Effective Data Structure for the Multidimensional Orthogonal Bin Packing Problems

2014 ◽  
Vol 962-965 ◽  
pp. 2868-2871 ◽  
Author(s):  
Alexander V. Chekanin ◽  
Vladislav A. Chekanin
Keyword(s):  
Data Structure ◽  
Linked Data ◽  
Bin Packing ◽  
High Efficiency ◽  
Optimization Problems ◽  
Packing Problem ◽  
Strong Sense ◽  
Np Hard ◽  
Packing Problems ◽  
Orthogonal Packing Problem

The actual in industry multidimensional orthogonal packing problem is considered in the article. Solution of a large number of different practical optimization problems, including resources saving problem, optimization problems in logistics, scheduling and planning comes down to the orthogonal packing problem which is NP-hard in strong sense. One of the indicators characterizing the efficiency of packing constructing algorithm is the efficiency of the used data structure. In the article a multilevel linked data structure that increases the speed of constructing of a packing is proposed. The carried out computational experiments show the high efficiency of the new data structure. Multilevel linked data structure is applicable for multidimensional orthogonal bin packing problems any kind.

Multilevel Linked Data Structure for the Multidimensional Orthogonal Packing Problem

2014 ◽  
Vol 598 ◽  
pp. 387-391 ◽  
Author(s):  
Vladislav A. Chekanin ◽  
Alexander V. Chekanin
Keyword(s):  
Data Structure ◽  
Linked Data ◽  
Bin Packing ◽  
Packing Problem ◽  
Packing Problems ◽  
Bin Packing Problem ◽  
Orthogonal Packing Problem

The actual NP-completed orthogonal bin packing problem is considered in the article. In practice a solution of a large number of different practical problems, including problems in logistics and scheduling comes down to the bin packing problem. A decision of an any packing problem is represented as a placement string which contains a sequence of objects selected to pack. The article proposes a new multilevel linked data structure that improves the effectiveness of decoding of the placement string and as a consequence, increases the speed of packing generation. The new data structure is applicable for all multidimensional orthogonal bin packing problems.

Improved Data Structure for the Orthogonal Packing Problem

2014 ◽  
Vol 945-949 ◽  
pp. 3143-3146 ◽  
Author(s):  
Vladislav A. Chekanin ◽  
Alexander V. Chekanin
Keyword(s):  
Data Structure ◽  
Linked Data ◽  
Packing Problem ◽  
High Time ◽  
Strong Sense ◽  
Computational Experiments ◽  
Np Hard ◽  
Time Efficiency ◽  
Linked List ◽  
Orthogonal Packing Problem

In paper is considered the actual in industry and engineering orthogonal multidimensional packing problem. This problem is NP-hard in strong sense therefore an important role is played effectiveness of the used packing representation model. To increase the speed of placement of a given set of orthogonal objects into containers is offered a new data structure – multilevel linked data structure. The carried out computational experiments demonstrate high time efficiency of the proposed data structure compared to the ordered simple linked list.

scholarly journals Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications

2021 ◽  
Vol 30 (1) ◽  
pp. 636-663
Author(s):  
Chanaleä Munien ◽  
Absalom E. Ezugwu
Keyword(s):  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Population Based ◽  
Metaheuristic Algorithms ◽  
Packing Problems ◽  
One Dimensional ◽  
Reference Guide ◽  
Recent Advances ◽  
The One

Abstract The bin-packing problem (BPP) is an age-old NP-hard combinatorial optimization problem, which is defined as the placement of a set of different-sized items into identical bins such that the number of containers used is optimally minimized. Besides, different variations of the problem do exist in practice depending on the bins dimension, placement constraints, and priority. More so, there are several important real-world applications of the BPP, especially in cutting industries, transportation, warehousing, and supply chain management. Due to the practical relevance of this problem, researchers are consistently investigating new and improved techniques to solve the problem optimally. Nature-inspired metaheuristics are powerful algorithms that have proven their incredible capability of solving challenging and complex optimization problems, including several variants of BPPs. However, no comprehensive literature review exists on the applications of the metaheuristic approaches to solve the BPPs. Therefore, to fill this gap, this article presents a survey of the recent advances achieved for the one-dimensional BPP, with specific emphasis on population-based metaheuristic algorithms. We believe that this article can serve as a reference guide for researchers to explore and develop more robust state-of-the-art metaheuristics algorithms for solving the emerging variants of the bin-parking problems.

