Energy-efficient scheduling of AGV-assisted robotic flexible flowshops under learning and processing time uncertainty

Area of Science:

• Operations Research
• Manufacturing Systems Engineering
• Computational Intelligence

Background:

• Flexible flow shop scheduling problems (FFSP) are critical in manufacturing.
• Integrating automated guided vehicles (AGVs), sequence-dependent setup times, and learning effects presents complex challenges.
• Energy efficiency is a growing concern in modern production systems.

Purpose of the Study:

• To develop an energy-efficient flexible flow shop scheduling problem (EEFFSP) model.
• To incorporate fuzzy uncertainty in processing times and learning coefficients.
• To simultaneously minimize makespan and total energy consumption.

Main Methods:

• A mixed-integer programming model was formulated.
• Fuzzy programming with Jiménez's ranking method was used to handle uncertainty.
• Multi-objective optimization was performed using AUGMECON, NSGA, and NSGA-II algorithms.

Main Results:

• The fuzzy-based NSGA-II algorithm demonstrated superior performance in providing high-quality Pareto solutions.
• The proposed hybrid framework achieved a balance between energy efficiency and production performance.
• NSGA-II exhibited enhanced solution diversity and robustness compared to other methods.

Conclusions:

• The study presents a novel integration of fuzziness, learning effects, and AGV scheduling in EEFFSP.
• The fuzzy-based NSGA-II is effective for complex, uncertain production environments.
• Findings offer valuable insights for designing sustainable manufacturing systems.