## 2 State of the Art
### Solving Complex Decision Problems  
One difficulty in risk management is the different, sometimes conflicting, target values that need to be taken into account when making decisions. In the context of energy-oriented production planning, one example is that the capacity of stations should be utilized to fulfill the delivery time and that machine costs are reduced. On the other hand, the station utilization should be reduced in time windows with high electricity prices and be shifted to times with lower or negative prices. In the literature, there are different approaches for multi-criteria decision-making (MCDM). A large number of articles in this field distinguishes between work on the development of new MCDM methods and detailed descriptions of their functioning (including [PERSON] [23], [PERSON] and [PERSON] [24], and [PERSON] and [PERSON] [25]), and comparative work that analyzes known MCDM methods (including [PERSON] [26], [PERSON] et al. [27], and [PERSON] et al. [28]). [PERSON] et al. [29] give an initial overview of the relevant literature within the categories mentioned. [PERSON] et al. [29] emphasize the increasing importance of hybrid MCDM methods for solving complex decision problems. An MCDM problem is generally divided into the steps of weighting the criteria and prioritizing the alternatives. Some MCDM methods are suitable for both weighting and subsequent prioritization, while other MCDM methods require a weighting of the criteria and do not offer a methodical approach for this. For the selection of environmentally friendly technologies, [PERSON] and [PERSON] [30] combine the analytic hierarchy process (AHP) and multi-attribute utility (MAUT) to give decision-makers in construction projects a method for a comprehensive assessment of sustainability without neglecting the conventional goals of construction planning. The evaluation is based on four criteria tailored to the construction sector. [PERSON] [31] conducts a comparative study of an MCDM problem in the context of sustainable energy generation in Turkey. The author compares the results of 42 decision problems resulting from a combination of different MCDM and weighting methods. The individual results are then summarized in an overall rating. [PERSON] concludes that a combination of several MCDM methods can compensate for the disadvantages of individual methods and thus enable a more accurate selection of the best alternative. [PERSON] et al. [32] combine a weighting based on AHP and Shannon entropy with a technique for order of preference by similarity to ideal solution (TOPSIS) to find an optimal mining location. The enormous number of 260,400 alternatives should be emphasized here. [PERSON] et al. [33] compare the results of three different hybrid MCDM methods in the context of the planning of sewage treatment plants in consideration of the sustainability aspects. It is found that the technology selection is method-dependent. Consequently, they recommend not basing a decision on a single MCDM method but comparing the results of different methods. In addition, the authors consider both hard, easily quantifiable, and soft, only qualitative, decision criteria. [PERSON] and [PERSON] [34], among others, deal with the risks resulting from disruptions in the production process. The authors carry out a failure mode and effects analysis (FMEA) with three different risk priority numbers, which are weighted using an AHP. Identified faults are then prioritized with the help of TOPSIS. By combining the methods, risks are prioritized depending on the subjective assessments of the decision-maker. [PERSON] et al. [35] also place their method for risk assessment in the context of disruptions that endanger the information technology (IT) security of critical infrastructure. This is a holistic risk management method, which includes both risk identification and risk assessment. [PERSON] et al. [36] consider service risks in hospitals with the aim of increasing service quality.