scholarly journals A Parallel Biased Random-Key Genetic Algorithm with Multiple Populations Applied to Irregular Strip Packing Problems

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Bonfim Amaro Júnior ◽  
Plácido Rogério Pinheiro ◽  
Pedro Veras Coelho
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Benchmark Problems ◽  
Strip Packing ◽  
Layout Problem ◽  
Multiple Populations ◽  
Packing Problems ◽  
Free Region ◽  
Positioning Method ◽  
The Given

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 (µ-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 (µ-BRKGA) algorithm, computational tests using benchmark problems were applied, analyzed, and compared with different approaches.

scholarly journals Dealing with Nonregular Shapes Packing

2014 ◽  
Vol 2014 ◽  
pp. 1-10 ◽  
Author(s):  
Bonfim Amaro Júnior ◽  
Plácido Rogério Pinheiro ◽  
Rommel Dias Saraiva ◽  
Pedro Gabriel Calíope Dantas Pinheiro
Keyword(s):  
Genetic Algorithm ◽  
Case Studies ◽  
Textile Industry ◽  
Packing Problem ◽  
Benchmark Problems ◽  
Practical Case ◽  
Two Dimensional ◽  
Cutting And Packing ◽  
Strip Packing ◽  
Bottom Left

This paper addresses the irregular strip packing problem, a particular two-dimensional cutting and packing problem in which convex/nonconvex shapes (polygons) have to be packed onto a single rectangular object. We propose an approach that prescribes the integration of a metaheuristic engine (i.e., genetic algorithm) and a placement rule (i.e., greedy bottom-left). Moreover, a shrinking algorithm is encapsulated into the metaheuristic engine to improve good quality solutions. To accomplish this task, we propose a no-fit polygon based heuristic that shifts polygons closer to each other. Computational experiments performed on standard benchmark problems, as well as practical case studies developed in the ambit of a large textile industry, are also reported and discussed here in order to testify the potentialities of proposed approach.

Development of the Multimethod Genetic Algorithm for the Strip Packing Problem

2014 ◽  
Vol 598 ◽  
pp. 377-381 ◽  
Author(s):  
Vladislav A. Chekanin ◽  
Alexander V. Chekanin
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Strong Sense ◽  
Np Hard ◽  
Two Dimensional ◽  
Strip Packing ◽  
Packing Problems ◽  
Total Length

The actual in industry strip packing problem which is NP-hard in strong sense is considered in paper. To the strip packing problem comes down solution of a large number of different practical problems, including problems in logistics, scheduling and planning. The new heuristics intended to pack a given set of rectangular two-dimensional objects in order to minimize of the total length of the filled part of container with an infinity length and fixed width are offered. The proposed multimethod genetic algorithm is investigated on well-known standard benchmarks of two-dimensional strip packing problems.

An algorithm for the strip packing problem using collision free region and exact fitting placement

2012 ◽  
Vol 44 (8) ◽  
pp. 766-777 ◽  
Author(s):  
André Kubagawa Sato ◽  
Thiago Castro Martins ◽  
Marcos Sales Guerra Tsuzuki
Keyword(s):  
Packing Problem ◽  
Strip Packing ◽  
Free Region

Parameter Analysis of Placement Function for the Rectangular Packing Problem Based on GA

2016 ◽  
Vol 836-837 ◽  
pp. 381-386
Author(s):  
Yan Hua Zhu
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Parameter Analysis ◽  
Algorithm Analysis ◽  
Feasible Region ◽  
Careful Study ◽  
Algorithm Optimization ◽  
Rectangle Packing ◽  
Golden Horn ◽  
Positioning Method

