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The paper deals with a flow shop scheduling problem, where energy costs are to be minimized with respect to time-varying electricity prices coming from the day-ahead market EPEX Spot Germany/Austria. To make efficient use of the corresponding price volatility in the production planning phase, i. e., weeks prior to the start of production, price forecasts have to be incorporated into decision making. Since the suitability of mid-term electricity price forecasts for scheduling applications has not been clarified, we analyze forecasts from the literature for their usability by means of extensive scenarios as well as an appropriate evaluation scheme. Furthermore, solution methods are provided for the considered flow shop scheduling problem. We study a Fix-and-Optimize and a priority rule-based sampling procedure in order to solve large-scale instances within a reasonable time. The performance analysis shows that Fix-and-Optimize achieves very good results in terms of solution quality. If production planners have to make quick decisions, the sampling procedure provides excellent run times.

The substantial amount of energy consumed through industrial production has given rise to a large number of research papers that incorporate environmental aspects and increased energy-awareness into production management. Nowadays, initiatives that push sustainable energy sources like wind and solar power together with new technologies for on-site power generation and energy storage open up a multitude of new options for making industrial energy consumption more environmental friendly. With this paper, we review the recent literature that attempts to reflect these options within industrial decision support models. We conduct a systematic literature review that identifies relevant papers from fields like machine scheduling, lot sizing, and other energy-intensive processes. For classifying the literature, a multi-dimensional scheme is developed that helps identifying areas that already received substantial scientific interest and areas that are not yet well understood. To this end, we actively address areas of future research that were mentioned in earlier review papers and show to what extent these gaps where closed by recent publications. Through this, we observe substantial progress with regard to various aspects such as pushing system boundaries, coming up with advanced conceptual approaches, and striving for more practical relevance. Nevertheless, there remains a substantial number of issues that have not yet been approached thoroughly.

The fact that energy prices on spot markets are volatile opens up economic potentials for industrial companies, as they consider them in production planning processes. In practice, this means that energy costs can be saved by shifting jobs to time intervals of low energy prices. This contribution addresses the effects of electricity price-oriented scheduling for planning periods of one week. We study a flow shop scheduling environment with up to three machines and energy-intensive jobs. The problem requires the information of electricity prices before they will be emerged on the market. Therefore, we analyze the suitability of electricity price forecasts in scheduling models. Moreover, we compare the potentials of electricity price-oriented scheduling to makespan minimization.

One month-ahead electricity prices open up a great potential for manufacturing companies that aim at reducing energy costs. On the machine level, an energy cost-oriented scheduling can be realized through determining the least electricity cost-intensive launch times for job processing during a day. To obtain reliable price information at production planning stage in advance, well-performing forecast models are required. In this paper, we apply a Weighted Nearest Neighbor (WNN) approach, a TBATS approach, and a Deep Feedforward Neural Network (DFNN) approach to forecast EPEX Spot day-ahead electricity prices of the European Energy Exchange in Germany/Austria, while accounting for a forecast horizon of up to one month. Model performances are compared by means of established error measures as well as by adjusted error measures to better interpret results from a production planner's point of view. For evaluation purposes, 1357 different forecasts were computed for each of the methods, allowing valid conclusions as to their performance. The results show that all approaches lead to acceptable accuracies. However, the DFNN outperforms WNN and TBATS, providing well-fitting forecasts even 29 days ahead.