cost reduction; the CO\({}_{2}\) emissions per kWh depend on the mix of power resources used and how they are combined during peak hours.  
The peak power consumption was evaluated as an important issue of the flow shop scheduling in ([PERSON] et al., 2019). The proposed approach considers the uncertain processing time of the operations and consists of first obtaining the initial schedule that allows minimizing the peak power consumption and the inventory cost. In a second phase, a rescheduling is provided to prevent the peak power due to the uncertainty of processing time, including idle times.  
Differing from the other works on the scheduling problem, [PERSON], [PERSON] et al. (2019) considered the minimization of energy consumption with the evaluation of the product quality. The machine speed influences the production time, energy consumption, and product quality. Quality control was considered at the end of the line and if the product quality does not satisfy the requirement, it is necessary to rework the piece with additional energy consumption and time. The authors developed a novel three-stage decomposition approach to address the scheduling problem.  
In ([PERSON] et al., 2019) the sum of energy consumption and tardiness was considered as the target of the optimization process. Fuzzy numbers were formulated to consider the uncertainty in the scheduling problem and a genetic algorithm was applied to solve it. In ([PERSON] et al., 2019), the fuzzy numbers were used to consider the uncertainty of processing time; in this paper, the minimization of the total weighted delivery penalty and of total energy consumption was defined as an objective function, and a multi-objective differential evolution algorithm was provided. The flow line was considered as a Bernoulli serial line with the aim of improving energy efficiency by scheduling machine shutdowns ([PERSON] et al., 2019).  
The problem of a single item capacitated lot-sizing in flow shop, considering energy aspects, was studied in ([PERSON] et al., 2019). In this paper, two mixed integer programming models were developed in order to minimize the total production cost consisting of electrical, holding, setup, and power demand costs. Extending this work to address the lot-sizing problem in the case of the medium and large scale, [PERSON] et al. (2019) proposed two heuristic methods based on movement techniques and [PERSON] et al. (2019) developed a fix-and-relax heuristic and a genetic algorithm. The multi-item capacitated sizing problem, considering energy aspects, was investigated in ([PERSON] et al., 2019).  
The blocking is typical in a flow shop system without buffers between machines or with limited and full intermediate buffers ([PERSON] et al., 2019). This blocking condition imposes the impossibility of leaving the upstream machine until the job on the next machine is completed.  
In the blocking flow shop context, a scheduling problem considering energy saving was proposed ([PERSON] et al., 2019). The authors considered the minimization of the makespan and of energy consumption during job blocks and machine idle time. A discrete multi-objective evolutionary optimization algorithm was used to address the problem.  
With the goal of the efficient use of switch-off policies, [PERSON] and [PERSON] (2019) developed a mathematical design model of production flow lines