Energy-aware production scheduling considering energy consumption in storage under time-of-use tariffs

In this paper, we investigate energy-aware production scheduling under Time-of-Use (TOU) tariffs, specifically for industries where inventory storage incurs significant energy costs. We develop a mixed-integer programming model that minimises total costs, including production energy costs, storage energy costs, and late-penalty costs. Given the model's complexity, we propose two algorithms: Multicost-Based Adaptive Genetic Algorithm (MBAGA) and Multicost-Based Adaptive Large Neighborhood Search (MBALNS). These algorithms integrate cost-based weighting mechanisms to efficiently balance trade-offs between different cost components. Numerical experiments demonstrate that, given a computational time constraint, our algorithms significantly outperform the exact method in efficiency and solution quality. Compared with three benchmark algorithms for scheduling under TOU tariffs, both MBAGA and MBALNS consistently achieve lower total costs and faster convergence. Sensitivity analyses with respect to key model and algorithmic parameters confirm the robustness of the proposed approaches. Our results highlight the importance of considering energy consumption during storage in scheduling models and offer a scalable and adaptable framework for optimising late-penalty cost and energy cost during production and storage.