DOI QR코드

DOI QR Code

Faster pipe auto-routing using improved jump point search

  • Min, Jwa-Geun (Graduate School, Dept. of Naval Architecture & Ocean Engineering, Chungnam National University) ;
  • Ruy, Won-Sun (Department of Naval Architecture & Ocean Engineering, Chungnam National University) ;
  • Park, Chul Su (Sv Plant Engineering)
  • Received : 2019.11.25
  • Accepted : 2020.07.05
  • Published : 2020.12.31

Abstract

Previous studies on pipe auto-routing algorithms generally used such algorithms as A*, Dijkstra, Genetic Algorithm, Particle Swarm Optimization, and Ant Colony Optimization, to satisfy the relevant constraints of its own field and improve the output quality. On the other hand, this study aimed to significantly improve path-finding speed by applying the Jump Point Search (JPS) algorithm, which requires lower search cost than the abovementioned algorithms, for pipe routing. The existing JPS, however, is limited to two-dimensional spaces and can only find the shortest path. Thus, it requires several improvements to be applied to pipe routing. Pipe routing is performed in a three-dimensional space, and the path of piping must be parallel to the axis to minimize its interference with other facilities. In addition, the number of elbows must be reduced to the maximum from an economic perspective, and preferred spaces in the path must also be included. The existing JPS was improved for the pipe routing problem such that it can consider the above-mentioned problem. The fast path-finding speed of the proposed algorithm was verified by comparing it with the conventional A* algorithm in terms of resolution.

Keywords

References

  1. Ando, Y., Kimura, H., 2012. An automatic piping algorithm including elbows and bends. JASNAOE 15, 219-226.
  2. Asmara, A., Nienhuis, U., 2006. Automatic piping system in ship. In: International Conference on Computer and IT Application (COMPIT), May.
  3. Guirardello, R., Swaney, R.E., 2005. Optimization of process plant layout with pipe routing. Comput. Chem. Eng. 30 (1), 99-114. https://doi.org/10.1016/j.compchemeng.2005.08.009
  4. Harabor, D.D., Grastien, A., 2011. Online graph pruning for pathfinding on grid maps. In: Twenty-Fifth AAAI Conference on Artificial Intelligence, August.
  5. Ito, T., 1999. A genetic algorithm approach to piping route path planning. J. Intell. Manuf. 10 (1), 103-114. https://doi.org/10.1023/A:1008924832167
  6. Kim, D.G., Corne, D., Ross, P., 1996. Industrial plant pipe-route optimisation with genetic algorithms. In: International Conference on Parallel Problem Solving from Nature. Springer, Berlin, Heidelberg, pp. 1012-1021. September.
  7. Kim, S.H., Ruy, W.S., Jang, B.S., 2013. The development of a practical pipe autorouting system in a shipbuilding CAD environment using network optimization. Int. J. Nav. Archit. Ocean Eng. 5 (3), 468-477. https://doi.org/10.2478/IJNAOE-2013-0146
  8. Liu, Q., Wang, C., 2012. Multi-terminal pipe routing by Steiner minimal tree and particle swarm optimisation. Enterp. Inf. Syst-UK 6 (3), 315-327. https://doi.org/10.1080/17517575.2011.594910
  9. Liu, Q., Wang, C., 2015. A graph-based pipe routing algorithm in aero-engine rotational space. J. Intell. Manuf. 26 (6), 1077-1083. https://doi.org/10.1007/s10845-013-0840-0
  10. Park, J.H., Storch, R.L., 2002. Pipe-routing algorithm development: case study of a ship engine room design. Expert Syst. Appl. 23 (3), 299-309. https://doi.org/10.1016/S0957-4174(02)00049-0
  11. Qu, Y., Jiang, D., Yang, Q., 2018. Branch pipe routing based on 3D connection graph and concurrent ant colony optimization algorithm. J. Intell. Manuf. 29 (7), 1647-1657. https://doi.org/10.1007/s10845-016-1203-4

Cited by

  1. Integrated Optimization of Pipe Routing and Clamp Layout for Aeroengine Using Improved MOALO vol.2021, 2020, https://doi.org/10.1155/2021/6681322