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A Self Organizing Bin Packing Heuristic [chapter]

Janos Csirik, David S. Johnson, Claire Kenyon, Peter W. Shor, Richard R. Weber
1999 Lecture Notes in Computer Science  
This paper reports on experiments with a new on-line heuristic for one-dimensional bin packing whose average-case behavior is surprisingly robust.  ...  The expected ratio of the number of bins used by SS to the optimal number appears to go to 1 asymptotically in the rst case, whereas there are distributions for which it can be as high as 1.5 in the second  ...  In Section 5 we suggest an intuitive explanation for its behavior that views the operation of SS as a self-organizing process.  ... 
doi:10.1007/3-540-48518-x_15 fatcat:74glvkyuxzemnkkcosgdqoe634

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

Marco Aurelio Sotelo-Figueroa, Héctor José Puga Soberanes, Juan Martín Carpio, Héctor J. Fraire Huacuja, Laura Cruz Reyes, Jorge Alberto Soria-Alcaraz
2014 Mathematical Problems in Engineering  
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  ...  A comparison between PESO, PSO, and BPP's heuristics is performed through the nonparametric Friedman test.  ...  To improve the Bin Packing Heuristics it was necessary to design a grammar that represents the Bin Packing Problem.  ... 
doi:10.1155/2014/545191 fatcat:q3a2gtvg5bcnfpr6hldkmmaj7y

No Fit Polygon for Nesting Problem Solving with Hybridizing Ant Algorithms

Qiang Yang
2014 Journal of Software Engineering and Applications  
The intelligence of this heuristic approach is not portrayed by individual ants, but rather is expressed by the colony as a whole inspired by labor division and brood sorting.  ...  This solution obtained by this method will be evaluated against the one obtained by other traditional heuristics.  ...  As a result, models in terms of self-organization have recently been introduced by ethologists to study collective behavior in social insects [8] [9] .  ... 
doi:10.4236/jsea.2014.75040 fatcat:2422a7wrrrgsvddwr3dwxsaf2u

One model packs thousands of items with Recurrent Conditional Query Learning

Dongda Li, Zhaoquan Gu, Yuexuan Wang, Changwei Ren, Francis C.M. Lau
2021 Knowledge-Based Systems  
Experiment results show that RCQL can effectively learn strong heuristics for offline and online strip packing problems (SPPs), outperforming a wide range of baselines in space utilization ratio.  ...  Benefiting from the recurrence, a single RCQL model is capable of handling different sizes of packing problems.  ...  three sub-actions to pack a box into the bin.  ... 
doi:10.1016/j.knosys.2021.107683 fatcat:axvbvhzihffvbaucka4npcmyzm

Server Consolidation Schemes in Cloud Computing Environment: A Review

Mirsaeid Hosseini Shirvani, Aidan Ghojoghi
2018 European Journal of Engineering and Technology Research  
Server consolidation approach abstracts system under study into NP-hard bin-packing problem. Several works have been done in literature to solve server consolidation problem.  ...  Cloud computing is a new paradigm which deliver computing resources as utility.  ...  Server consolidation problem is abstracted to well-known NP-Hard bin-packing problem which items must be packed into minimum number of bins.  ... 
doi:10.24018/ejeng.2016.1.3.169 fatcat:nfrq7f36tncinibgbwyrl7qcre

Guided Local Search for the Three-Dimensional Bin-Packing Problem

Oluf Faroe, David Pisinger, Martin Zachariasen
2003 INFORMS journal on computing  
The three-dimensional bin packing problem is the problem of orthogonally packing a set of boxes into a minimum number of three-dimensional bins.  ...  Starting with an upper bound on the number of bins obtained by a greedy heuristic, the presented algorithm iteratively decreases the number of bins, each time searching for a feasible packing of the boxes  ...  Application of GLS The problem handed to the GLS heuristic is therefore to construct a feasible packing of the boxes into a xed number of bins.  ... 
doi:10.1287/ijoc. fatcat:tbfftaeeibg53aveklnsbu25v4

Asymptotic Results for the Generalized Bin Packing Problem

Mauro Maria Baldi, Teodor Gabriel Crainic, Guido Perboli, Roberto Tadei
2014 Procedia - Social and Behavioral Sciences  
We present a worst case analysis for the Generalized Bin Packing Problem, a novel packing problem arising in many Transportation and Logistics settings, characterized by multiple item and bin attributes  ...  The contribution of this paper is twofold: we conduct a worst case analysis applied to the much richer Generalized Bin Packing Problem of two outstanding bin packing algorithms (the First Fit Decreasing  ...  This paper is organized as follows. In Section 2, we present in a more formal way the problem and the nomenclature.  ... 
doi:10.1016/j.sbspro.2014.01.100 fatcat:peiwryg7dnbi3fosipuorhydla

