In this paper we conducted thorough analysis of research papers focused on resource aware problems and using one of the following formulations: integer linear programming (ILP), greedy algorithms (GrA), dynamic programming (DP), evolutionary algorithms (EA) and machine learning (ML). Basing on such general problem formulations we identified actual research tasks considered in many different domains. Furthermore, we analyzed each of these problems in terms of: resources being considered/subject to optimization, specific optimization algorithms, if applicable, and domains. Finally, based on over 170 1 research papers, we assessed which particular resources like: time, cost, energy, human, computer, natural resources, data/information are used in which problems formulations, which formulations and resources are used and considered in which application/domains. It can serve as reference for algorithms in particular domains or, conversely, looking for unexplored approaches in specific contexts.