Almaira Raisa Iga Putri, Rudi Nurdiansyah
This paper investigates the optimization of the No-Idle Permutation Flowshop Scheduling Problem (NIPFSP) with the aim of minimizing total energy consumption in manufacturing environments. Two population-based metaheuristic algorithms, Differential Evolution (DE) and Whale Optimization Algorithm (WOA), are implemented and evaluated on a real-world case involving 27 jobs and 4 machines. Experimental results indicate that the DE algorithm provides the best energy-saving performance, while WOA exhibits faster computational time. Both approaches demonstrate notable improvements over conventional methods, highlighting their potential for enhancing energy efficiency in industrial scheduling applications. © 2025 IEEE.
Universitas Negeri Malang, Department of Mechanical and Industrial Engineering, Malang, Indonesia
