A genetic algorithm for a 2D industrial packing problem

Hopper, E. Mitchell; Turton, Brian

doi:10.1016/s0360-8352(99)00097-2

Cited by 131 publications

(55 citation statements)

References 6 publications

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“…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

104

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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.

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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

104

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“…Examples of their success can be found in areas as diverse as 2D packing (Hopper & Turton, 1999), protein folding simulations (Unger & Moult, 1993) and jazz improvisation (Biles, 1994).…”

Section: Genetic Algorithms and Interactive Genetic Algorithmsmentioning

confidence: 99%

The evolution of a visual-to-auditory sensory substitution device using interactive genetic algorithms

Wright

Ward

2013

Quarterly Journal of Experimental Psychology

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Sensory substitution is a promising technique for mitigating the loss of a sensory modality. Sensory substitution devices (SSDs) work by converting information from the impaired sense (e.g., vision) into another, intact sense (e.g., audition). However, there are a potentially infinite number of ways of converting images into sounds, and it is important that the conversion takes into account the limits of human perception and other user-related factors (e.g., whether the sounds are pleasant to listen to). The device explored here is termed “polyglot” because it generates a very large set of solutions. Specifically, we adapt a procedure that has been in widespread use in the design of technology but has rarely been used as a tool to explore perception—namely, interactive genetic algorithms. In this procedure, a very large range of potential sensory substitution devices can be explored by creating a set of “genes” with different allelic variants (e.g., different ways of translating luminance into loudness). The most successful devices are then “bred” together, and we statistically explore the characteristics of the selected-for traits after multiple generations. The aim of the present study is to produce design guidelines for a better SSD. In three experiments, we vary the way that the fitness of the device is computed: by asking the user to rate the auditory aesthetics of different devices (Experiment 1), and by measuring the ability of participants to match sounds to images (Experiment 2) and the ability to perceptually discriminate between two sounds derived from similar images (Experiment 3). In each case, the traits selected for by the genetic algorithm represent the ideal SSD for that task. Taken together, these traits can guide the design of a better SSD.

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“…Due to its robustness, it's particularly suited to deal with the complicated and non-linear problems that traditional search algorithms are hard to solve [2].Genetic algorithm develops from Darwin's theory of evolution thought, genetic algorithm uses a natural evolutionary model, according to the "struggle for existence" and "survival of the fittest" principle, through reproduction, crossover, mutation, so it can solve the problem from the initial solution then step by step and approach the optimal solution [3,4].Early garment CAD algorithms mainly use this algorithm, but for the effect is not so good when doing complex marker making .…”

Section: A Genetic Algorithmmentioning

confidence: 99%

Research of improved particle swarm algorithm based on apparel intelligent marker making

Huang¹

2013

Proceedings of the 2nd International Conference on Computer Science and Electronics Engineering (ICCSEE 2013)

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Abstract-The intelligent algorithm of apparel marker making is the core technology of apparel marker making, this paper brings forward an improved particle swarm algorithm at the base of analyzing the merit and demerit of genetic algorithm, simulated annealing algorithm and particle swarm optimization algorithm, furthermore, a demonstration test is done by C++ under the environment of VS2005. The result indicates the improved particle swarm algorithm can achieve ideal material utilization.Keywords-apparel intelligent marker making; genetic algorithm; simulated annealing algorithm; improved particle swarm algorithm I. INTRODUCION Garment marker making technology is one of the important technologies of clothing enterprises, the operation time of marker process and the utilization ratio of maker making directly affect the enterprise's cost, profit, product competition ability and economic benefits. Marker making technology has been used as the most important way to raise the material utilization ratio, reduce the material loss and production cost for garment manufacturing enterprises in the production process .Therefore, a set of intelligent and efficient marker making technology can not only reduce the staff's working strength, but also greatly improve the producing speed and the utilization ratio of material, and but also bring considerable economic benefit to enterprises.With the development and popularization of garment CAD software, the core technology of garment CAD, the intelligent marker algorithm has been improved also. The optimization algorithms which can solve the maker problem conclude artificial neural network, genetic algorithm, heuristic algorithm and simulated annealing algorithm. Therein, the artificial neural network is more difficult to solve optimal marker problem, as it's not suitable for non rectangular pieces; and as genetic algorithm has no special restrictions to its required problem, and more facilitate for organically integration of rectangular parts graphic arithmetic and genetic algorithm, so there are many solutions can solve the optimal marker problem. But the genetic algorithm has limitation which we call the premature phenomenon, and its local searching ability is poor; simulated annealing algorithm has strong overall search ability. In a word, marker making problem is a typical NP complete problem, namely the time complexity of finding the solution is in exponential order. In the relatively large size of the problem, the loss of viability often occurs because of the increasing computing time and, so it is necessary to find other feasible scheme. This paper introduces a method that based on particle swarm algorithm, and does experiment at the same time analysis its effectiveness.

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A genetic algorithm for a 2D industrial packing problem

Cited by 131 publications

References 6 publications

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

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

The evolution of a visual-to-auditory sensory substitution device using interactive genetic algorithms

Research of improved particle swarm algorithm based on apparel intelligent marker making

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