Hybrid evolutionary algorithm for stochastic multiobjective disassembly line balancing problem in remanufacturing

Area of Science:

• Industrial Engineering
• Environmental Engineering
• Operations Research

Background:

• Growing industrial economies generate substantial end-of-life (EOL) products, increasing environmental pollution.
• Recycling and remanufacturing EOL products are critical for sustainability.
• Efficient disassembly lines are key to improving EOL product processing and reducing environmental impact.

Purpose of the Study:

• To address the uncertainty ignored in traditional disassembly line balancing problems (DLBP).
• To propose a stochastic multi-objective optimization model for DLBP.
• To minimize disassembly idle rate, smoothness, and energy consumption under uncertain operation times.

Main Methods:

• Development of a stochastic multi-objective optimization model for the disassembly line balancing problem (DLBP).
• Implementation of an improved northern goshawk optimization algorithm integrated with stochastic simulation.
• Validation using two extensive case examples to demonstrate model feasibility and algorithm applicability.

Main Results:

• The proposed stochastic model effectively handles uncertain operation times in DLBP.
• The enhanced northern goshawk optimization algorithm successfully solves the complex multi-objective problem.
• Demonstrated improvements in minimizing disassembly idle rate, smoothness, and energy consumption.

Conclusions:

• The developed stochastic optimization model and algorithm offer a robust solution for EOL product disassembly line balancing.
• This approach enhances the efficiency and environmental sustainability of recycling and remanufacturing processes.
• The findings provide valuable insights for optimizing industrial waste management and resource recovery.