Greedy mechanism-based bi-objective optimization for green scheduling in manufacturing systems considering transportation

This paper addresses scheduling challenges in hybrid flow manufacturing systems with crane transportation (HFMS-CT) driven by intelligent control, mass customization, and eco-friendly manufacturing. Unlike previous studies, it considers the interdependence between machine processing and crane transport, focusing on minimizing both makespan and energy consumption. A bi-objective mixed-integer programming model is developed, and the Epsilon-constraint method is used for small-scale cases. Given the NP-hardness, a modified multi-objective Harris Hawk optimization (MMOHHO) is proposed. It adopts greedy mechanisms by integrating Laplace crossover, tent-based chaotic mapping, elite selection, and nonlinear optimization strategy to balance exploration and exploitation capabilities. The proposed algorithm is compared with the Epsilon-constraint method and benchmark metaheuristics. The experimental results reveal that the proposed algorithm outperforms other methods regarding NPS, DPO, IGD, and ES evaluation metrics. Finally, an in-depth discussion is conducted using a real-world case study, offering valuable managerial insights and practical recommendations for implementation.