Hybrid flow shop scheduling (HFS) has been thoroughly studied due to its significant impact on productivity. Besides the impact on
productivity, the abovementioned problem has attracted researchers from different background because of its difficulty in obtaining
the most optimum solution. HFS complexity provides good opportunity for researcher to propose an efficient optimization method for
the said problem. Recently, research in HFS has moved towards sustainability by considering energy utilization in the study.
Consequently, the problem becomes more difficult to be solved via existing approach. This paper modeled and optimized HFS with
energy consumption using Tiki-Taka Algorithm (TTA). TTA is a novel algorithm inspired by football playing style that focuses on
short passing and player positioning. In different with existing metaheuristics, the TTA collected information from nearby solution and
utilized multiple leaders’ concept in the algorithm. The research began with problem modeling, followed by TTA algorithm
formulation. A computational experiment is then conducted using benchmark problems. Then, a case study problem is presented to
assess the applicability of model and algorithm in real-life problems. The results indicated that the TTA consistently was in the first
and second ranks in all benchmark problems. In addition, the case study results confirmed that TTA is able to search the best fitness
solution by compromising the makespan and total energy utilization in the production schedule. In future, the potential of TTA will be
further investigated for flexible hybrid flow shop scheduling problems.
Keywords
Hybrid flow shop, Tiki-Taka algorithm, Energy efficiency