Multiple-type, two-dimensional bin packing problems: Applications and algorithms

Han, Bernard T.; Diehr, George; Cook, Jack

doi:10.1007/bf02085642

Cited by 32 publications

(22 citation statements)

References 37 publications

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“…In [7], a solver which uses column generation and branch and bound algorithms to solve multiple type, two dimensional bin packing problems is presented. [8] uses a mixed integer programming (MIP) solver to calculate application placements on VMs, VM resource allocation and consolidation, meeting SLA constraints.…”

Section: A Exact Algorithms Based Strategiesmentioning

confidence: 99%

“…An usual strategy identified in [7], [11], and [12], is the usage of the objective function and constraints of linear programming problems as fitness function / energy function and selection criterion / cooling strategy respectively for genetic algorithms and simulated annealing.…”

Section: B Meta-heuristic Based Strategiesmentioning

confidence: 99%

“…We address huge problem instances that must be solved in feasible time, consequently, this solution is not adequate. Also, when compared to greedy heuristics, the execution time of meta-heuristics are orders of magnitude slower (seconds versus hours) and often the quality of solutions does not follow this proportion, as discussed in [7] and [15].…”

Section: B Meta-heuristic Based Strategiesmentioning

confidence: 99%

“…[19] presents the Best Fit Dot Product, First Fit Weighted Sum based algorithms and the process, that would be latter named measure in [22], of measuring multi-dimensional items or bins. [7] presents the First Fit Ordered Deviation, an algorithm that deals with homogeneity issues but manages only to output solutions to two-dimensional bin packing problems.…”

Section: Greedy Heuristics Based Strategiesmentioning

confidence: 99%

“…Among all algorithms of this type, like genetic algorithms, particle swarm optimization, ant colony optimization and others, we choose the simulated annealing because, in addition to successful experiences in other similar contexts [7], it has less configuration parameters, hence it is easier to apply it to our problem.…”

Section: B Mip Solver and Simulated Annealing Analysismentioning

confidence: 99%

See 4 more Smart Citations

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Pérez

Desprez

2016

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

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Abstract-With the fast growth of the demand for Cloud computing services, the Cloud has become a very popular platform to develop distributed applications. Features that in the past were available only to big corporations, like fast scalability, availability, and reliability, are now accessible to any customer, including individuals and small companies, thanks to Cloud computing. In order to place an application, a designer must choose among VM types, from private and public cloud providers, those that are capable of hosting her application or its parts using as criteria application requirements, VM prices, and VM resources. This procedure becomes more complicated when the objective is to place large component based applications on multiple clouds. In this case, the number of possible configurations explodes making necessary the automation of the placement. In this context, scalability has a central role since the placement problem is a generalization of the NP-Hard multi-dimensional bin packing problem.In this paper we propose efficient greedy heuristics based on first fit decreasing and best fit algorithms, which are capable of computing near optimal solutions for very large applications, with the objective of minimizing costs and meeting application performance requirements. Through a meticulous evaluation, we show that the greedy heuristics took a few seconds to calculate near optimal solutions to placements that would require hours or even days when calculated using state of the art solutions, namely exact algorithms or meta-heuristics.

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Section: A Exact Algorithms Based Strategiesmentioning

confidence: 99%

Section: B Meta-heuristic Based Strategiesmentioning

confidence: 99%

Section: B Meta-heuristic Based Strategiesmentioning

confidence: 99%

Section: Greedy Heuristics Based Strategiesmentioning

confidence: 99%

Section: B Mip Solver and Simulated Annealing Analysismentioning

confidence: 99%

See 3 more Smart Citations

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Pérez

Desprez

2016

2016 16th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid)

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Bin-Packing

Wang

Coleman

2001

Encyclopedia of Operations Research and Management Science

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Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

Sotelo-Figueroa

Soberanes

Carpio

et al. 2016

Nature-Inspired Design of Hybrid Intelligent Systems

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In the recent years, Grammatical Evolution (GE) has been used as a representation of Genetic Programming (GP). GE can use a diversity of search strategies including Swarm Intelligence (SI). Bee Swarm Optimization (BSO) is part of SI and it tries to solve the main problems of the Particle Swarm Optimization (PSO): the premature convergence and the poor diversity. In this paper we propose using BSO as part of GE as strategies to generate heuristics that solve the Bin Packing Problem (BPP). A comparison between BSO, PSO, and BPP heuristics is performed through the nonparametric Friedman test. The main contribution of this paper is to propose a way to implement different algorithms as search strategy in GE. In this paper, it is proposed that the BSO obtains better results than the ones obtained by PSO, also there is a grammar proposed to generate online and offline heuristics to improve the heuristics generated by other grammars and humans.

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Multiple-type, two-dimensional bin packing problems: Applications and algorithms

Cited by 32 publications

References 37 publications

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Efficient Heuristics for Placing Large-Scale Distributed Applications on Multiple Clouds

Bin-Packing

Generating Bin Packing Heuristic Through Grammatical Evolution Based on Bee Swarm Optimization

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