Assembly line Optimization: A systematic review of various algorithms, applications & drawbacks
Abstract
One of the most popular production methods is assembly line manufacturing. The assembly line balance problem, which is used to quickly build large quantities of a consistent product, is focused on decreasing the number of workstations, lowering cycle time, maximizing work cohesion, and maximizing workload uniformity. The initial purpose of assembly lines was to produce standardized goods in large quantities at a reasonable cost. A production unit's assembly line consists of a number of employees and equipment. An assembly line is used to produce the components in a production unit. During processing, the product travels along this line. In a flexible manufacturing system, assembly lines have changed throughout time from straight lines with a single model to mixed lines with numerous models, U-shaped lines, and lines with parallel workstations. Reducing the number of work stations and balancing the assembly line based on the intended production volume each shift are the primary goals of system assembly line planning. Various assembly line balance difficulties (ALB) have been examined and described in this paper. Researchers can greatly benefit from the creation of a mathematical model to address the assembly line balancing and sequencing difficulties.
References
References
Álvarez-Miranda, E., Pereira, J., Torrez-Meruvia, H., & Vilà, M. (2021). A hybrid genetic algorithm for the simple assembly line balancing problem with a fixed number of workstations. Mathematics, 9(17), 2157
Abdous, M. A., Delorme, X., Battini, D., Sgarbossa, F., & Berger-Douce, S. (2023). Assembly line balancing problem with ergonomics: a new fatigue and recovery model. International Journal of Production Research, 61(3), 693-706
Ab Rashid, M. F. F., Tiwari, A., & Hutabarat, W. (2019). Integrated optimization of mixed-model assembly sequence planning and line balancing using multi-objective discrete particle swarm optimization. AI EDAM, 33(3), 332-345.
Boysen, N., Schulze, P., & Scholl, A. (2022). Assembly line balancing: What happened in the last fifteen years?. European Journal of Operational Research, 301(3), 797-814
Bukchin, Y., & Raviv, T. (2018). Constraint programming for solving various assembly line balancing problems. Omega, 78, 57-68.
Borba, L., Ritt, M., & Miralles, C. (2018). Exact and heuristic methods for solving the robotic assembly line balancing problem. European Journal of Operational Research, 270(1), 146-156
Burggräf, P., Dannapfel, M., Adlon, T., Riegauf, A., Schukat, E., & Schuster, F. (2020). Optimization approach for the combined planning and control of an agile assembly system for electric vehicles. In Proceedings of the Conference on Production Systems and Logistics: CPSL 2020. Hannover: publish-Ing..
Baskaran, S., Niaki, F. A., Tomaszewski, M., Gill, J. S., Chen, Y., Jia, Y., ... & Krovi, V. (2019). Digital human and robot simulation in automotive assembly using siemens process simulate: a feasibility study. Procedia Manufacturing, 34, 986-994
Çil, Z. A., Li, Z., Mete, S., & Özceylan, E. (2020). Mathematical model and bee algorithms for mixed-model assembly line balancing problem with physical human–robot collaboration. Applied soft computing, 93, 106394.
Cohen, Y., Naseraldin, H., Chaudhuri, A., & Pilati, F. (2019). Assembly systems in Industry 4.0 era: a road map to understand Assembly 4.0. The International Journal of Advanced Manufacturing Technology, 105, 4037-4054.
Comberti, L., Baldissone, G., & Demichela, M. (2019). An Empirical Approach to Workload Assessment for Process Optimization. CET Journal-Chemical Engineering Transactions, 74.
Dalle Mura, M., & Dini, G. (2019). Optimizing ergonomics in assembly lines: A multi objective genetic algorithm. CIRP Journal of Manufacturing Science and Technology, 27, 31-45.
Dolgui, A., Kovalev, S., Kovalyov, M. Y., Malyutin, S., & Soukhal, A. (2018). Optimal workforce assignment to operations of a paced assembly line. European Journal of Operational Research, 264(1), 200-211.
Dias, P., Silva, F. J. G., Campilho, R. D. S. G., Ferreira, L. P., & Santos, T. (2019). Analysis and improvement of an assembly line in the automotive industry. Procedia Manufacturing, 38, 1444-1452.
