Abstract
The Cutting Path Problem (CPP) is a complex continuous and combinatorial optimization problem that is about finding an optimal tool path for CNC technologies equipment. The problem has many valuable industrial applications arising from the Industry 4.0 strategy, such as those, related to tool path routing for the sheet metal cutting machines. The CPP is strongly NP-hard enclosing variants of the well-known Traveling Salesman Problem (TSP) as sub-problems. In this paper, we for the first time propose an open access library of sample instances (CPPLib) for the CPP to facilitate the benchmarking of optimization algorithms, most of them are heuristics or metaheuristics. Each instance is obtained as an image of a finite set of mutually nested industrial parts on a metal sheet and is presented in the DXF vector format that is induced by a solution result of the well-known 2D nesting problem. For the first time we propose geometric and quantitative principles for constructing different groups (classes) of such image instances. Along with continuous CPP settings, the library contains their discrete counterparts presented in the form of instances of the Precedence Constraints Generalized Traveling Salesman Problem (PCGTSP), since the solution processes for the CPP mostly based on discretizing boundary contours of parts. In addition, the paper presents examples of testing some optimization algorithms for solving the cutting path problem on test instances from the developed CPPLib library.
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Acknowledgements
This research was performed as part of research conducted in the Ural Mathematical Center and funded by the Russian Foundation for Basic Research, grants no. 19-01-00573 and no. 20-08-00873.
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Petunin, A. et al. (2021). Library of Sample Image Instances for the Cutting Path Problem. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12665. Springer, Cham. https://doi.org/10.1007/978-3-030-68821-9_21
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