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This paper presents an Adaptive Knowledge-based Multi-Objective Evolutionary Algorithm (AMOEA/D) designed to solve the Hybrid Flow Shop Scheduling Problem with Parallel Batch Processing Machines (PBHFSP), optimizing both makespan and total energy consumption. The proposed algorithm incorporates a hybrid initialization strategy, a disjunctive graph model, and adaptive search time adjustments to enhance its performance. Experimental results demonstrate that AMOEA/D outperforms existing state-of-the-art algori

Author

Safiye Sencer

Language

EN