On Performance of Binary Flower Pollination Algorithm for Rectangular Packing Problem

2020 ◽  
Vol 13 (1) ◽  
pp. 22-34
Author(s):  
Amandeep K. Virk ◽  
Kawaljeet Singh
Keyword(s):  
Packing Problem ◽  
Optimal Placement ◽  
Flower Pollination Algorithm ◽  
Utilization Factor ◽  
Rectangle Packing ◽  
Flower Pollination ◽  
Benchmark Datasets ◽  
Rectangular Packing ◽  
Industrial Problem ◽  
Rectangle Packing Problem

Background: Metaheuristic algorithms are optimization algorithms capable of finding near-optimal solutions for real world problems. Rectangle Packing Problem is a widely used industrial problem in which a number of small rectangles are placed into a large rectangular sheet to maximize the total area usage of the rectangular sheet. Metaheuristics have been widely used to solve the Rectangle Packing Problem. Objective: A recent metaheuristic approach, Binary Flower Pollination Algorithm, has been used to solve for rectangle packing optimization problem and its performance has been assessed. Methods: A heuristic placement strategy has been used for rectangle placement. Then, the Binary Flower Pollination Algorithm searches the optimal placement order and optimal layout. Results: Benchmark datasets have been used for experimentation to test the efficacy of Binary Flower Pollination Algorithm on the basis of utilization factor and number of bins used. The simulation results obtained show that the Binary Flower Pollination Algorithm outperforms in comparison to the other well-known algorithms. Conclusion: BFPA gave superior results and outperformed the existing state-of-the-art algorithms in many instances. Thus, the potential of a new nature based metaheuristic technique has been discovered.

Application of Nature Inspired Algorithms to Optimize Multi-objective Two-Dimensional Rectangle Packing Problem

2019 ◽  
Vol 04 (04) ◽  
pp. 1950010
Author(s):  
Amandeep Kaur Virk ◽  
Kawaljeet Singh
Keyword(s):  
Production Process ◽  
Bin Packing ◽  
Performance Metrics ◽  
Packing Problem ◽  
Flower Pollination Algorithm ◽  
Two Dimensional ◽  
Due Dates ◽  
Utilization Factor ◽  
Multi Objective ◽  
Metaheuristic Techniques

This paper considers two-dimensional non-guillotine rectangular bin packing problem with multiple objectives in which small rectangular parts are to be arranged optimally on a large rectangular sheet. The optimization of rectangular parts is attained with respect to three objectives involving maximization of (1) utilization factor, minimization of (2) due dates of rectangles and (3) number of cuts. Three nature based metaheuristic algorithms — Cuckoo Search, Bat Algorithm and Flower Pollination Algorithm — have been used to solve the multi-objective packing problem. The purpose of this work is to consider multiple industrial objectives for improving the overall production process and to explore the potential of the recent metaheuristic techniques. Benchmark test data compare the performance of recent approaches with the popular approaches and also of the different objectives used. Different performance metrics analyze the behavior/performance of the proposed technique. Experimental results obtained in this work prove the effectiveness of the recent metaheuristic techniques used. Also, it was observed that considering multiple and independent factors as objectives for the production process does not degrade the overall performance and they do not necessarily conflict with each other.

An effective quasi-human based heuristic for solving the rectangle packing problem

2002 ◽  
Vol 141 (2) ◽  
pp. 341-358 ◽  
Author(s):  
Yu-Liang Wu ◽  
Wenqi Huang ◽  
Siu-chung Lau ◽  
C.K Wong ◽  
Gilbert H Young
Keyword(s):  
Packing Problem ◽  
Rectangle Packing ◽  
Rectangle Packing Problem

A NEW HEURISTIC ALGORITHM FOR CONSTRAINED RECTANGLE-PACKING PROBLEM

2007 ◽  
Vol 24 (04) ◽  
pp. 463-478 ◽  
Author(s):  
DUANBING CHEN ◽  
WENQI HUANG
Keyword(s):  
Heuristic Algorithm ◽  
Heuristic Algorithms ◽  
Packing Problem ◽  
Industrial Applications ◽  
Rectangle Packing ◽  
Simulated Annealing Genetic Algorithm ◽  
Rectangular Container ◽  
Glass Cutting ◽  
Integrated Performance ◽  
Rectangle Packing Problem

The constrained rectangle-packing problem is the problem of packing a subset of rectangles into a larger rectangular container, with the objective of maximizing the layout value. It has many industrial applications such as shipping, wood and glass cutting, etc. Many algorithms have been proposed to solve it, for example, simulated annealing, genetic algorithm and other heuristic algorithms. In this paper a new heuristic algorithm is proposed based on two strategies: the rectangle selecting strategy and the rectangle packing strategy. We have applied the algorithm to 21 smaller, 630 larger and other zero-waste instances. The computational results demonstrate that the integrated performance of the algorithm is rather satisfying and the algorithm developed is fairly efficient for solving the constrained rectangle-packing problem.

4-Block heuristic for the rectangle packing problem

1998 ◽  
Vol 108 (3) ◽  
pp. 509-526 ◽  
Author(s):  
Gubtram Scheithauer ◽  
Uta Sommerweiß
Keyword(s):  
Packing Problem ◽  
Rectangle Packing ◽  
Rectangle Packing Problem

scholarly journals A new nonlinear model for the two-dimensional rectangle packing problem

2013 ◽  
Vol 93 (107) ◽  
pp. 95-107
Author(s):  
Aleksandar Savic ◽  
Jozef Kratica ◽  
Vladimir Filipovic
Keyword(s):  
Nonlinear Programming ◽  
Piecewise Linear ◽  
Packing Problem ◽  
Linear Formulation ◽  
Mixed Integer ◽  
Two Dimensional ◽  
Programming Formulation ◽  
Real Numbers ◽  
Rectangle Packing ◽  
Rectangle Packing Problem

This paper deals with the rectangle packing problem, of filling a big rectangle with smaller rectangles, while the rectangle dimensions are real numbers. A new nonlinear programming formulation is presented and the validity of the formulation is proved. In addition, two cases of the problem are presented, with and without rotation of smaller rectangles by 90?. The mixed integer piecewise linear formulation derived from the model is given, but with a simple form of the objective function.

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