Multi-objective Energy-efficient Flexible Flow Shop Scheduling Problem : Integrating Renewable Energy and Demand Response Programs

This study tackles an energy-efficient flexible flow shop scheduling problem motivated
by the increasing need for sustainable manufacturing under volatile energy
markets. The proposed framework jointly integrates photovoltaic generation, energy
storage systems, Time-of-Use pricing, and demand bidding mechanisms, while
explicitly accounting for machine On/Off strategies with realistic turn-on time constraints.
A bi-objective mixed-integer linear programming model is first solved using
the augmented ϵ-constraint method, simultaneously minimizing makespan and
total electricity cost minus demand-response incentives for small-scale instances.
For larger problems, a constructive heuristic and a Non-dominated Sorting Genetic
Algorithm II are proposed and compared with two state-of-the-art metaheuristics
from the literature. Computational experiments demonstrate the efficiency of the
proposed approach in generating well-distributed Pareto fronts, revealing valuable
trade-offs between production performance and energy expenditure. A case study
and a sensitivity analysis are conducted to evaluate the practical relevance of the
framework, examining the effects of key energy pricing and infrastructure parameters
on scheduling decisions. The results provide actionable insights for manufacturers,
showing how integrating renewable energy and demand response programs
can reduce electricity costs without compromising production efficiency, thereby
contributing to the broader transition toward low-carbon and cost-aware factories