Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125573 - 125573
Published: Oct. 1, 2024
Language: Английский
Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125573 - 125573
Published: Oct. 1, 2024
Language: Английский
Engineering Applications of Artificial Intelligence, Journal Year: 2025, Volume and Issue: 145, P. 110211 - 110211
Published: Feb. 19, 2025
Language: Английский
Citations
3Waste Management, Journal Year: 2025, Volume and Issue: 196, P. 93 - 105
Published: Feb. 21, 2025
Language: Английский
Citations
1Advanced Engineering Informatics, Journal Year: 2024, Volume and Issue: 62, P. 102895 - 102895
Published: Oct. 1, 2024
Language: Английский
Citations
3Computers & Industrial Engineering, Journal Year: 2025, Volume and Issue: unknown, P. 111210 - 111210
Published: May 1, 2025
Language: Английский
Citations
0Processes, Journal Year: 2025, Volume and Issue: 13(6), P. 1675 - 1675
Published: May 27, 2025
The remanufacturing of end-of-life products is an effective approach to alleviating resource shortages, environmental pollution, and global warming. As the initial step in process, quality efficiency disassembly have a decisive impact on entire workflow. However, complexity product structures poses numerous challenges practical operations. These include not only conventional precedence constraints among tasks but also sequential dependencies, where interference between due their execution order can prolong operation times complicate formulation plans. Additionally, inherent uncertainties process further affect applicability Therefore, developing reliable plans must fully consider both dependencies uncertainties. To this end, paper employs chance-constrained programming model characterise uncertain information constructs multi-objective sequence-dependent line balancing (MO-SDDLB) problem under environments. aims minimise hazard index, workstation time variance, energy consumption, achieving multi-dimensional optimisation process. efficiently solve problem, designs innovative adaptive large neighbourhood search (MO-ALNS) algorithm. algorithm integrates three destruction repair operators, combined with simulated annealing, roulette wheel selection, local strategies, significantly enhancing solution quality. Practical experiments lithium-ion battery validate effectiveness proposed Moreover, MO-ALNS demonstrated superior performance compared other state-of-the-art methods. On average, against best competitor results, improved number Pareto solutions (NPS) by approximately 21%, reduced inverted generational distance (IGD) about increased hypervolume (HV) nearly 8%. Furthermore, exhibited stability, providing feasible for optimisation.
Language: Английский
Citations
0Expert Systems with Applications, Journal Year: 2024, Volume and Issue: unknown, P. 125573 - 125573
Published: Oct. 1, 2024
Language: Английский
Citations
1