Integration of production scheduling and energy-cost optimization using Mean Value Cross Decomposition

•Large-scale combined scheduling and energy procurement optimization problems are solved.•Energy-cost optimization and energy-aware production scheduling are functionally separated.•Different variants of Mean Value Cross Decomposition are investigated that yield near-optimal or optimal solutions.•Existing solutions for the scheduling and for the energy procurement problem that take into account problem-specific aspects can be integrated.•Energy-intense industries as e.g. steel and pulp and paper can reduce their energy cost while meeting the production demands. Integrated optimization of the procurement cost of electric energy with production planning is increasingly considered in various industries. The traditional approach in industry is production driven, i.e. the production is scheduled first, followed by the energy supply optimization to find the best available energy portfolio, which is usually sub-optimal. The combined scheduling and energy procurement optimization can be formulated as an integrated monolithic optimization model, resulting in intractable problems, even if solutions to the two isolated problems are available. We propose to use Mean Value Cross Decomposition for solving the combined problem by iterating between energy-aware production scheduling and energy-cost optimization, possibly building on existing solutions. We apply the approach to a pulping process and a steel production process. MILP-based models are employed for the two scheduling problems and for the energy cost optimization a Minimum-Cost Flow Network model is used, resulting in good quality solutions within reasonable computation times.