Aslan, ŞehmusAslan, Ş.Department of Management / İşletme Bölümü2025-02-152025-02-1520250305-215Xhttps://doi.org/10.1080/0305215X.2024.2420738Group technology is a managerial strategy used to optimize production by reducing setup times, lead times, and work-in-process inventories. Research on flow-shop sequence-dependent group scheduling problems (FSDGSPs) has primarily focused on minimizing makespan and total flow time to improve efficiency. However, the need for energy-efficient scheduling in FSDGSPs remains underexplored despite increasing sustainability concerns. To address this, the energy-efficient flow-shop sequence-dependent group scheduling problem (EEFSDGSP) is introduced. A novel multi-objective optimization (MOO) technique, the artificial neural network-based multi-objective genetic algorithm (ANN-MOGA), is proposed to minimize makespan and energy consumption in EEFSDGSP. ANN-MOGA advances MOO by using a neural network to evaluate fitness and guide selection, reducing computational complexity versus traditional methods like NSGA-II and SPEA2. A post-processing step (PPANNS) further enhances solution diversity and distribution. Results show ANN-MOGA, especially with PPANNS, outperforms NSGA-II and competes effectively with SPEA2 in larger problem instances. © 2024 Informa UK Limited, trading as Taylor & Francis Group.en10.1080/0305215X.2024.2420738info:eu-repo/semantics/closedAccessArtificial Neural NetworkEnergy AwareFlow-Shop Group SchedulingGenetic AlgorithmMulti-Objective OptimizationArticle572333360Q2Q2WOS:0013531889000012-s2.0-1050018376460