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

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.