An energy-efficient bi-objective no-wait permutation flowshop scheduling problem to minimize total tardiness and total energy consumption

In manufacturing scheduling, sustainability concerns that raise from the service-oriented performance criteria have seldom been studied in the literature. This study aims to fill this gap in the literature by integrating the different energy consumption levels at the operational level. Since energy-efficient scheduling ideas have recently been increasing its popularity in industry due to the need for sustainable production, this study will be a good resource for future energy-efficient scheduling problems. Energy consumption in high volume manufacturing is a significant cost item in most industries. Potential energy saving mechanisms are needed to be integrated into manufacturing facilities for cost minimization at the operational level. A leading energy-saving mechanism in manufacturing is to be able to adapt/change the machine speed levels which exactly determines the energy consumption of the machines. Hence, in this study, the afore-mentioned framework is applied to the no-wait permutation flowshop scheduling problem (NWPFSP) which is a variant of classical permutation flowshop scheduling problems. However, it has various critical applications in industries such as chemical, pharmaceutical, food-processing, etc. This study proposes both mixed-integer linear programming (MILP) and constraint programming (CP) model formulations for the energy-efficient bi-objective no-wait permutation flowshop scheduling problems (NWPFSPs) considering the total tardiness and the total energy consumption minimization simultaneously. This problem treats total energy consumption as a second objective. Thus, the trade-off between the total tardiness - a service level measurement indicator - and the total energy consumption - a sustainability level indicator - is analyzed in this study. Furthermore, due to the NP-hardness nature of the first objective of the problem, a novel multi-objective discrete artificial bee colony algorithm (MO-DABC), a traditional multi-objective genetic algorithm (MO-GA) and a variant of multi-objective genetic algorithm with a local search (MO-GALS) are proposed for the bi-objective no-wait permutation flowshop scheduling problem. Besides the proposed algorithms are compared with the multi-objective energy-efficient algorithms from the literature. Consequently, a comprehensive comparative metaheuristic analysis is carried out. The computational results indicate that the proposed MO-DABC algorithm outperforms MILP, CP, MO-GA, MO-GALS, and algorithms from the literature in terms of both cardinality and quality of the solutions. The powerful results of this study show that the proposed models and algorithms can be adapted to other energy-efficient scheduling problems such as no-idle flowshop, blocking flowshop and job-shop scheduling problems or to other higher-level integrated manufacturing problems.