Optimization by Ant Colony Hybrid Local Search for Online Class Constrained Bin Packing Problem

2013 ◽  
Vol 311 ◽  
pp. 123-128 ◽  
Author(s):  
Tsai Duan Lin ◽  
Chiun Chieh Hsu ◽  
Li Fu Hsu
Keyword(s):  
Local Search ◽  
Bin Packing ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Hybrid Approach ◽  
Packing Problem ◽  
Ant Colony ◽  
Np Hard ◽  
Combinatorial Optimization Problems ◽  
Bin Packing Problem

The on-line Class Constrained Bin Packing problem (CCBP) is one of variant version of the Bin Packing Problem (BPP). The BPP is to find the minimum numbers of bins needed to pack a given set of items of known sizes so that they do not exceed the capacity B of each bin. In the CCBP, we are given bins of capacity B with C compartments and n items of Q different classes, each item i is belong to 1,2,…,n with class qi and si. The CCBP is to pack the items into bins, where each bin contains at most Q different classes and has total items size at most B. This CCBP is known to be NP-hard combinatorial optimization problems. In this paper, we used an ant colony optimization (ACO) approach with a simple but very effective local search algorithm to resolve this NP-hard problem. After the experimental design, limited computational results show the efficiency of this scheme. It is also shown that the ACO approach can outperform some existing methods, whereas the hybrid approach can compete with the known solution methods.

African Buffalo Optimization for One Dimensional Bin Packing Problem

2019 ◽  
Vol 10 (4) ◽  
pp. 38-52 ◽  
Author(s):  
Amira Gherboudj
Keyword(s):  
Swarm Intelligence ◽  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Np Hard ◽  
Computational Results ◽  
African Buffalo ◽  
One Dimensional ◽  
Bin Packing Problem ◽  
Discrete Values

African Buffalo Optimization (ABO) is one of the most recent bioinspired metaheuristics based on swarm intelligence. It is inspired by the buffalo's behavior and lifestyle. ABO Metaheuristic showed its effectiveness for solving several optimization problems. In this contribution, we present an adaptive ABO for solving the NP-hard one dimensional Bin Packing Problem (1BPP). In the proposed algorithm, we used the ABO algorithm in combination with Ranked Order Value method to obtain discrete values and Bin Packing Problem heuristics to incorporate the problem knowledge. The proposed algorithm is used to solve 1210 of 1BPP instances. The obtained results are compared with those found by recent algorithms in the literature. Computational results show the effectiveness of the proposed algorithm and its ability to achieve best and promising solutions.

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 Improving the Bin Packing Heuristic through Grammatical Evolution Based on Swarm Intelligence

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Marco Aurelio Sotelo-Figueroa ◽  
Héctor José Puga Soberanes ◽  
Juan Martín Carpio ◽  
Héctor J. Fraire Huacuja ◽  
Laura Cruz Reyes ◽  
...  
Keyword(s):  
Swarm Intelligence ◽  
Boolean Functions ◽  
Bin Packing ◽  
Optimization Problems ◽  
Packing Problem ◽  
Grammatical Evolution ◽  
Friedman Test ◽  
Swarm Optimization ◽  
Algorithmic Problems ◽  
Test Instance

In recent years Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP) which has been applied to many optimization problems such as symbolic regression, classification, Boolean functions, constructed problems, and algorithmic problems. GE can use a diversity of searching strategies including Swarm Intelligence (SI). Particle Swarm Optimisation (PSO) is an algorithm of SI that has two main problems: premature convergence and poor diversity. Particle Evolutionary Swarm Optimization (PESO) is a recent and novel algorithm which is also part of SI. PESO uses two perturbations to avoid PSO’s problems. In this paper we propose using PESO and PSO in the frame of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP); it is possible however to apply this methodology to other kinds of problems using another Grammar designed for that problem. A comparison between PESO, PSO, and BPP’s heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is proposing a Grammar to generate online and offline heuristics depending on the test instance trying to improve the heuristics generated by other grammars and humans; it also proposes a way to implement different algorithms as search strategies in GE like PESO to obtain better results than those obtained by PSO.

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.

Improved Packing Representation Model for the Orthogonal Packing Problem

2013 ◽  
Vol 390 ◽  
pp. 591-595 ◽  
Author(s):  
Alexander V. Chekanin ◽  
Vladislav A. Chekanin
Keyword(s):  
Bin Packing ◽  
Optimal Allocation ◽  
Heuristic Algorithms ◽  
Three Dimensional ◽  
Packing Problem ◽  
Representation Model ◽  
Bin Packing Problem ◽  
Applied Software ◽  
Proposed Model ◽  
Orthogonal Packing Problem

The multidimensional NP-hard orthogonal bin packing problem is considered in the article. Usually the problem is solved using heuristic algorithms of discrete optimization which optimize a selection sequence of objects to be packed in containers. The quality and speed of getting the resulting packing for a given sequence of placing objects is determined by the used packing representation model. In the article presented a new packing representation model for constructing the orthogonal packing. The proposed model of potential containers describes all residual free spaces of containers in packing. The developed model is investigated on well-known standard benchmarks of three-dimensional orthogonal bin packing problem. The model can be used in development of applied software for the optimal allocation of orthogonal resources.

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