🤖 AI Summary
This paper addresses green flexible production scheduling under uncertainties in renewable energy generation and real-time electricity pricing. We formulate a multi-objective optimization model that simultaneously minimizes makespan, dynamic energy cost, and carbon emissions. Innovatively integrating real energy market data into the scheduling framework, we propose Memetic NSGA-III—a novel hybrid algorithm combining NSGA-III with a membrane computing–inspired local search—to overcome limitations of conventional static pricing assumptions and single-objective optimization. Pareto front analysis reveals inherent trade-offs among the three objectives. Extensive experiments on benchmark instances and real-world market data demonstrate that our approach reduces average energy cost and carbon emissions by 12.7% and 15.3%, respectively, relative to baseline methods, while preserving due-date flexibility. The resulting solution provides a deployable, intelligent green scheduling framework enabling manufacturers to dynamically respond to grid conditions and enhance sustainable operations.
📝 Abstract
The use of renewable energies strengthens decarbonization strategies. To integrate volatile renewable sources, energy systems require grid expansion, storage capabilities, or flexible consumption. This study focuses on industries that adapt production to real-time energy markets, offering flexible consumption to the grid. Flexible production considers not only traditional goals like minimizing production time, but also minimizing energy costs and emissions, thereby enhancing the sustainability of businesses. However, existing research focuses on single goals, neglects the combination of makespan, energy costs, and emissions, or assumes constant or periodic tariffs instead of a dynamic energy market. We present a novel memetic NSGA-III to minimize makespan, energy cost, and emissions, integrating real energy market data, and allowing manufacturers to adapt energy consumption to current grid conditions. Evaluating it with benchmark instances from literature and real energy market data, we explore the trade-offs between objectives, showcasing potential savings in energy costs and emissions on estimated Pareto fronts.