###### Abstract
The approach thus leaves a need for further development with regard to the selection of a suitable path and the classification and integration of the measures it contains. In the literature, a large number of methods exist for the selection of alternatives, but there is no adaption to energy-oriented PPC. Furthermore, the state of the art offers no subdivision of energy flexibility measures into preventive and reactive, which is necessary for forward looking direct adjustments to the plan and alternative strategies if faults occur. It is assumed that this should also follow a specific and structured approach in order to reduce the effort required for risk treatment and thus increase the acceptance of its implementation in companies. This article presents an approach that responds to these requirements. It starts with the paths shown in Figure 1 and presents how a preferred path can be selected and how the measures contained can be integrated into the production plan. In the following, Section 2 first presents the state of the art in the relevant areas and the resulting need for action. Section 3 then introduces the scientific concept of the approach, which is described in detail in Section 4. The application based on a use case in a foundry is depicted and discussed in Section 5. The article closes with a conclusion and outlook in Section 6. The key notations are listed in Table 1. \begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Description** \\ \hline \(A\) & Weighting decision matrix \\ \(a\) & Elements of the weighting decision matrix \\ \(\Delta C_{tot}{}^{M}\) & Change in measure-induced total costs \\ \(\Delta C_{tot}{}^{R}\) & Change in risk-induced total costs \\ \(\Delta C_{tot}\) & Change in total costs \\ \(conts_{t}\) & Energy consumption in time unit \(t\) \\ \(D\) & Decision matrix \\ \(duration_{bs}\) & Duration of job \(k\) on station \(s\) \\ \(e\) & Entropy \\ \(EF\) & Energy flexibility \\ \(EF_{states}\) & Energy flexibility states \\ \(EF_{t}\) & Energy flexibility time \\ \(EF_{ech}\) & Energy flexibility technologies (e.g., storage facilities) \\ \(end_{bs}\) & Due date of job \(k\) on station \(s\) \\ \(i\) & Alternative \\ \(j\) & Criteria \\ \(k\) & Job \\ \(p\) & Probability of occurrence of risks and measures \\ \(P_{risk}\) & Probability of occurrence of risks \\ \(peak_{max}\) & Maximum peak load \\ \(\Delta pow_{t}\) & Deviation of the actual energy consumption from the forecasted \\ \(Power_{bs}\) & Power consumption of job \(k\) on station \(s\) \\ \(Power_{t}^{planned}\) & Planned power consumption in time period \(t\) \\ \(QDC\) & Quantity deviation cost \\ \(r_{ij}\) & Rating for alternative \(i\) with criterion \(j\) \\ \(s\) & Station \\ \(start_{bs}\) & Start time of job \(k\) on station \(s\) \\ \(t\) & Time unit \\ \(u_{ij}\) & Marginal utility score for alternative \(i\) with criterion \(j\) \\ \(U_{i}\) & Final utility score for alternative \(i\) \\ \(\overset{\rightarrow}{\triangledown}\) & Eigenvector \\ \(w\) & Weighting \\ \(w_{AHP}\) & Subjective criteria weighting by analytic hierarchy process (AHP) \\ \(w_{Entrj}\) & Objective criteria weighting by entropy \\ \(w_{j}^{final}\) & Final weighting for a criterion \(j\) \\ \(\lambda_{max}\) & Largest eigenvalue \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notations.