Complete and robust no-fit polygon generation for the irregular stock cutting problem

Burke, Edmund K.; Hellier, R. S. R.; Kendall, Graham; Whitwell, Glenn

doi:10.1016/j.ejor.2006.03.011

Cited by 132 publications

(83 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The problem can be further classified by the type of items that are to be placed. Whereas the rectangular (or orthogonal) problem involves the placement of rectangles onto rectangular stock sheets (Baker et al 1980, Hopper andTurton 1999), the irregular variant can involve items and sheets of any two-dimensional form that may contain arcs, concavities, and holes (Burke et al 2006), and typically require more sophisticated geometric algorithms (Bennell and Song 2008, Burke et al 2007, Bennell et al 2001. Three-dimensional problems are generally constrained to the packing of boxes into regular container boxes (i.e., pallet or lorry loading), although the packing of irregular three-dimensional forms has also been attempted (Stoyan et al 2002).…”

Section: Introductionmentioning

confidence: 99%

A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem

Burke

Kendall

Whitwell

2009

INFORMS Journal on Computing

106

View full text Add to dashboard Cite

T he best-fit heuristic is a simple yet powerful one-pass approach for the two-dimensional rectangular stockcutting problem. It had achieved the best published results on a wide range of benchmark problems until the development of the approaches described in this paper. Here, we illustrate how improvements in solution quality can be achieved by the hybridisation of the best-fit heuristic together with simulated annealing and the bottom-left-fill algorithm. We compare and contrast the new hybrid approach with other approaches from the literature in terms of execution times and the quality of the solutions achieved. Using a range of standard benchmark problems from the literature, we demonstrate how the new approach achieves significantly better results than previously published methods on almost all of the problem instances. In addition, we provide results on 10 new benchmark problems to encourage further research and greater comparison between current and future methods.

show abstract

Section: Introductionmentioning

confidence: 99%

A Simulated Annealing Enhancement of the Best-Fit Heuristic for the Orthogonal Stock-Cutting Problem

Burke

Kendall

Whitwell

2009

INFORMS Journal on Computing

106

View full text Add to dashboard Cite

show abstract

“…The no-fit polygon gives the set of non-overlapping placements for a given pair of polygons. Good examples of this type of procedure that work well for the strip packing problem are described by Hu-yao and Yuan-jun (2006), Gomes and Oliveira (2002), Dowsland et al (2002), Burke and Kendall (1999b), Burke et al (2007). While the no-fit polygon is a powerful geometric technique, there are several issues that limit its scalability for industrial applications.…”

Section: The 2d Irregular Bin Packing Problemmentioning

confidence: 99%

“…No-fit polygon techniques are notorious for the large quantity of degenerate cases that must be handled to make it completely robust (Burke et al 2006). Whilst the generation of the no-fit polygon is academically challenging, it is a tool and not a solution (Burke et al 2007). After generating the possible placement positions, it is necessary to have some criteria for choosing the best position.…”

Section: The 2d Irregular Bin Packing Problemmentioning

confidence: 99%

An effective heuristic for the two-dimensional irregular bin packing problem

et al. 2013

View full text Add to dashboard Cite

This paper proposes an adaptation, to the two-dimensional irregular bin packing problem of the Djang and Finch heuristic (DJD), originally designed for the one-dimensional bin packing problem. In the two-dimensional case, not only is it the case that the piece's size is important but its shape also has a significant influence. Therefore, DJD as a selection heuristic has to be paired with a placement heuristic to completely construct a solution to the underlying packing problem. A successful adaptation of the DJD requires a routine to reduce computational costs, which is also proposed and successfully tested in this paper. Results, on a wide variety of instance types with convex polygons, are found to be significantly better than those produced by more conventional selection heuristics.

show abstract

“…Li and Milenkovic [14] decomposed the items into star-shaped polygons. Burke et al [15] proposed an orbiting algorithm in which the movable item slides along the fixed item.…”

Section: E-mail Address: Mtsuzuki@uspbr (Msg Tsuzuki)mentioning

confidence: 99%