Fathi, M., Fontes, D. B. M. M., Urenda Moris, M., & Ghobakhloo, M. (2018). Assembly line balancing problem: A comparative evaluation of heuristics and a computational assessment of objectives. Journal of Modelling in Management, 13(2), 455-474.
Gansterer, M., & Hartl, R. F. (2018). One-and two-sided assembly line balancing problems with real-world constraints. International Journal of Production Research, 56(8), 3025-3042.
Janardhanan, M. N., Li, Z., & Nielsen, P. (2019). Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem. Soft Computing, 23, 11263-11276
Kang, Z., Catal, C., & Tekinerdogan, B. (2020). Machine learning applications in production lines: A systematic literature review. Computers & Industrial Engineering, 149, 106773.
Kousi, N., Gkournelos, C., Aivaliotis, S., Giannoulis, C., Michalos, G., & Makris, S. (2019). Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia manufacturing, 28, 121-126.
Kumaran, M., & Senthilkumar, V. (2021). Generative design and topology optimization of analysis and repair work of industrial robot arm manufactured using additive manufacturing technology. In IOP Conference Series: Materials Science and Engineering (Vol. 1012, No. 1, p. 012036). IOP Publishing.
Kousi, N., Gkournelos, C., Aivaliotis, S., Giannoulis, C., Michalos, G., & Makris, S. (2019). Digital twin for adaptation of robots’ behavior in flexible robotic assembly lines. Procedia manufacturing, 28, 121-126.
Li, J. Q., Sang, H. Y., Han, Y. Y., Wang, C. G., & Gao, K. Z. (2018). Efficient multi-objective optimization algorithm for hybrid flow shop scheduling problems with setup energy consumptions. Journal of Cleaner Production, 181, 584-598.
Li, Z., Çil, Z. A., Mete, S., & Kucukkoc, I. (2020). A fast branch, bound and remember algorithm for disassembly line balancing problem. International Journal of Production Research, 58(11), 3220-3234.
Liu, J., Gaynor, A. T., Chen, S., Kang, Z., Suresh, K., Takezawa, A., ... & To, A. (2018). Current and future trends in topology optimization for additive manufacturing. Structural and multidisciplinary optimization, 57(6), 2457-2483.
Mumtaz, J., Guan, Z., Yue, L., Wang, Z., Ullah, S., & Rauf, M. (2019). Multi-level planning and scheduling for parallel PCB assembly lines using hybrid spider monkey optimization approach. Ieee Access, 7, 18685-18700.
Michels, A. S., Lopes, T. C., Sikora, C. G. S., & Magatão, L. (2019). A Benders’ decomposition algorithm with combinatorial cuts for the multi-manned assembly line balancing problem. European Journal of Operational Research, 278(3), 796-808.
Mumtaz, J., Guan, Z., Yue, L., Wang, Z., Ullah, S., & Rauf, M. (2019). Multi-level planning and scheduling for parallel PCB assembly lines using hybrid spider monkey optimization approach. Ieee Access, 7, 18685-18700.
Moussavi, S. E., Zare, M., Mahdjoub, M., & Grunder, O. (2019). Balancing high operator's workload through a new job rotation approach: Application to an automotive assembly line. International Journal of Industrial Ergonomics, 71, 136-144
Nourmohammadi, A., Fathi, M., & Ng, A. H. (2022). Balancing and scheduling assembly lines with human-robot collaboration tasks. Computers & Operations Research, 140, 105674.
Osaba, E., Villar-Rodriguez, E., Del Ser, J., Nebro, A. J., Molina, D., LaTorre, A., ... & Herrera, F. (2021). A tutorial on the design, experimentation and application of metaheuristic algorithms to real-world optimization problems. Swarm and Evolutionary Computation, 64, 100888.
Oliva, M., Mas, F., Eguia, I., Del Valle, C., Lourenço, E. J., & Baptista, A. J. (2020). An innovative methodology to optimize aerospace eco-efficiency assembly processes. In Product Lifecycle Management Enabling Smart X: 17th IFIP WG 5.1 International Conference, PLM 2020, Rapperswil, Switzerland, July 5–8, 2020, Revised Selected Papers 17 (pp. 448-459). Springer International Publishing.
