## 3 Literature Survey and Critical Analysis
### Taxonomies and Literature Analysis
#### 3.2.1 Taxonomy 1: Problem Features  
From the point of view of the features of the problem, four taxonomic features of interest were considered: (1) number of objectives; (2) energy objective; (3) secondary objectives; (4) the type of JSP formulation. The classification of the works according to these taxonomy criteria [36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94] is shown in Figure 3.  
We started our analysis of the literature through this first taxonomy by analyzing the first of the problem features: the number of objectives. This feature is about the formulation of the optimization problem in terms of the number of objectives. The higher this number is, the more complex the problem can be thought of to be, as the search space grows (the curse of dimensionality) and decision-making becomes harder to handcraft in practice. We can observe in Figure 3 that 75.44% of the reviewed references considered two objectives, more than 22% had the objectives, and only one paper focused on a problem with more than three objectives. As will be later discussed, this predominance of references with two or three references aligns with the characteristics of real-world problems, in which we rarely find JSPs that are governed by more than three conflicting objectives. We now turn the focus of the analysis to the specific energy objective defined in the formulated JSP. Indeed, the generic objective of reducing energy consumption can be defined by means of different indicators. We considered four criteria: energy consumption, electric power costs, non-processing energy, and idle energy. Based on this criteria, we found that 81.97% of the analyzed works were based on the consumption point of view, expressed in kWh. By contrast, 11.48% of the contributions referred to the cost of energy consumption, which does not necessarily have to be the same as consumption due to the different energy policies applied in each region, such as, for example, hourly discrimination. In this sense, an algorithm could find, for example, that it is better to produce at night at high rates, consequently increasing the energy consumption, but with a low impact on the energy cost, as opposed to producing at lower speeds in the evening (higher consumption, lower cost). The consumption of energy not used in production and the energy consumed in machine waiting times were taken into account in 4.92% and 1.6% of the reviewed literature works, respectively. Therefore, less than 10% of the works under analysis studied the idle energy consumption when the machine is on standby. As the reduction of the makespan is directly affected by the waiting times, the consumption of machines during waiting times can be of great importance, e.g., in a steel or smelting furnace, where energy consumption during the standby of production assets is critical. Ignoring this part of the behavior can be a great waste of energy. Since the survey addressed works dealing with multi-objective formulations of the JSP where energy consumption is minimized jointly with other secondary objectives, we continued our examination of the first taxonomy by evaluating which other objectives not related to energy have been in use. The secondary objective that appeared most frequently was, as expected (as the research has been performed in industrial environments), makespan, with 70.5% of the reviewed articles. This objective gauges the total amount of time that is necessary to produce and complete a set of scheduled jobs (production commit). The lower the makespan is, the more production efficient or the higher the  
Figure 3: Taxonomy of the reviewed literature by problem features. production capacity of a plant is. Therefore, makespan is an objective to be minimized.