## 5 Discussion  
The planning horizon defined for this study was 12 months. [PERSON] et al. [41] stated that it is usually performed from 3 to 18 months. [PERSON] et al. [42], and [PERSON] et al. [19] also planned with a horizon of 12 months. On the other hand, [PERSON] et al. [43] used a horizon of only five days and, [PERSON] and [PERSON] [44] of 7 days and three weeks. The formulated model addressed the demand monitoring strategy, using overtime, hiring, and firing. The strategy depends on the reality of the place, as is the case of studies that considered the stock (i.e., [27; 28; 29; 30; 31]), subcontracting [25; 29; 32], and missing orders [21]. This work is similar to that of [PERSON] et al. [21] regarding aggregate planning since it determined the number of items to be produced in regular and overtime and the need to keep the workforce constant or not, as well as [PERSON] et al. [43] and [PERSON] [44]. They also determined a regular production plan. In contrast, [PERSON] et al. [19] used APP also to obtain the production lead time. This presented model was formulated with linear programming, following [PERSON] and [PERSON] [45], who stated that APP models are generally designed as linear programming that minimizes costs. The model formulated in this study sought to minimize production costs as demand was met. Therefore, it is possible to affirm that, in the literature discussed here, the planning models usually seek to minimize costs through strategies according to the reality of the study site. For example, [PERSON] et al. [42] considered inventory levels, production rates, overtime, regular labor, and subcontracting. On the other hand, [PERSON] et al. [18] used minimum inventory costs, ideal production rate, and production balance load rate, seeking minimization. [PERSON] et al. [43] focused on minimizing inventory, operating, batch recipe preparation, unit usage, family moving, and external production costs. On the other hand, [PERSON] and [PERSON] [44] sought to maximize their production, as did [PERSON] and [PERSON] [46], who also aimed to maximize profit while determining the amount and type of product that should be produced. In this way, these authors go in the opposite direction to [PERSON] [15], who claimed that the problems of any of the planning models can only be solved if they have the sole objective of minimizing the cost. A similar work to this study is that of [PERSON] and [PERSON] [21], which included energy cost and carbon emission in the model. The authors used three objective functions seeking to minimize such costs simultaneously. The main point was to find the ideal power demand value before including it in the aggregate planning. This contributes to the literature since even some studies considering electric energy in their costs do not consider the demand for power and, consequently, did not seek to reduce it before adding it. [PERSON] et al. [20] reported that aggregate planning costs involve inventory, ordering, and production costs, and yet, [PERSON] [47] complemented with unit costs related to regular hours, overtime, subcontracting, layoffs, or other factors that affect the costs in a relevant way, that is, it does not consider the cost of electricity. [PERSON] and [PERSON] [1] minimized energy consumption while maximizing production based on a multiobjective function. [PERSON] et al. [48] sought to reduce the energy used by production equipment based on mathematical programming, similar to the study by [PERSON] et al. [49], who reduced energy use in production by minimizing the final energy demand of the machines involved in the process. Finally, [PERSON] et al. [50] and [PERSON] [31] aimed to reduce energy in the process facilities and shared resources of aggregate planning. In contrast to these studies, this paper sought to include the power demand in the planning, considering minimizing its cost from an equation before adding it to the other existing ones.