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Generalized hyper-heuristics for solving 2D Regular and Irregular Packing Problems

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  • H. Terashima-Marín
  • P. Ross
  • C. Farías-Zárate
  • E. López-Camacho
  • M. Valenzuela-Rendón

Abstract

The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such a combination should outperform the single heuristics. This article presents a GA-based method that produces general hyper-heuristics that solve two-dimensional regular (rectangular) and irregular (convex polygonal) bin-packing problems. A hyper-heuristic is used to define a high-level heuristic that controls low-level heuristics. The hyper-heuristic should decide when and where to apply each single low-level heuristic, depending on the given problem state. In this investigation two kinds of heuristics were considered: for selecting the figures (pieces) and objects (bins), and for placing the figures into the objects. Some of the heuristics were taken from the literature, others were adapted, and some other variations developed by us. We chose the most representative heuristics of their type, considering their individual performance in various studies and also in an initial experimentation on a collection of benchmark problems. The GA included in the proposed model uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce outstanding results for most of the cases. The testbed is composed of problems used in other similar studies in the literature. Some additional instances for the testbed were randomly generated. Copyright Springer Science+Business Media, LLC 2010

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  • H. Terashima-Marín & P. Ross & C. Farías-Zárate & E. López-Camacho & M. Valenzuela-Rendón, 2010. "Generalized hyper-heuristics for solving 2D Regular and Irregular Packing Problems," Annals of Operations Research, Springer, vol. 179(1), pages 369-392, September.
  • Handle: RePEc:spr:annopr:v:179:y:2010:i:1:p:369-392:10.1007/s10479-008-0475-2
    DOI: 10.1007/s10479-008-0475-2
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    1. Bennell, J.A. & Cabo, M. & Martínez-Sykora, A., 2018. "A beam search approach to solve the convex irregular bin packing problem with guillotine guts," European Journal of Operational Research, Elsevier, vol. 270(1), pages 89-102.
    2. Gahm, Christian & Uzunoglu, Aykut & Wahl, Stefan & Ganschinietz, Chantal & Tuma, Axel, 2022. "Applying machine learning for the anticipation of complex nesting solutions in hierarchical production planning," European Journal of Operational Research, Elsevier, vol. 296(3), pages 819-836.
    3. Hongtao Tang & Xixing Li & Shunsheng Guo & Shuwei Liu & Li Li & Lang Huang, 2017. "An optimizing model to solve the nesting problem of rectangle pieces based on genetic algorithm," Journal of Intelligent Manufacturing, Springer, vol. 28(8), pages 1817-1826, December.
    4. Igor Kierkosz & Maciej Łuczak, 2019. "A one-pass heuristic for nesting problems," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 29(1), pages 37-60.
    5. Sato, André Kubagawa & Martins, Thiago Castro & Gomes, Antonio Miguel & Tsuzuki, Marcos Sales Guerra, 2019. "Raster penetration map applied to the irregular packing problem," European Journal of Operational Research, Elsevier, vol. 279(2), pages 657-671.
    6. Eunice López-Camacho & Gabriela Ochoa & Hugo Terashima-Marín & Edmund Burke, 2013. "An effective heuristic for the two-dimensional irregular bin packing problem," Annals of Operations Research, Springer, vol. 206(1), pages 241-264, July.
    7. Martinez-Sykora, A. & Alvarez-Valdes, R. & Bennell, J.A. & Ruiz, R. & Tamarit, J.M., 2017. "Matheuristics for the irregular bin packing problem with free rotations," European Journal of Operational Research, Elsevier, vol. 258(2), pages 440-455.
    8. López-Camacho, Eunice & Terashima-Marín, Hugo & Ochoa, Gabriela & Conant-Pablos, Santiago Enrique, 2013. "Understanding the structure of bin packing problems through principal component analysis," International Journal of Production Economics, Elsevier, vol. 145(2), pages 488-499.
    9. Miguel Santoro & Felipe Lemos, 2015. "Irregular packing: MILP model based on a polygonal enclosure," Annals of Operations Research, Springer, vol. 235(1), pages 693-707, December.
    10. Yizhe Yang & Bingshan Liu & Haochen Li & Xin Li & Gong Wang & Shan Li, 2023. "A nesting optimization method based on digital contour similarity matching for additive manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2825-2847, August.

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