An Effective Hybrid Novel Genetic and Adaptive Artificial Bee Colony (NG-AABC) Metaheuristic Algorithm for Transforming Concurrent Scheduling Problems - Kaur, Gaganpreet; Mishra, Radhey Shyam; Madan, Ashok kumar (International Journal of Industrial Engineering: Theory, Applications and Practice, 2024)
In Industry 4.0, Automated Guided Vehicles (AGVs) enhance material handling efficiency and cost reduction. However, research on multi-objective scheduling of jobs, tools, automated storage, and AGVs in Flexible Manufacturing Systems (FMS) is limited. This study introduces the Novel Genetic and Adaptive Artificial Bee Colony Algorithm (NG-AABCA) to minimize the makespan, total tardiness, and penalty costs. NG-AABCA integrates cognitive (ε1) and social (ε2) learning factors, often overlooked, to achieve optimal solutions by leveraging external sources like the global optimal solution. This approach expedites convergence and avoids local optima by adjusting parameters iteratively. The Genetic Algorithm component employs elitism and Random-Restart Hill-Climbing to balance solution quality and diversity. Compared to other algorithms, NG-AABCA reduces makespan by 5.3% and tardiness by 8.7%, promising increased productivity and efficient resource use. This robust method aims to transform manufacturing optimization in Industry 4.0, addressing complex scheduling challenges in FMSs effectively.