2021
DOI: 10.1371/journal.pone.0245267
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Exact solutions for the 2d-strip packing problem using the positions-and-covering methodology

Abstract: We use the Positions and Covering methodology to obtain exact solutions for the two-dimensional, non-guillotine restricted, strip packing problem. In this classical NP-hard problem, a given set of rectangular items has to be packed into a strip of fixed weight and infinite height. The objective consists in determining the minimum height of the strip. The Positions and Covering methodology is based on a two-stage procedure. First, it is generated, in a pseudo-polynomial way, a set of valid positions in which an… Show more

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Cited by 5 publications
(6 citation statements)
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“…A hybrid metaheuristic approach that relies on a local search algorithm to deliver satisfying rectangles placements on the horizontal axis and an exact procedure to find rectangles positions on the vertical axis is shown in [ 23 ] for the 2D-SPP, being competitive on moderate-sized instances when compared to the best-known approaches. Also, [ 24 ] used the positions and covering methodology to obtain exact solutions for the 2D-SPP, generating a set of valid positions in which a rectangle can be packed into the strip and, with a set-covering formulation, selecting the best configuration of rectangles. Also, the method was found to be useful for small and medium-size instances, as well as proving optimality for new literature instances.…”
Section: Research Frameworkmentioning
confidence: 99%
“…A hybrid metaheuristic approach that relies on a local search algorithm to deliver satisfying rectangles placements on the horizontal axis and an exact procedure to find rectangles positions on the vertical axis is shown in [ 23 ] for the 2D-SPP, being competitive on moderate-sized instances when compared to the best-known approaches. Also, [ 24 ] used the positions and covering methodology to obtain exact solutions for the 2D-SPP, generating a set of valid positions in which a rectangle can be packed into the strip and, with a set-covering formulation, selecting the best configuration of rectangles. Also, the method was found to be useful for small and medium-size instances, as well as proving optimality for new literature instances.…”
Section: Research Frameworkmentioning
confidence: 99%
“…Condition (6) states that all rectangles R ki (x ki ,y ki ) are placed in a horizontal strip box of height H and length L. According to Condition (5), the optimization goal is to minimize all delay lengths. Condition (8) ensures that the position relations between any two pairs of rectangles can only be one of the four directions, namely, the determinable variables u ri,sj , u sj, ri , v ri,sj , and v sj,ri , and only one of the four position relations can be equal to 1. Condition (7) indicates that all rectangles will not overlap horizontally or vertically; this model can also be taken as a Padberg-type model due to the use of binary variables.…”
Section: Mathematical Modelmentioning
confidence: 99%
“…Deterministic algorithms mainly use models to construct accurate solutions or improve the branch pricing method to improve the optimization ability and efficiency of the algorithm. Cid-Garcia et al [8] used the location and coverage method to obtain the two-dimensional, non-guillotine-constrained 2SP exact solution, generated a set of effective locations in the form of pseudo polynomials, and selected the best configuration of the strip box items by using the set coverage formula. Qi et al [9] studied the relationship between the lower left coordinates of the rectangles and strip boxes and established linear integer programming models of nonrotating and rotating two-dimensional rectangular strip boxes to ensure that the two rectangles would not be placed repeatedly.…”
Section: Introductionmentioning
confidence: 99%
“…Also, for the 2D-SPP, new exact methods and algorithms were proposed in recent years, without considering practical constraints. In [ 29 ], a methodology called Positions and Covering was developed, providing exact and previously unknown solutions for literature instances with validated results and effectiveness for small and medium-size instances. Additionally, [ 30 ] proposed a novel algorithm based on deep reinforcement learning, achieving superior optimization results, and demonstrating high solution efficiency, generalization, and potential for practical applications.…”
Section: Introductionmentioning
confidence: 99%