A Generalized Bin Packing Problem for parcel delivery in last-mile logistics

Mauro Maria Baldi, Daniele Manerba, Guido Perboli, Roberto Tadei
2019 European Journal of Operational Research  
We show that the problem can be modeled as a new packing problem, the Generalized Bin Packing Problem with bin-dependent item profits (GBPPI), where the items are the parcels to deliver and the bins are  ...  We provide a Mixed Integer Programming formulation of the problem, which is the starting point for the development of efficient heuristics that can address the GBPPI for instances involving up to 10 0  ...  Let S ⊆ J be the set of bins used in a solution of a bin packing problem, and let W res ( b ) be the residual volume of bin b ∈ S.  ... 
doi:10.1016/j.ejor.2018.10.056 fatcat:yrru3akiqvgxzpyik76nb2m6hm

A Generalized Reinforcement Learning Algorithm for Online 3D Bin-Packing [article]

Richa Verma, Aniruddha Singhal, Harshad Khadilkar, Ansuma Basumatary, Siddharth Nayak, Harsh Vardhan Singh, Swagat Kumar, Rajesh Sinha
2020 arXiv   pre-print
We propose a Deep Reinforcement Learning (Deep RL) algorithm for solving the online 3D bin packing problem for an arbitrary number of bins and any bin size.  ...  Simulation results show that the RL-based method outperforms state-of-the-art online bin packing heuristics in terms of empirical competitive ratio and volume efficiency.  ...  The claimed contributions of this paper are, (1) a novel heuristic (called WallE) for solving RT-3D-BPP which is shown to outperform existing bin-packing heuristics, (2) a Deep RL methodology (called PackMan  ... 
arXiv:2007.00463v1 fatcat:fnvpgbpfifappodedsuatlgqum

Understanding the structure of bin packing problems through principal component analysis

Eunice López-Camacho, Hugo Terashima-Marín, Gabriela Ochoa, Santiago Enrique Conant-Pablos
2013 International Journal of Production Economics  
This paper uses a knowledge discovery method, Principal Component Analysis (PCA), to gain a deeper understanding of the structure of bin packing problems and how this relates to the performance of heuristic  ...  The so-called DJD heuristics are able to best solve a large proportion of instances, but simpler and faster heuristics can outperform them in some cases.  ...  single bin size bin packing problem.  ... 
doi:10.1016/j.ijpe.2013.04.041 fatcat:wcqml4ggrjcuzgzdycasfcg3tm

Optimization of heterogeneous Bin packing using adaptive genetic algorithm

R Sridhar, M Chandrasekaran, C Sriramya, Tom Page
2017 IOP Conference Series: Materials Science and Engineering  
This work presents a combinational of heuristic Genetic Algorithm (HGA) for solving Three Dimensional (3D) Single container arbitrary sized rectangular prismatic bin packing optimization problem by considering  ...  This research is concentrates on a very interesting work, the bin packing using hybrid genetic approach.  ...  Commonly used 1D Bin Packing Algorithms are: l bin packing problem by adding a variable to optimize.  ... 
doi:10.1088/1757-899x/183/1/012026 fatcat:zq4rhcu6tvhwdcfkucvcxcpo7u

Backward Chaining [chapter]

2013 Encyclopedia of Operations Research and Management Science  
b 1 (r, L, t) and b 2 (r, L, t), and the variance terms are expressed by a 1 (r, L, t) and a 2 (r, L, t).  ...  a common cumulative distribution function.  ...  If this packing is not feasible due to bin size violations, a second heuristic is employed to optimize the packing with respect to the bin size constraints.  ... 
doi:10.1007/978-1-4419-1153-7_200968 fatcat:wsdsqf2wxrckvpoq4yh5f7rsqm

A Survey of Virtual Machine Placement Techniques in a Cloud Data Center

Zoha Usmani, Shailendra Singh
2016 Procedia Computer Science  
There is a need to improve the energy efficiency of such data centers using Server Consolidation which aims at minimizing the number of Active Physical Machines (APMs) in a data center.  ...  A detailed comparison is presented, revealing pitfalls and suggesting improvisation methods along this direction.  ...  This heuristic can be called as bin packing extended with additional constraints. Mi et al.  ... 
doi:10.1016/j.procs.2016.02.093 fatcat:yrgmosxvszgv7jjc4vmyycksta

Page 5241 of Mathematical Reviews Vol. , Issue 2000g [page]

2000 Mathematical Reviews  
Summary: “The bin packing problem is one of the classical NP- hard optimization problems.  ...  (D-KOLN-PR; Cologne); Schepers, Jérg (D-KOLN-PR; Cologne) New classes of lower bounds for bin packing problems.  ... 

Automated Algorithm Selection: from Feature-Based to Feature-Free Approaches [article]

Mohamad Alissa, Kevin Sim, Emma Hart
2022 arXiv   pre-print
Specifically we train two types of recurrent neural networks to predict a packing heuristic in online bin-packing, selecting from four well-known heuristics.  ...  We propose a novel technique for algorithm-selection, applicable to optimisation domains in which there is implicit sequential information encapsulated in the data, e.g., in online bin-packing.  ...  ., 2004) , a decision tree to build the selectors and a Self-Organizing Map (SOM) (Haykin et al., 2009) to implement the selection system with feedback. Five features were used as input.  ... 
arXiv:2203.13392v1 fatcat:vrgo6kggkrfo3eofl7loqvnm5q
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