An adaptive melody search algorithm based on low-level heuristics for material feeding scheduling optimization in a hybrid kitting system

Facing highly diversified market demands in automotive industry, changing variants of components produced in mixed-model assembly lines (MMALs) has led to an increasing attention towards the material-feeding processes. Therefore, this paper originally proposes a novel type of material-feeding mode called hybrid kitting, leading to a better adaptation to MMALs. Since energy-saving and Just-in-time (JIT) principles are the two major concerns in production systems, a bi-objective mathematical model is established aiming to collaboratively minimize the multi-load automated guided vehicle (AGV) energy consumption as well as the kit conveyor depreciation cost in the hybrid kitting-based material-feeding system. Due to the non-deterministic polynomial hard (NP-hard) nature of the problem, a modified melody search-based hyper-heuristic algorithm (MMSA-HH) is proposed with seven low-level heuristic (LLH) operators. Based on the basic MSA, the melody composition rules are redesigned to enrich the diversity of solutions, adaptive adjustment of parameters is used to balance the local search and global search, and the fluctuated crowding distance calculation method is used in elite selection along with Pareto rank calculation. Computational experiment results reveal the effectiveness of the MMSA-HH when solving the problem. Finally, the managerial insights are given through comparing the impacts of kit container size, AGV type, and different kitting modes on the two objectives.