A genetic algorithm for the two-dimensional strip packing problem with rectangular pieces

2006 ◽  
Vol 172 (3) ◽  
pp. 814-837 ◽  
Author(s):  
Andreas Bortfeldt
Keyword(s):  
Genetic Algorithm ◽  
Packing Problem ◽  
Two Dimensional ◽  
Strip Packing

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.

scholarly journals A SAT-based Method for Solving the Two-dimensional Strip Packing Problem

2010 ◽  
Vol 102 (3-4) ◽  
pp. 467-487 ◽  
Author(s):  
Takehide Soh ◽  
Katsumi Inoue ◽  
Naoyuki Tamura ◽  
Mutsunori Banbara ◽  
Hidetomo Nabeshima
Keyword(s):  
Packing Problem ◽  
Two Dimensional ◽  
Strip Packing

A Reinforced Tabu Search Approach for 2D Strip Packing

2012 ◽  
pp. 171-188
Author(s):  
Giglia Gómez-Villouta ◽  
Jean-Philippe Hamiez ◽  
Jin-Kao Hao
Keyword(s):  
Tabu Search ◽  
Search Algorithm ◽  
Fitness Function ◽  
Packing Problem ◽  
Two Dimensional ◽  
Strip Packing ◽  
Benchmark Instances ◽  
Finite Set ◽  
Infinite Height ◽  
Search Approach

This paper discusses a particular “packing” problem, namely the two dimensional strip packing problem, where a finite set of objects have to be located in a strip of fixed width and infinite height. The variant studied considers regular items, rectangular to be precise, that must be packed without overlap, not allowing rotations. The objective is to minimize the height of the resulting packing. In this regard, the authors present a local search algorithm based on the well-known tabu search metaheuristic. Two important components of the presented tabu search strategy are reinforced in attempting to include problem knowledge. The fitness function incorporates a measure related to the empty spaces, while the diversification relies on a set of historically “frozen” objects. The resulting reinforced tabu search approach is evaluated on a set of well-known hard benchmark instances and compared with state-of-the-art algorithms.

Improved metaheuristics for the two-dimensional strip packing problem

2020 ◽  
Vol 92 ◽  
pp. 106268 ◽  
Author(s):  
Rosephine G. Rakotonirainy ◽  
Jan H. van Vuuren
Keyword(s):  
Packing Problem ◽  
Two Dimensional ◽  
Strip Packing

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.

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