Energy-Aware Scheduling in a Flexible Flow Shop Using a Hybrid Constraint Programming Approach

This paper addresses energy-efficient production scheduling in a flexible flow shop by integrating advanced energy
flexibility strategies. A smart manufacturing setting is considered, with energy supplied from multiple sources: grid
electricity with time-of-use pricing, photovoltaic generation, and an energy storage system. In addition, a demand
bidding mechanism allows the facility to submit curtailment capacity bids during periods of power system stress,
reducing electricity load in exchange for financial incentives. Two exact optimization approaches are investigated: a
mixed integer linear programming model and a hybrid method integrating constraint programming. Computational
experiments are conducted on a dedicated benchmark and compared with genetic algorithm, tabu search, and ant
colony optimization approaches. Results show that the hybrid method consistently delivers high-quality solutions,
achieving the best relative gap below 1% for all instance sizes, while metaheuristics remain competitive in terms
of computation time with small relative gaps.