The Improvement of a Hybrid Genetic Algorithm for Three Dimensional Bin-Packing Problems

2012 ◽  
Vol 200 ◽  
pp. 470-473
Author(s):  
Zhen Zhai ◽  
Li Chen ◽  
Xiao Min Han
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Three Dimensional ◽  
Packing Problem ◽  
Flow Process ◽  
Packing Problems ◽  
Loading Problem ◽  
The Cost ◽  
Process Diagrams

The multi-constrained bi-objective bin packing problem has many extensive applications. In the loading section of logistics it has mainly been transported by truck. The cost of transportation is not only determined by the bin space utilization, but also by the number of vehicles in transporta¬tion utilization. The type of items and bins is introduced in the mathematical model, as well as the volume of the items. In this paper, the hybrid genetic algorithm which tabu and simulated annealed rules are added for complex container-loading problem is studied. The effective coding and decod-ing method together with flow process diagrams are given.

scholarly journals Load Planning of Transport Aircraft Based on Hybrid Genetic Algorithm

2018 ◽  
Vol 179 ◽  
pp. 01007
Author(s):  
Yang Chenguang ◽  
Liu Hu ◽  
Gao Yuan
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Hybrid Genetic Algorithm ◽  
Transport Costs ◽  
Gravity Center ◽  
Transport Aircraft ◽  
Load Planning ◽  
Volume Weight ◽  
Loading Problem

Loading of transport aircraft attracts much attention as the airlift is developing rapidly. It refers to the process that various cargoes are loaded, in an appropriate manner, into kinds of transport aircrafts with constraints of volume, weight and gravity center. Based on two-dimensional bin packing with genetic algorithm (GA), a new hybrid algorithm is proposed to solve the multi-constraint loading problem of transport aircraft for seeking the minimum of fuel consumption. Heuristic algorithm is applied to optimize single-aircraft loading in GA decoding, and the procedure of hybrid GA is summarized for the multi-aircraft loading issues. In the case study, eight kinds of cargos are distributed in three different aircrafts. The optimal result indicates that this algorithm can rapidly generate the best plan for the loading problem regarding lower transport costs.

scholarly journals Erratum to “The Three-Dimensional Bin Packing Problem”: Robot-Packable and Orthogonal Variants of Packing Problems

2005 ◽  
Vol 53 (4) ◽  
pp. 735-736 ◽  
Author(s):  
Edgar den Boef ◽  
Jan Korst ◽  
Silvano Martello ◽  
David Pisinger ◽  
Daniele Vigo
Keyword(s):  
Bin Packing ◽  
Three Dimensional ◽  
Packing Problem ◽  
Packing Problems ◽  
Bin Packing Problem

GENERALIZED FIRST-FIT ALGORITHMS IN TWO AND THREE DIMENSIONS

1990 ◽  
Vol 01 (02) ◽  
pp. 131-150 ◽  
Author(s):  
KEQIN LI ◽  
KAM-HOI CHENG
Keyword(s):  
Bin Packing ◽  
Three Dimensional ◽  
Packing Problem ◽  
Three Dimensions ◽  
Performance Ratio ◽  
Dimensional Case ◽  
Performance Bound ◽  
Worst Case ◽  
Packing Problems ◽  
The One

We investigate the two and three dimensional bin packing problems, i.e., packing a list of rectangles (boxes) into unit square (cube) bins so that the number of bins used is a minimum. A simple on-line packing algorithm for the one dimensional bin packing problem, the First-Fit algorithm, is generalized to two and three dimensions. We first give an algorithm for the two dimensional case and show that its asymptotic worse case performance ratio is [Formula: see text]. The algorithm is then generalized to the three dimensional case and its performance ratio [Formula: see text]. The second algorithm takes a parameter and we prove that by choosing the parameter properly, it has an asymptotic worst case performance bound which can be made as close as desired to 1.72=2.89 and 1.73=4.913 respectively in two and three dimensions.

scholarly journals A novel bottom-left packing genetic algorithm for analog module placement

2003 ◽  
Vol 1 ◽  
pp. 191-196 ◽  
Author(s):  
L. Zhang ◽  
U. Kleine
Keyword(s):  
Genetic Algorithm ◽  
Integrated Circuits ◽  
Cost Function ◽  
Bin Packing ◽  
Optimal Parameter ◽  
Packing Problem ◽  
Fractional Factorial ◽  
Genetic Encoding ◽  
The Cost ◽  
Parameter Values

Abstract. This paper presents a novel genetic algorithm for analog module placement. It is based on a generalization of the two-dimensional bin packing problem. The genetic encoding and operators assures that all constraints of the problem are always satisfied. Thus the potential problems of adding penalty terms to the cost function are eliminated, so that the search configuration space decreases drastically. The dedicated cost function covers the special requirements of analog integrated circuits. A fractional factorial experiment was conducted using an orthogonal array to study the algorithm parameters. A meta-GA was applied to determine the optimal parameter values. The algorithm has been tested with several local benchmark circuits. The experimental results show this promising algorithm makes the better performance than simulated annealing approach with the satisfactory results comparable to manual placement.

scholarly journals Two and three-dimensional bin packing problems : an efficient implementation of evolutionary algorithms

2018 ◽  
Author(s):  
◽  
Andile Ntanjana
Keyword(s):  
Genetic Algorithm ◽  
Polynomial Time ◽  
Heuristic Algorithm ◽  
Bin Packing ◽  
Optimization Problems ◽  
Research Work ◽  
Three Dimensional ◽  
Second Phase ◽  
Packing Problems ◽  
Elitist Strategy

The present research work deals with the implementation of heuristics and genetic algo- rithms to solve various bin packing problems (BPP). Bin packing problems are a class of optimization problems that have numerous applications in the industrial world, ranging from efficient cutting of material to packing various items in a larger container. Bin packing problems are known to be non-deterministic polynomial-time hard (NP-hard), and hence it is impossible to solve them exactly in polynomial time. Thus heuristics are very important to design practical algorithms for such problems. In this research we avoid the use of linear programming because we consider it to be a very cumbersome approach for analysing these types of problems and instead we proposed a simple and very efficient algorithm which is a combination of the fi fi heuristic algorithm in combination with the genetic algorithm, to solve the two and three – dimensional bin packing problems. The packing was carried out in two phases, wherein the fi phase the bins are packed by means of the fi fi heuristic algorithm with the help of other auxiliary techniques, and in the second phase the genetic algorithm is implemented. The purpose of the second phase is to improve the initial arrangements by performing combinatorial optimization for either a limited number of bins or the whole set at one time without destroying the original pattern (elitist strategy). The programming code developed can be used to write high-speed and capable software, which can be used in real-time applications. To conclude, the developed optimization ap- proach signifi tly helps to handle the bin packing problem. Numerical results obtained by optimizing existing industrial problems demonstrated that in many cases it was possible to achieve the optimum solution within only a few seconds, whereas for large-scale complex problems the result was near optimum efficiency over 90% within the same period of time.

scholarly journals Real-polarized genetic algorithm for the three-dimensional bin packing problem

2017 ◽  
Author(s):  
André Homem Dornas ◽  
Flávio Vinícius Cruzeiro Martins ◽  
João Fernando Machry Sarubbi ◽  
Elizabeth Fialho Wanner
Keyword(s):  
Genetic Algorithm ◽  
Bin Packing ◽  
Three Dimensional ◽  
Packing Problem ◽  
Bin Packing Problem
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