Efficient algorithms for the offline variable sized bin-packing problem

Mohamed, M. Ezzat; Labed, Abdenour; Radjef, Mohammed Saïd

doi:10.1007/s10898-012-9989-x

Cited by 15 publications

(8 citation statements)

References 20 publications

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“…The cluster resource management is a typical application in this domain. Depending on whether the algorithm can see all the items beforehand, there are on-line [14] and off-line [15] bin packing problems. The number of different candidate bin types divides the bin packing problems into the single sized bin packing and the variable sized bin packing [5].…”

Section: A Bin Packing Classificationmentioning

confidence: 99%

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Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing

Mao

Blanco²,

et al. 2017

2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)

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Abstract-Bin Packing problems have been widely studied because of their broad applications in different domains. Known as a set of NP-hard problems, they have different variations and many heuristics have been proposed for obtaining approximate solutions. Specifically, for the 1D variable sized bin packing problem, the two key sets of optimization heuristics are the bin assignment and the bin allocation. Usually the performance of a single static optimization heuristic can not beat that of a dynamic one which is tailored for each bin packing instance. Building such an adaptive system requires modeling the relationship between bin features and packing perform profiles. The primary drawbacks of traditional AI machine learnings for this task are the natural limitations of feature engineering, such as the curse of dimensionality and feature selection quality. We introduce a deep learning approach to overcome the drawbacks by applying a large training data set, auto feature selection and fast, accurate labeling. We show in this paper how to build such a system by both theoretical formulation and engineering practices. Our prediction system achieves up to 89% training accuracy and 72% validation accuracy to select the best heuristic that can generate a better quality bin packing solution.

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Section: A Bin Packing Classificationmentioning

confidence: 99%

“…The drop out vector will be applied to outputs for each of the network layer by performing a element wise product defined in (14). Now the composition network structure definition (5) becomes (15) with the drop out implementation. In other words, d l is used to sample many sub-networks of a large network.…”

Section: Over Fittingmentioning

confidence: 99%

Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing

Mao

Blanco²,

et al. 2017

2017 IEEE Third International Conference on Big Data Computing Service and Applications (BigDataService)

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“…Thereafter Fleszar et al [8] proposed a variant of MBS called MBS', in which the item of maximum size is not unstuck. MBS has been used in several variants of bin packing problem, such as the bin packing problem with conflicts [9], and the variable sized bin packing problem [10].…”

Section: B Upper Boundsmentioning

confidence: 99%

Modeling and Solving the Open-End Bin Packing Problem

Mohamed¹,

Tebbal²,

Billal³

2016

ijacsa

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Abstract-In the Open-End Bin Packing Problem a set of items with varying weights must be packed into bins of uniform weight limit such that the capacity of the bin can be exceeded only by the last packed item, known as the overflow item. The objective is to minimize the number of used bins. In this paper, we present our Integer Linear Program model based on a modification of Cesili and Righini model [1]. Also, we propose two greedy heuristics to solve a problem. The first one is an adaptation of the Minimum Bin Slack heuristic where we have reduced to one unit capacity, the weight of the largest item in the current bin. While, the second heuristic is based on the well-known First Fit Decreasing heuristic. Computational results based on benchmark instances taken from the literature as well as generated instances show the effectiveness of the proposed heuristics in both solution quality and time requirement.

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“…Bang-Jensen & Larsen (2012) investigated real life instances of the VSBPP with a construction and a local search heuristic with the aim of obtaining high quality solutions for large number of bin sizes and adapting fast to online changes in data. Maiza, Labed, & Radjef (2013) analyzed VSBPP with the aim of minimizing the total cost of bins by applying four heuristics they proposed. Dokeroglu & Cosar (2014) analyzed the one-dimensional BPP and applied bin-oriented heuristics in island parallel grouping genetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Decision Support System Based on Genetic Algorithm for Variable Sized Bin Packing Problem With Item Conflicts

Kabasakal

Keskin

2017

International Review of Economics and Management

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Bin packing problem (BPP) is a combinatorial NP-hard problem that has variations including one, two and three dimensional packing, variable sized packing and packing with constraints. In the literature, exact and approximation algorithms have been mostly used to solve bin packing problems.Genetic Algorithms are meta-heuristic methods that have been applied to a vast majority of wellknown optimization problems including the bin packing problems. In this paper, a variant of binpacking problem for variable bins is addressed. The capacity constraints including volume and weight are given; moreover, to avoid item conflicts is defined as an additional constraint. A decision support model utilizing the genetic algorithm is introduced for this variant of the BPP. The performance of the model is tested with sample input, the results obtained are presented and discussed in the results section.

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Efficient algorithms for the offline variable sized bin-packing problem

Cited by 15 publications

References 20 publications

Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing

Small Boxes Big Data: A Deep Learning Approach to Optimize Variable Sized Bin Packing

Modeling and Solving the Open-End Bin Packing Problem

A Decision Support System Based on Genetic Algorithm for Variable Sized Bin Packing Problem With Item Conflicts

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