Collaborative Hyper-Heuristic Ant Colony Algorithm for Solving Multi-objective Fuzzy Low-Carbon Distributed Permutation Flow-Shop and Two-Echelon Vehicle Transportation Integrated Scheduling Problem

This paper proposed a collaborative hyper-heuristic ant colony algorithm (CHACA) for solving a kind of multi-objective fuzzy low-carbon distributed permutation flow-shop and two-echelon vehicle transportation integrated scheduling problem (MDF-TVISP), which adopt triangular fuzzy numbers (TFNs) to represent job processing times and vehicle travel times. The optimization objectives of MDF-TVISP are to minimize the total fuzzy production and transportation costs and fuzzy carbon emissions. CHACA integrated collaborative ant colony (CACA) and hyper-heuristic algorithm (HHA). We employ eight pre-designed heuristic operations in the low-level heuristics (LLHs) to enhance the algorithm’s local search capability, while utilizing a novel CACA to improve the performance of high-level strategy (HLS). The interaction between LLHs and HLS greatly improves the search performance of the CHACA. Finally, simulation experiments and algorithm comparisons validate the effectiveness of CHACA.