Jul 2026· International Conference on Control, Decision and Information Technologies· pp. 2285-2290· 0 citations· 24 references
Abstract
Green manufacturing increasingly requires production systems to balance operational performance with energy efficiency and cost-effectiveness. This paper addresses an energy-aware flexible flow shop scheduling problem in which a factory operates under a time-of-use electricity tariff, participates in a demand response reward program, exploits on-site photovoltaic generation, battery energy storage, and applies an on/off machine strategy to reduce idle power consumption. Two objectives are optimized in lexicographic order: makespan as the primary criterion and net energy cost, electricity expenditure minus demand response rewards collected, as the secondary one. A Genetic Algorithm is proposed, combining an order-crossover permutation encoding, an earliest-finish-time decoder, and an energy management heuristic. Results show that the approach produces compact, energy-efficient schedules, while a sensitivity analysis highlights the strong impact of demand response incentives on reducing net energy costs without affecting production throughput.
Energy-aware scheduling has become a central challenge in modern manufacturing environments. As industries increasingly aim to reduce operational costs and carbon emissions, aligning production activities with electricity tariffs is increasingly important. The integration of battery energy storage system (BESS) further...
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