A parallel biased random-key genetic algorithm with multiple populations applied to irregular strip packing problems
type: Item
instance of: scholarly article
title: A parallel biased random-key genetic algorithm with multiple populations applied to irregular strip packing problems
zbMATH Open document ID: 1426.90173
DOI: 10.1155/2017/1670709
author: Bonfim Amaro Júnior
author: Pedro Veras Coelho
author: Placido Rogerio Pinheiro
published in: Mathematical Problems in Engineering
publication date: 2018-11-05T00:00:00Z
review text: Summary: The irregular strip packing problem (ISPP) is a class of cutting and packing problem (C\&P) in which a set of items with arbitrary formats must be placed in a container with a variable length. The aim of this work is to minimize the area needed to accommodate the given demand. ISPP is present in various types of industries from manufacturers to exporters (e.g., shipbuilding, clothes, and glass). In this paper, we propose a parallel Biased Random-Key Genetic Algorithm \(( \mu \)-BRKGA) with multiple populations for the ISPP by applying a collision-free region (CFR) concept as the positioning method, in order to obtain an efficient and fast layout solution. The layout problem for the proposed algorithm is represented by the placement order into the container and the corresponding orientation. In order to evaluate the proposed \(( \mu \)-BRKGA) algorithm, computational tests using benchmark problems were applied, analyzed, and compared with different approaches.
Mathematics Subject Classification ID: 90B80
Mathematics Subject Classification ID: 90C27
Mathematics Subject Classification ID: 68T05
zbMATH DE Number: 6971782
describes a project that uses: TOPOS
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2017/1670709
OpenAlex ID: W2754611409
cites work: An improved typology of cutting and packing problems
cites work: TOPOS -- A new constructive algorithm for nesting problems
cites work: Orthogonal Packings in Two Dimensions
cites work: Solving nesting problems with non-convex polygons by constraint logic programming
cites work: Mixed-integer programming models for nesting problems
cites work: Solution approaches to irregular nesting problems
cites work: On genetic algorithms for the packing of polygons
cites work: Dealing with nonregular shapes packing
cites work: A generic approach for nesting of 2-D parts in 2-D sheets using genetic and heuristic algorithms
cites work: A 2-exchange heuristic for nesting problems
cites work: A New Bottom-Left-Fill Heuristic Algorithm for the Two-Dimensional Irregular Packing Problem
cites work: An iterated local search algorithm based on nonlinear programming for the irregular strip packing problem
cites work: Extended local search algorithm based on nonlinear programming for two-dimensional irregular strip packing problem
cites work: Fast neighborhood search for two- and three-dimensional nesting problems
cites work: Solving irregular strip packing problems by hybridising simulated annealing and linear program\-ming
cites work: A Solution of the Rectangular Cutting-Stock Problem
cites work: A beam search implementation for the irregular shape packing problem
cites work: Polygon decomposition for efficient construction of Minkowski sums
cites work: Genetic Algorithms and Random Keys for Sequencing and Optimization
cites work: A new approach for sheet nesting problem using guided cuckoo search and pairwise clustering
Recommended article: A parallel multi-population biased random-key genetic algorithm for a container loading problem
Recommended article: Resolution of strip-packing problems with genetic algorithms
Recommended article: A genetic algorithm for the two-dimensional strip packing problem with rectangular pieces
Recommended article: An Improved Heuristic Recursive Strategy Based on Genetic Algorithm for the Strip Rectangular Packing Problem
Recommended article: A biased random-key genetic algorithm for the container pre-marshalling problem
Recommended article: Optimisation of a multi-objective two-dimensional strip packing problem based on evolutionary algorithms
description: scientific article; zbMATH DE number 6971782