## 1 Introduction  
In the field of operations research, project management remains a topic of intensive research from various angles such as scheduling and resource allocation. For both cases, there exist such well-known formulations as a resource-constrained project scheduling problem (RCPSP) and a resource leveling problem (RLP). The former aims to minimize the completion time of the set of jobs with given precedence relations and the set of limited required resources. The latter deals with resource allocation with the objective to minimize the cost of resource usage. Both problems were proved to be NP-hard (see [1] for RCPSP and [2] for the resource leveling problem). To respond to practical requirements, these basic models undergo numerous modifications. For example, the model that optimizes the distribution of man hours for workforce or throughput on a production line was presented by [PERSON] and [PERSON] [3]. In this paper, we focus on the resource leveling problem with several practical enhancements. However, we also refer to RCPSP modeling techniques and approaches that are used for the RLP and applicable to our case. The RLP was actively studied from both the theoretical and practical sides. In the paper of [PERSON] and [PERSON] [4] three basic objective function types from the existing literature were presented with properties and existing solution approaches. The first objective function type was described as a total amount of variations in resource utilization within the project duration. The second case arises when available resource utilization is exceeded; it is total (squared) overload cost. The authors also presented a total adjustment cost function, which is formed by costs arising from increasing and decreasing resource utilization. In all cases, the planning horizon was fixed with the reference that for mid-term planning usually there is a determined project deadline. There are some papers involving multi-criteria optimization techniques with attention to both project duration and resource utilization. However, RLP with a fixed deadline and the objective functions that were presented above still attracts attention: The majority of the following literature references are focused on this formulation. From the practical side, these RLP types are found in a wide range of industries, especially in construction. For these particular cases, researchers apply different heuristics to provide good solutions. An overview of existing RLP heuristic techniques was prepared by [PERSON] et al. [5]. We can point out recent results with references to industrial problems. For example, a meta-heuristic genetic algorithm was applied to RLP based on a construction project in the paper of [PERSON] and [PERSON] [6]. Simulated annealing was also tested in the construction area by [PERSON] et al. [7]. [PERSON] et al. [8] implemented a genetic algorithm for the RLP with uncertain activity durations and possible overlapping. Construction area and project management usually include discrete resources such as equipment, machines, materials or manpower. [PERSON] et al. [9] studied a tactical level of construction and large-scale engineering planning. To achieve robust tactical planning and resource allocation, they created a proactive approach providing lower variations in project costs in case of resource capacity uncertain data. The problem was denoted as rough cut capacity planning (RCCP), but it is also similar to the classical RLP with several practical changes. The same notation was also used by [PERSON] et al. [10]. There is another point that inspires us to study and develop RLP solution methods. Energy is the main continuous resource, and the energy management problem becomes more and more crucial for any industry. According to this, the scheduling of operations with attention to careful energy consumption remains actual. [PERSON] et al. [11] considered the industrial case of the energy scheduling problem for a pipe-manufacturing plant.