## 1 Introduction
This objective has been prevalent over the years during which JSP problems have been approached with metaheuristic algorithms, yet it has not been until recently when energy efficiency has become of utmost relevance in the industrial realm, in particular in energy-intensive industries. The growing importance of energy consumption as a key indicator of industrial performance and efficiency has permeated into practical planning processes. Consequently, the interests of the research community working on metaheuristics for JSP problems have progressively steered towards energy efficiency as another objective, giving rise to an upsurge of contributions dealing with energy-aware multi-objective JSP problems for industrial production scheduling. In light of this rising research trend, this survey aims to provide the readership of this journal with a comprehensive survey of multi-objective meta-heuristic algorithms applied to JSP problems where energy efficiency is considered among the objectives to be optimized. The literature study offered in our work intends to pose itself as a reference material for those interested in pursuing research in this area. To this end, we systematically reviewed and critically analyzed the most relevant features of both problem formulations and algorithms to solve them effectively. To do this, three taxonomies are proposed to arrange the contributions so far as per (1) the JSP problem to be solved (i.e., type and domain, number of objectives and secondary objectives), (2) practical constraints and considerations that affect the formulation of the problem and the feasibility of the solutions, and (3) the technique in use to solve the formulated problem (correspondingly, the adopted algorithm, main parameter settings, etc.). After organizing and describing the contributionsnoted in this area, we highlight good practices identified in the reviewed corpus of literature, a well as underscore research niches and poor practices that must be overcome to achieve actionable insights and approaches of practical utility. The manuscript also informs with empirical evidence the main findings of our bibliographic review. To this end, the survey includes an experimental study including representative multi-objective evolutionary solvers applied to a number of synthetic JSP test instances that have appeared in the literature [17]. These instances were augmented with energy costs added in order to be representative of the area surveyed in this study. The ultimate goal of this experimental study was to methodologically prescribe how to perform a comparison among multi-objective metaheuristic solvers for energy-aware JSP problems in a principled way, considering not only the quantitative significance of the performance differences found among the algorithms under comparison, but also pausing on the qualitative implications of such differences in practical settings. In order to promote reproducibility and transparency, the source code of the software generating the discussed results has been made public in a GitHub repository, together with the augmented JSP instances under consideration. The rest of this manuscript is structured as follows. Section 2 introduces the basic concepts about multi-objective optimization, metaheuristic optimization, and a generic description of the JSP problem. The literature collection methodology, the three proposed taxonomies, and the literature review on multi-objective JSP problems with energy as one of the objectives are provided in Section 3. This section is concluded with the summary of critical points mentioned above, flowing into the experimental setup and results obtained therefrom discussed in Section 4. Finally, conclusions are drawn and research directions are outlined in Section 5, motivating the readership towards investing further research efforts in this exciting research avenue.