Hybrid genetic approach for 1-D bin packing problem (original) (raw)
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Hybrid genetic algorithms for bin-packing and related problems
Annals of Operations Research, 1996
The genetic algorithm (GA) paradigm has attracted considerable attention as a promising heuristic approach for solving optimization problems. Much of the development has related to problems of optimizing functions of continuous variables, but recently there have been several applications to problems of a combinatorial nature. What is often found is that GAs have fairly poor performance for combinatorial problems if implemented in a naive way, and most reported work has involved somewhat ad hoc adjustments to the basic method. In this paper, we will describe a general approach which promises good performance for a fairly extensive class of problems by hybridizing the GA with existing simple heuristics. The procedure will be illustrated mainly in relation to the problem of bin-packing, but it could be extended to other problems such as graph-partitioning, parallel-machine scheduling and generalized assignment. The method is further extended by using problem size reduction hybrids. Some results of numerical experiments will be presented which attempt to identify those circumstances in which these heuristics will perform well relative to exact methods. Finally, we discuss some general issues involving hybridization: in particular, we raise the possibility of blending GAs with orthodox mathematical programming procedures.
TWO EVOLUTIONARY HYBRID STAGES FOR THE RECTANGULAR BIN PACKING PROBLEM WITH CONSTRAINTS
The Bin Packing problem is met in several domains of application, especially in the industry of: sheet metal, wood, glass, paper etc. In this article we are interested to the orthogonal cutting problem, with the hold in charge of the constraint of end-to-end cutting, and orientation constraint. The bin packing problem belongs to the class of NP-hard problems; therefore, our work has turned towards heuristic methods of resolution, and more particularly evolutionary methods. The application of genetic algorithms that are part of the evolutionary methods has limitations for solving the bin packing problem with large data. To minimize this disadvantage, we propose an original method which consists in subdividing the initial problem into two sub-problems. The first step tries to apply a hybrid genetic algorithm based on the order of appearance of pieces, to be packed on levels in an infinite band by applying the new placement routine (BLF2G). The second step uses the results of the first, namely the levels, and tries to project them on Bins by applying a second hybrid genetic algorithm. Besides that, we propose a new definition of the problem, it's about seeing the strip not as usual, with a fixed width and infinite height, but with a fixed height and infinite width. And we must apply some improvements, found in the literature, to the classic genetic algorithm to improve results, by introducing greedy heuristics to the population. Results are compared with other heuristic methods on data sets found in the literature.
Bin-Packing Using Genetic Algorithms
… and Computers, 2005 …, 2005
We present in this paper a genetic algorithm (GA) approach to solve 2-D bin packing problems of polygonal shapes on a rectangular canvas. We present how to encode shape parameters and a fitness function based on a the medial axis transform (MAT) to evaluate individuals of a genetic algorithm population. Some test and results of our experimentation are presented.
2021
My sincere thanks go to my two supervisors, Professor Pavel, Y Tabakov, and Professor Sibusiso Moyo, for the encouragement and guidance they have given to me over the last two years. Through their support and trust, I have had the opportunity to direct my part in an interesting and very challenging project, and to enjoy myself along the way. I also thank those who let me describe the technical details of my research to them on numerous occasions. When I do not communicate my thoughts, they often become jumbled in my head and never clearly develop. Having people who would sit and listen as I explained my ideas to them was thus essential in the development of this work. For this I thank Donnah Biyela, Zolisa Dolwana, Happy Christian Tshimbiluni, Aaron Lecheko and Euclid Nkuna. There is a long list of names missing from this list of those with whom I only spoke once briefly about my work, and I would like to thank those people as well.
Application of Genetic Algorithm for the Bin Packing Problem with a New Representation Scheme
The Bin Packing Problem (BPP) is to find the minimum number of bins needed to pack a given set of objects of known sizes so that they do not exceed the capacity of each bin. This problem is known to be NP-Hard [5]; hence many heuristic procedures for its solution have been suggested. In this paper we propose a new representation scheme and solve the problem by a Genetic Algorithm. Limited computational results show the efficiency of this scheme.
Genetic Algorithm With Random Crossover and Dynamic Mutation on Bin Packing Problem
Proceeding of the Electrical Engineering Computer Science and Informatics, 2019
Bin Packing Problem (BPP) is a problem that aims to minimize the number of container usage by maximizing its contents. BPP can be applied to a case, such as maximizing the printing of a number of stickers on a sheet of paper of a certain size. Genetic Algorithm is one way to overcome BPP problems. Examples of the use of a combination of BPP and Genetic Algorithms are applied to printed paper in Digital Printing companies. Genetic Algorithms adopt evolutionary characteristics, such as selection, crossover and mutation. Repeatedly, Genetic Algorithms produce individuals who represent solutions. However, this algorithm often does not achieve maximum results because it is trapped in a local search and a case of premature convergence. The best results obtained are not comprehensive, so it is necessary to modify the parameters to improve this condition. Random Crossover and Dynamic Mutation were chosen to improve the performance of Genetic Algorithms. With this application, the performance of the Genetic Algorithm in the case of BPP can overcome premature convergence and maximize the allocation of printing and the use of paper. The test results show that an average of 99 stickers can be loaded on A3 + size paper and the best generation is obtained on average in the 21 st generation and the remaining space is 3,500mm 2 .
Packing Bins Using Multi-chromosomal Genetic Representation and Better-Fit Heuristic
Neural Information Processing
We propose a multi-chromosome genetic coding and set-based genetic operators for solving bin packing problem using genetic algorithm. A heuristic called better-fit is proposed, in which a left-out object replaces an existing object from a bin if it can fill the bin better. Performance of the genetic algorithm augmented with the better-fit heuristic has been compared with that of hybrid grouping genetic algorithm (HGGA). Our method has provided optimal solutions at highly reduced computational time for the benchmark uniform problem instances used. The better-fit heuristic is more effective compared to the best-fit heuristic when combined with the coding.
A Hybrid Evolutionary Algorithm for the Sequencing m-Vector Bin Packing Problem
Journal of Advances in Information Technology
In this paper, the product sequencing decisions in multiple-piece-flow assembly lines problem is approximately solved with a hybrid evolutionary algorithm. The product sequencing decisions in multiple-piece-flow assembly lines, known as the sequencing m-vector bin packing problem, occurs in manufacturing organization and because of its NPhardness it is however computationally challenging. The designed method combines a population approach and both first fit bin packing procedure coupled with a repairing operator: the population approach tries to maintain the diversity of a series of populations reached throughout an iterative procedure while the added operators try to highlight the quality of the solutions throughout the search process. The performance of the proposed method is evaluated on a set of benchmark instances taken from the literature. The results provided by the method are compared to those reached by recent published methods and to those reached by the state-of-the-art Cplex solver. The preliminary experimental part showed that the designed method outperforms the other ones by discovering new bounds for most of considered instances.
Evolutionary Approach for the Containers Bin-Packing Problem
This paper deals with the resolution of combinatorial optimization problems, particularly those concerning the maritime transport scheduling. We are interested in the management platforms in a river port and more specifically in container organisation operations with a view to minimizing the number of container rehandlings. Subsequently, we rmeet customers' delivery deadlines and we reduce ship stoppage time In this paper, we propose a genetic algorithm to solve this problem and we present some experiments and results.