Neighbourhood-search-enhanced non-dominated sorting genetic algorithm-III for multi-objective assembly line balancing problem considering operator skill levels and carbon footprint

With growing consumer awareness of carbon footprints, manufacturers must achieve profitability while maintaining sustainability. This study introduces a multi-objective optimization model for production lines that emphasizes the balance rate, minimal emissions and profit maximization, incorporating worker skill levels as a key variable. To solve this model, an enhanced version of the non-dominated sorting genetic algorithm-III integrated with a neighbourhood search strategy (NSGA-III-LS) is proposed. The chromosome decoding process was reframed as a one-dimensional binning problem, enabling the rapid calculation of workstation cycle times for determining the relationship between processes and workstations. The experimental results and real-world case studies validate the effectiveness of NSGA-III-LS. It was found that the multi-objective model effectively balances diverse objectives and promotes sustainable production. Moreover, the model considering varying skill levels outperforms the model with fixed skill levels in terms of the balance rate. Finally, the proposed algorithm demonstrates superior performance in solving this complex problem.