Roussel, S., Polacsek, T., & Chan, A. (2023, August). Assembly line preliminary design optimization for an aircraft. In CP 2023 (The 29th International Conference on Principles and Practice of Constraint Programming).
Rezaei Aderiani, A., Wärmefjord, K., & Söderberg, R. (2020). An improved phenotype-genotype mapping for solving selective assembly problem using evolutionary optimization algorithms. Journal of Computing and Information Science in Engineering, 20(6), 061010.
Romero-Silva, R., & Hernández-López, G. (2020). Shop-floor scheduling as a competitive advantage: A study on the relevance of cyber-physical systems in different manufacturing contexts. International journal of production economics, 224, 107555.
Rauf, M., Guan, Z., Sarfraz, S., Mumtaz, J., Shehab, E., Jahanzaib, M., & Hanif, M. (2020). A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines. Robotics and Computer-Integrated Manufacturing, 61, 101844.
Rosa, C., Silva, F. J. G., Ferreira, L. P., Pereira, T., & Gouveia, R. (2018). Establishing standard methodologies to improve the production rate of assembly lines used for low added-value products. Procedia Manufacturing, 17, 555-562.
Rossit, D. A., Tohmé, F., & Frutos, M. (2019). An Industry 4.0 approach to assembly line resequencing. The International Journal of Advanced Manufacturing Technology, 105, 3619-3630.
Samouei, P., & Ashayeri, J. (2019). Developing optimization & robust models for a mixed-model assembly line balancing problem with semi-automated operations. Applied mathematical modelling, 72, 259-275.
Soltanali, H., Rohani, A., Tabasizadeh, M., Abbaspour-Fard, M. H., & Parida, A. (2020). Operational reliability evaluation-based maintenance planning for automotive production line. Quality Technology & Quantitative Management, 17(2), 186-202.
Sedding, H. A. (2024). Mixed-model moving assembly line material placement optimization for a shorter time-dependent worker walking time. Journal of Scheduling, 27(3), 257-275.
Şahin, M., & Kellegöz, T. (2019). A new mixed-integer linear programming formulation and particle swarm optimization based hybrid heuristic for the problem of resource investment and balancing of the assembly line with multi-manned workstations. Computers & Industrial Engineering, 133, 107-120
Tanhaie, F. (2024). Applying a multi-objective particle swarm optimization algorithm for sequencing and balancing a mixed-model assembly line problem with setup times between tasks. Journal of Applied Research on Industrial Engineering, 11(3), 350-368.
Wątróbski, J., Karczmarczyk, A., & Rymaszewski, S. (2020). Multi-criteria decision making approach to production line optimization. Procedia Computer Science, 176, 3820-3830.
Wang, B., Liu, G., & Wu, J. (2019). Blind deblurring of saturated images based on optimization and deep learning for dynamic visual inspection on the assembly line. Symmetry, 11(5), 678.
Weckenborg, C., Kieckhäfer, K., Müller, C., Grunewald, M., & Spengler, T. S. (2020). Balancing of assembly lines with collaborative robots. Business Research, 13(1), 93-132.
Yang, S. L., Xu, Z. G., & Wang, J. Y. (2019). Modelling and production configuration optimization for an assembly shop. International Journal of Simulation Modelling, 18(2), 366-377.
Yang, M. S., Ba, L., Xu, E. B., Li, Y., Gao, X. Q., Liu, Y., & Li, Y. (2019). Batch optimization in integrated scheduling of machining and assembly. International Journal of Simulation Modelling, 18(4), 689-698.
Yılmaz, Ö. F. (2020). Robust optimization for U-shaped assembly line worker assignment and balancing problem with uncertain task times. Croatian Operational Research Review, 229-239.
Zhou, B. H., & Tan, F. (2018). Electric vehicle handling routing and battery swap station location optimisation for automotive assembly lines. International Journal of Computer Integrated Manufacturing, 31(10), 978-991.
Zhang, Z., Tang, Q., Ruiz, R., & Zhang, L. (2020). Ergonomic risk and cycle time minimization for the U-shaped worker assignment assembly line balancing problem: A multi-objective approach. Computers & Operations Research, 118, 104905.
