Energy-Aware Flexible Flow Shop Scheduling with Solar PV, Battery Storage, and Demand Response: A Genetic Algorithm Approach

Green manufacturing increasingly requires pro-
duction systems to balance operational performance with en-
ergy 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 ex-
penditure 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.