Rectangle part is the foundation of irregular part layout, about which domestic and overseas scholars have studied a lot and have put forward many algorithms. Based on a careful study of these algorithms in the paper, it is determined to solve the rectangle packing problem with genetic algorithm analysis. Different from the formerly used the genetic algorithm optimization layout, the algorithm of this paper stresses optimization localization rule in order to solve layout problem with the advantages of global searching ability of genetic algorithm. This algorithm sets the utilization ratio of the maximum area of capacity as the goal, after confirming the priority of deposition sequence of rectangle, in view of the locating rule of rectangle packing, based on feasible region and by introducing the method of attractive factors, optimizing the calculation for each parameter of placement function by utilizing genetic algorithm.Positioning function of the structure of this paper by changing the parameter value can cover the previous golden horn strategy, the lower left corner strategy, down the steps such as positioning method. Use VC programming to realize automatic two dimensional rectangular layout systems, the algorithm and example verification, the precision of parameter on the result of layout, the number of attractor for layout results, and the influence of parameter values for different rectangular piece of regularity. According to different rectangular block configuration, layout scheme can be better and faster.

scholarly journals A STRIP PACKING SOLVING METHOD USING AN INCREMENTAL MOVE BASED ON MAXIMAL HOLES

2008 ◽  
Vol 17 (05) ◽  
pp. 881-901 ◽  
Author(s):  
BERTRAND NEVEU ◽  
GILLES TROMBETTONI ◽  
IGNACIO ARAYA ◽  
MARIA-CRISTINA RIFF
Keyword(s):  
Local Search ◽  
Bin Packing ◽  
Packing Problem ◽  
Experimental Results ◽  
Greedy Heuristics ◽  
Strip Packing ◽  
Packing Problems ◽  
Alternative Approach ◽  
Bottom Left ◽  
Complete Optimization

When handling 2D packing problems, numerous incomplete and complete algorithms maintain a so-called bottom-left (BL) property: no rectangle placed in a container can be moved more left or bottom. While it is easy to make a rectangle BL when it is added in a container, it is more expensive to maintain all the placed pieces BL when a rectangle is removed. This prevents researchers from designing incremental moves for metaheuristics or efficient complete optimization algorithms. This paper investigates the possibility of violating the BL property. Instead, we propose to maintain the set of maximal holes, which allows incremental additions and removals of rectangles. To validate our alternative approach, we have designed an incremental move, maintaining maximal holes, for the strip packing problem, a variant of the famous 2D bin-packing. We have also implemented a metaheuristic, with no user-defined parameter, using this move and standard greedy heuristics. We have finally designed two variants of this incomplete method. In the first variant, a better first layout is provided by a hyperheuristic proposed by some of the authors. In the second variant, a fast repacking procedure recovering the BL property is occasionally called during the local search. Experimental results show that the approach is competitive with the best known incomplete algorithms.

A biased random-key genetic algorithm using dotted board model for solving two-dimensional irregular strip packing problems

2020 ◽  
Author(s):  
Bonfim Amaro Junior ◽  
Rodrigo Lima da Costa ◽  
Placido Rogerio Pinheiro ◽  
Luiz Jonata Pires de Araujo ◽  
Alexandr Grichshenko
Keyword(s):  
Genetic Algorithm ◽  
Two Dimensional ◽  
Strip Packing ◽  
Packing Problems

Two-Dimensional Packing Problems Using Genetic Algorithms

1996 ◽  
Author(s):  
Sakait Jain ◽  
Hae Chang Gea
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Finite Number ◽  
Packing Problem ◽  
Two Dimensional ◽  
Connectivity Analysis ◽  
Packing Problems ◽  
The Creation ◽  
New Feature ◽  
Number Of Cells

Abstract This paper presents a technique for applying genetic algorithms for the two dimensional packing problem. The approach is applicable to not only convex shaped objects, but, can also accommodate any type of concave and complex shaped objects including objects with holes. In this approach, a new concept of a two dimensional genetic chromosome is introduced. The total layout space is divided into a finite number of cells for mapping it into this 2-D genetic algorithm chromosome. The mutation and crossover operators have been modified and are applied in conjunction with connectivity analysis for the objects to reduce the creation of faulty generations. A new feature has been added to the genetic algorithm(GA) in the form of a new operator called compaction. Several examples of GA based layout are presented.

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