Parallel batch processing machines scheduling for SLM additive manufacturing in cloud manufacturing

Shuangshuang Hong; Xiuli Wang

doi:10.54097/5kyzn576

Authors

Shuangshuang Hong
Xiuli Wang

DOI:

https://doi.org/10.54097/5kyzn576

Keywords:

Additive manufacturing; Batch processing machines; Cloud manufacturing; Artificial Bee Colony algorithm.

Abstract

For the scheduling problem of parallel batch processing machines for Selective Laser Melting (SLM) additive manufacturing in cloud manufacturing environments, an Improved Discrete Artificial Bee Colony algorithm(IABC) is proposed by considering the service time of the product as well as introducing the mathematical model of the problem. Firstly, a population initialization strategy is introduced to improve the quality of initial solutions and accelerate the convergence of the population.Secondly, through three neighborhood search operators, the transition from the original nectar source to the new nectar source is achieved for employed bees and following bees. Furthermore, a local search strategy and a modified abandonment pattern are proposed to increase population diversity and prevent the algorithm from falling into local optima.Finally, the performance of the algorithm is analyzed, and the IABC algorithm was applied to 36 examples. The effectiveness and advantages of IABC in solving the studied problem are verified through a large number of simulation experiments.

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References

Zhang Y, Bernard A, Harik R, et al. Build orientation optimization for multi-part production in additive manufacturing. Journal of Intelligent Manufacturing, 2017, 28: 1393-1407.

Zhang Y, Bernard A, Harik R, et al. A new method for single-layer-part nesting in additive manufacturing. Rapid Prototyping Journal, 2018, 24(5): 840-854.

Lee S J, Kim B S. Two-stage meta-heuristic for part-packing and build-scheduling problem in parallel additive manufacturing. Applied Soft Computing, 2023: 110132.

Griffiths V, Scanlan J P, Eres M H, et al. Cost-driven build orientation and bin packing of parts in Selective Laser Melting (SLM). European Journal of Operational Research, 2019, 273(1): 334-352.

Kucukkoc, Ibrahim, Qiang Li, and David Z. Zhang. Increasing the utilisation of additive manufacturing and 3D printing machines considering order delivery times. 19th International working seminar on production economics. 2016.

Li Q, Kucukkoc I, Zhang D Z. Production planning in additive manufacturing and 3D printing. Computers & Operations Research, 2017, 83: 157-172.

Kucukkoc I. MILP models to minimise makespan in additive manufacturing machine scheduling problems. Computers & Operations Research, 2019, 105: 58-67.

Li X, Zhang K. Single batch processing machine scheduling with two-dimensional bin packing constraints. International Journal of Production Economics, 2018, 196: 113-121.

Kim Y J, Kim B S. Part-group and build-scheduling with sequence-dependent setup time to minimize the makespan for non-identical parallel additive manufacturing machines. The International Journal of Advanced Manufacturing Technology, 2022: 1-12.

Toksarı M D, Toğa G. Single batch processing machine scheduling with sequence-dependent setup times and multi-material parts in additive manufacturing. CIRP Journal of Manufacturing Science and Technology, 2022, 37: 302-311.

Chergui A, Hadj-Hamou K, Vignat F. Production scheduling and nesting in additive manufacturing. Computers & Industrial Engineering, 2018, 126: 292-301.

Rohaninejad M, Hanzálek Z, Tavakkoli-Moghaddam R. Scheduling of parallel 3D-printing machines with incompatible job families: A matheuristic algorithm. IFIP International Conference on Advances in Production Management Systems. Cham: Springer International Publishing, 2021: 51-61.

Aloui A, Hadj-Hamou K. A heuristic approach for a scheduling problem in additive manufacturing under technological constraints. Computers & Industrial Engineering, 2021, 154: 107115.

Rudolph J P, Emmelmann C. A cloud-based platform for automated order processing in additive manufacturing. Procedia Cirp, 2017, 63: 412-417.

Qian C, Zhang Y, Liu Y, et al. A cloud service platform integrating additive and subtractive manufacturing with high resource efficiency. Journal of Cleaner Production, 2019, 241: 118379.

Wu Q, Xie N, et al. Integrated cross-supplier order and logistic scheduling in cloud manufacturing. International Journal of Production Research, 2020, 60: 5, 1633-1649

Chang, P.-Y., P. Damodaran, and S. Melouk. Minimizing Makespan on Parallel Batch Processing Machines. International Journal of Production Research 2004, 42:(19): 4211-4220.

Zehetner D, Gansterer M. The collaborative batching problem in multi-site additive manufacturing. International Journal of Production Economics, 2022, 248: 108432.

Karaboga D. An idea based on honey bee swarm for numerical optimization. Technical Report TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.

Yazdani M, Gohari S, Naderi B. Multi-factory parallel machine problems: Improved mathematical models and artificial bee colony algorithm. Computers and Industrial Engineering, 2015, 81:36-45.

Rohaninejad M, Tavakkoli-Moghaddam R, Vahedi-Nouri B, et al. A hybrid learning-based meta-heuristic algorithm for scheduling of an additive manufacturing system consisting of parallel SLM machines. International Journal of Production Research, 2022, 60(20): 6205-6225.

Baumers M, Beltrametti L, Gasparre A, et al. Informing additive manufacturing technology adoption: total cost and the impact of capacity utilisation. International Journal of Production Research, 2017, 55(23): 6957-6970.

Zhang J, Yao X, Li Y. Improved evolutionary algorithm for parallel batch processing machine scheduling in additive manufacturing. International Journal of Production Research, 2020, 58(8): 2263-2282.

Yeh W C, Lai P J, Lee W C, et al. Parallel-machine scheduling to minimize makespan with fuzzy processing times and learning effects. Information Sciences, 2014, 269: 142-158.