A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem

Terashima-Marín, Hugo; Zárate, C. J. Farías; Ross, Peter; Valenzuela-Rendón, Manuel

doi:10.1145/1143997.1144102

Cited by 23 publications

(17 citation statements)

References 18 publications

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“…These seven heuristics are all the single-pass selection heuristics that we could get in the literature for the offline BPP. These selection heuristics are mainly associated with rectangles in the literature (Hopper and Turton 2002;Ross et al 2002;Terashima-Marín et al 2006). To implement them with irregular shapes we need to employ adequate functions for shape movement and feasibility check (Sect.…”

Section: Worst Fit (Wf) Same As Heuristic Bf But Places the Piece Inmentioning

confidence: 99%

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

et al. 2013

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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.

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Section: Worst Fit (Wf) Same As Heuristic Bf But Places the Piece Inmentioning

confidence: 99%

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

et al. 2013

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“…Recently, Terashima et al used a combination between low level heuristics and a Genetic Algorithm for dynnamic variable ordering in CSP but did not incorporate the concept of hyper-heuristic [22]. More recently, Terashima et al also used a GA based method to produce hyper-heuristics for the 2-D binpacking problem with encouraging results [23].…”

Section: Solution Approachmentioning

confidence: 99%

“…The solution model used in this investigation carries features from previous work by Ross et al [19] and Terashima et al [23], in which the main focus is to solve one dimensional and two dimensional bin-packing problems, respectively. In the research presented in this article, a GA with variablelength individuals is proposed to find a combination of single heuristics to order variables to solve efficiently a wide variety of instances of CSP.…”

Section: Combining Heuristics With the Proposed Gamentioning

confidence: 99%

Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems

Terashima-Marín

Ortíz-Bayliss

Ross

et al. 2008

Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation

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The idea behind hyper-heuristics is to discover some combination of straightforward heuristics to solve a wide range of problems. To be worthwhile, such combination should outperform the single heuristics. This paper presents a GAbased method that produces general hyper-heuristics for the dynamic variable ordering within Constraint Satisfaction Problems. The GA uses a variable-length representation, which evolves combinations of condition-action rules producing hyper-heuristics after going through a learning process which includes training and testing phases. Such hyper-heuristics, when tested with a large set of benchmark problems, produce encouraging results for most of the cases. The testebed is composed of problems randomly generated using an algorithm proposed by Prosser [17].

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“…There are two kinds of stocks in (1D-CSP) [3]: a. A Standard One-Dimensional Cutting Stock Problem (S1D-CSP) is known as an Non-Deterministic Polynomial (NP) complete one, as shown in Figure (1.a).…”

Section: Introductionmentioning

confidence: 99%

Optimal Utilization of Rebar Stock for Cutting Processes in Housing Project

Al-Zubaidy¹,

Dawood²,

Khalaf³

2016

International Advanced Research Journal in Science, Engineering

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Abstract:The cutting processes of rebar stock in housing project cause a large amount of waste because of high variety between required rebar lengths and standard rebar lengths, this problem is called one dimensional cutting stock problem. This research aims to utilize the rebar stock and minimize the losses using integer linear programming approach-aided by Advanced Interactive Mathematical Modeling System (AIMMS) software for solving the problems. These one-dimensional cutting rebar stock problems are divided into 19-problem according to the variety in the rebar diameter (25 mm, 16 mm, 12 mm, and 10 mm), and the variety in the design of activities in that housing project. The losses that are produced for each activity are utilized in the next activity when the length of item losses is larger than the required small item lengths in next activity for the same rebar diameters. That makes the utilization is to be maximized, where the Utilization Stock Ratio is reached to (98.53%).

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A GA-based method to produce generalized hyper-heuristics for the 2D-regular cutting stock problem

Cited by 23 publications

References 18 publications

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

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

Hyper-heuristics for the dynamic variable ordering in constraint satisfaction problems

Optimal Utilization of Rebar Stock for Cutting Processes in Housing Project

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