In response to the deficiencies of commonly used optimization methods for assembly lines, a production demand-oriented optimization method for assembly lines is proposed. Taking a certain compressor assembly line as an example, the production rhythm and the number of workstations are calculated based on production requirements and working systems. With assembly rhythm and smoothing index as optimization goals, an improved particle swarm optimization algorithm is employed for process allocation. Subsequently, Flexsim simulation is used to analyze the assembly line. The final results show that after optimization using the improved particle swarm algorithm, the assembly line balance rate increased from 71.1% to 85.9%, and the assembly line smoothing index decreased from 47.4 to 29.8, significantly enhancing assembly efficiency. This demonstrates the effectiveness of the proposed optimization method for the assembly line and provides a reference for other products in the same industry.
Yuan KH, Zhang HP, Nan SJ, 2023, Too Big but Not Strong Characteristic and Micro-mechanisms of Digitalization in Chinese Manufacturing Enterprises: Empirical Evidence from Exporting Firms. International Trade Problems, 2023(5): 140–157.
Tripathi V, Chattopadhyaya S, Mukhopadhyay AK, et al., 2022, A Sustainable Methodology Using Lean and Smart Manufacturing for the Cleaner Production of Shop Floor Management in Industry 4.0. Mathematics, 10(3): 347.
Yu HL, He T, 2024, Flexible Production Line Scheduling Method Based on Particle Swarm Optimization Algorithm. Aerospace Manufacturing Technology, 67(6): 84–91.
Xiao H, Zheng QX, 2023, Improved Particle Swarm Optimization Algorithm for the Second Type of Assembly Line Balancing Problem. Journal of Hubei University (Natural Science Edition), 45(2): 164–170.
Yang QM, Liu N, 2024, Optimization of Fuzzy PID Control for Four-Wheel Drive Vehicles Based on Improved Particle Swarm Optimization Algorithm. Chinese Journal of Construction Machinery, 22(6): 744–749.
Luo QY, Jiang DY, Hu XB, et al., 2023, Simulation Optimization of Key Tanning Processes Based on Particle Swarm Optimization Algorithm and Witness. Mechanical Design and Manufacture, 2023(5): 211–214.
Ankita, Sahana SK, 2022, Ba-PSO: A Balanced PSO to Solve Multi-objective Grid Scheduling Problem. Applied Intelligence, 52(4): 4015–4027.
Wang M, Jiang JW, Cao YT, 2022, A Dynamic Target Grasping Control Method for Food Sorting Robot Based on Improved Particle Swarm Optimization. Food & Machinery, 38(3): 86–91.
Zhao B, Wang G, Song JY, et al., 2021, PSO-Algorithm-Based Optimal Design of LCLC Resonant Converters for Space Travelling-Wave Tube Amplifiers Applications. Journal of Electronics & Information Technology, 43(6): 1622–1629.
Shojaie AA, Bariran SES, 2020, Comparison of Two Modified Meta-heuristic Soft Algorithms for Solving a Bi-objective Facility Layout Problem. International Journal of Mathematics in Operational Research, 16(3): 335–345.
Aydoğan EK, Delice Y, Özcan U, et al., 2019, Balancing Stochastic U-lines Using Particle Swarm Optimization. Journal of Intelligent Manufacturing, 30: 97–111.
Yang Z, Liu CG, 2018, Improved Particle Swarm Optimization Algorithm for Multi-objective Fuzzy Scheduling of Shipbuilding Block Production Line. Journal of Ship Science and Technology, 40(9): 46–51.
Liu J, Zhang H, He K, et al., 2018, Multi-objective Particle Swarm Optimization Algorithm Based on Objective Space Division for the Unequal-area Facility Layout Problem. Expert Systems With Applications, 102: 179–192.
Yang B, Chen W, Lin C, et al., 2017, The Algorithm and Simulation of Multi-objective Sequence and Balancing Problem for Mixed Mode Assembly Line. International Journal of Simulation Modeling, 16(2): 357–367.