Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Júnior, Álvaro Luiz Neuenfeldt; Silva, Elsa; Gomes, António Alberto; Soares, Carlos; Oliveira, José Fernando

doi:10.1016/j.eswa.2018.10.006

Cited by 20 publications

(7 citation statements)

References 46 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In problem characterization (Step 3), the independent variables are predictors based on variations of problem characteristics. A total of 19 predictors (Table 1) were developed by (Neuenfeldt et al, 2019) for the 2D-SPP, based on information collected from cutting and packing problems generators and with an observation of literature instances. Variations in the rectangles and the strip dimensions, in addition to intrinsic instances information, were used to develop the 19 predictors (Neuenfeldt et al, 2017).…”

Section: Fig 1 Methodological Frameworkmentioning

confidence: 99%

“…Table 1 Predictors' definition. Source: (Neuenfeldt et al, 2019;Neuenfeldt et al, 2017) Predictor Definition areacomp…”

Section: Fig 1 Methodological Frameworkmentioning

confidence: 99%

“…The rectangles sequence ordering is not completely random. Each rectangle has a probability to occupy the first place to be positioned based on the rectangles' geometric characteristics (Neuenfeldt et al, 2019). For LA, LC, and LD, the current reference solution is replaced only if the new solution found in each iteration is better (lowest gap).…”

Section: Methodological Framework Usagementioning

confidence: 99%

See 2 more Smart Citations

Management and Production Engineering Review

Alvaro,

Francescatto,

Stieler

et al. 2021

View full text Add to dashboard Cite

The present paper describes a methodological framework developed to select a multi-label dataset transformation method in the context of supervised machine learning techniques. We explore the rectangular 2D strip-packing problem (2D-SPP), widely applied in industrial processes to cut sheet metals and paper rolls, where high-quality solutions can be found for more than one improvement heuristic, generating instances with multi-label behavior. To obtain single-label datasets, a total of five multi-label transformation methods are explored. 1000 instances were generated to represent different 2D-SPP variations found in real-world applications, labels for each instance represented by improvement heuristics were calculated, along with 19 predictors provided by problem characteristics. Finally, classification models were fitted to verify the accuracy of each multi-label transformation method. For the 2D-SPP, the single-label obtained using the exclusion method fit more accurate classification models compared to the other four multi-label transformation methods adopted.

show abstract

Section: Fig 1 Methodological Frameworkmentioning

confidence: 99%

“…Table 1 Predictors' definition. Source: (Neuenfeldt et al, 2019;Neuenfeldt et al, 2017) Predictor Definition areacomp…”

Section: Fig 1 Methodological Frameworkmentioning

confidence: 99%

Section: Methodological Framework Usagementioning

confidence: 99%

See 1 more Smart Citation

Management and Production Engineering Review

Alvaro,

Francescatto,

Stieler

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, 36 instances were selected from bwmv [ 35 ], C [ 36 ], cgcut [ 37 ], gcut [ 38 ], ngcut [ 39 ], path [ 34 ], and pt [ 40 ]. The characteristics and corresponding scenarios of each instance are detailed in Table B.1, located in S2 Appendix .…”

Section: Computational Experimentsmentioning

confidence: 99%

Impact of minimum distance constraints on sheet metal waste for plasma cutting

Francescatto,

Luiz Neuenfeldt Júnior,

Silva

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

We approached the two-dimensional rectangular strip packing problem (2D-SPP), where the main goal is to pack a given number of rectangles without any overlap to minimize the height of the strip. Real-life constraints must be considered when developing 2D-SPP algorithms to deliver solutions that will improve the cutting processes. In the 2D-SPP literature, a gap related to studies approaching constraints in real-life scenarios was identified. Therefore, the impact of real-life constraints found in the plasma cutting process in sheet metal waste was analyzed. A mathematical model from the literature was modified to obtain packing arrangements with plasma cutting constraints. The combination of size and number of rectangles, as well as strip width, was the main factor that affected the packing arrangement, limiting the allocation of rectangles and generating empty spaces. In summary, considering the sheet metal waste context, instances with smaller widths should be avoided in practical operations for high minimum distance constraint values, returning the worst packing arrangements. For low minimum distance constraint values, smaller width instances can be used in practical operations, as the packing arrangement is acceptable. Finally, this article can reduce material waste and enhance the cutting process in the sheet metal industry, by showing packing characteristics which lead to higher amounts of raw material waste.

show abstract

“…Both ar pi and ht pi are characterization variables based on complex characteristics used to verify how a single problem instance can be diverse in the rectangles and strip geometries, which is easily used to define its level of complexity, especially when reasonable amounts of empty spaces are allowed in the optimal packing layout. Thus, scenarios are established with the four characterization variables’ average values, using as a reference 790 benchmark problem instances with a maximum of 50 rectangles and W pi ≤ 500 mm from the scientific literature datasets C [ 38 ], N [ 7 ], cx [ 39 ], gcut [ 40 ], cgcut [ 41 ], ngcut [ 42 ], bwmv [ 43 , 44 ], beng [ 45 ], nice/path [ 37 ], and pt [ 46 ]. Therefore, the mean characterization variables values used as a reference for the scenarios are ht pi = 20, W pi = 200 mm , ht pi = 0.6, and ar pi = 1.0.…”

Section: Comparisonmentioning

confidence: 99%

The machining torch movement for the rectangular plasma sheet metal cut

Neuenfeldt Júnior,

Francescatto,

Araújo

et al. 2023

PLoS ONE

Self Cite

View full text Add to dashboard Cite

The sheet metals can be cut into rectangular or irregular pieces, where the main objective is to minimize the sheet metal’s physical waste. However, the operating time, the number of movements to cut all rectangles edges, and the cutting process quality, must be considered. The objective of this research was to compare the machining torch movement behavior using optimal and alternative solutions for rectangular plasma sheet metal cuts. A bottom-left-fill heuristic and a tabu search heuristic were used to find the alternative solution, while the optimal solution was obtained with a mixed-integer linear programming. The comparison was developed considering three parameters: the total processing time, the effective distance traveled by the machining torch to cut the metal, and the movement distance traveled by the machining torch without cutting the metal. The packing layout given by alternative solutions can reduce the operational cutting processing time and the distance covered by the machining torch movement. From an economic perspective, optimal solutions are recommended when compared to alternative solutions given the lower expenses with raw material waste.

show abstract

Data mining based framework to assess solution quality for the rectangular 2D strip-packing problem

Cited by 20 publications

References 46 publications

Management and Production Engineering Review

Management and Production Engineering Review

Impact of minimum distance constraints on sheet metal waste for plasma cutting

The machining torch movement for the rectangular plasma sheet metal cut

Contact Info

Product

Resources

About