Relevant bibliographies by topics / Bin packing

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bin packing'

Author: Grafiati
Published: 4 June 2021
Last updated: 7 July 2024

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Journal articles on the topic "Bin packing"

1

Gutin, Gregory, Tommy Jensen, and Anders Yeo. "Batched bin packing." Discrete Optimization 2, no. 1 (March 2005): 71–82. http://dx.doi.org/10.1016/j.disopt.2004.11.001.

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Epstein, Leah, and Elena Kleiman. "Selfish Bin Packing." Algorithmica 60, no. 2 (August 13, 2009): 368–94. http://dx.doi.org/10.1007/s00453-009-9348-6.

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Kuipers, Jeroen. "Bin packing games." Mathematical Methods of Operations Research 47, no. 3 (October 1998): 499–510. http://dx.doi.org/10.1007/bf01198407.

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Epstein, Leah. "On bin packing with clustering and bin packing with delays." Discrete Optimization 41 (August 2021): 100647. http://dx.doi.org/10.1016/j.disopt.2021.100647.

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Kartak, Vadim M., and Artem V. Ripatti. "Large proper gaps in bin packing and dual bin packing problems." Journal of Global Optimization 74, no. 3 (August 3, 2018): 467–76. http://dx.doi.org/10.1007/s10898-018-0696-0.

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Liu, Yang, and Thanh Vinh Vo. "Bin Packing Solution for Automated Packaging Application." Applied Mechanics and Materials 143-144 (December 2011): 279–83. http://dx.doi.org/10.4028/www.scientific.net/amm.143-144.279.

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This paper introduces the implementation of a new heuristic recursive algorithm for bin packing solution used in automated packaging application. The theoretical method proposed in this paper is successfully implemented on a real ABB robot arm with some important improvements such as added rotation flexibility and removing an added product from the structure. The computational results on a class of benchmark problems have shown that this algorithm not only finds shorter height than the known meta-heuristic ones, but also runs in shorter time. The average running time is very suitable for such kind of automated packaging application.
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Yang, Jianglong, Kaibo Liang, Huwei Liu, Man Shan, Li Zhou, Lingjie Kong, and Xiaolan Li. "Optimizing e-commerce warehousing through open dimension management in a three-dimensional bin packing system." PeerJ Computer Science 9 (October 9, 2023): e1613. http://dx.doi.org/10.7717/peerj-cs.1613.

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In the field of e-commerce warehousing, maximizing the utilization of packing bins is a fundamental goal for all major logistics enterprises. However, determining the appropriate size of packing bins poses a practical challenge for many logistics companies. Given the limited research on the open-size 3D bin packing problem as well as the high complexity and lengthy computation time of existing models, this study focuses on optimizing multiple-bin sizes within the e-commerce context. Building upon existing research, we propose a hybrid integer programming model, denoted as the three dimensional multiple option dimensional rectangular packing problem (3D-MODRPP), to address the multiple-bin size 3D bin packing problem. Additionally, we leverage well-established hardware and software technologies to propose a 3D bin packing system capable of accommodating multiple bin types with open dimensions. To reduce the complexity of the model and the number of constraints, we introduce a novel assumption method for 0–1 integer variables in the overlap and rotation constraints. By applying this approach, we significantly streamline the computational complexity associated with the model calculations. Furthermore, we refine the dataset by developing a customized version based on the classical Three-Dimensional One-Size Dependent Rectangular Packing Problem (3D-ODRPP) dataset, leading to improved outcomes. Through comprehensive analysis of the research results, our model exhibits remarkable advancements in addressing the strong heterogeneous bin packing problem, the weak heterogeneous bin packing problem, the actual bin packing problem, and the bin packing problem with multiple bin types and open sizes. Specifically, it significantly reduces model complexity and computation time and increases space utilization. The system designed in this study paves the way for practical applications based on the proposed model, providing researchers with broader research prospects and directions to expand the scope of investigation in the field of 3D bin packing. Consequently, this system contributes to solving complex 3D packing problems, reducing space waste, and enhancing transportation efficiency.
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Lin, Bingchen, Jiawei Li, Ruibin Bai, Rong Qu, Tianxiang Cui, and Huan Jin. "Identify Patterns in Online Bin Packing Problem: An Adaptive Pattern-Based Algorithm." Symmetry 14, no. 7 (June 23, 2022): 1301. http://dx.doi.org/10.3390/sym14071301.

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Bin packing is a typical optimization problem with many real-world application scenarios. In the online bin packing problem, a sequence of items is revealed one at a time, and each item must be packed into a bin immediately after its arrival. Inspired by duality in optimization, we proposed pattern-based adaptive heuristics for the online bin packing problem. The idea is to predict the distribution of items based on packed items, and to apply this information in packing future arrival items in order to handle uncertainty in online bin packing. A pattern in bin packing is a combination of items that can be packed into a single bin. Patterns selected according to past items are adopted and periodically updated in scheduling future items in the algorithm. Symmetry in patterns and the stability of patterns in the online bin packing problem are discussed. We have implemented the algorithm and compared it with the Best-Fit in a series of experiments with various distribution of items to show its effectiveness.
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Shah, Devavrat, and John N. Tsitsiklis. "Bin Packing with Queues." Journal of Applied Probability 45, no. 4 (December 2008): 922–39. http://dx.doi.org/10.1239/jap/1231340224.

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We study the best achievable performance (in terms of the average queue size and delay) in a stochastic and dynamic version of the bin-packing problem. Items arrive to a queue according to a Poisson process with rate 2ρ, where ρ ∈ (0, 1). The item sizes are independent and identically distributed (i.i.d.) with a uniform distribution in [0, 1]. At each time unit, a single unit-size bin is available and can receive any of the queued items, as long as their total size does not exceed 1. Coffman and Stolyar (1999) and Gamarnik (2004) have established that there exist packing policies under which the average queue size is finite for every ρ ∈ (0, 1). In this paper we study the precise scaling of the average queue size, as a function of ρ, with emphasis on the critical regime where ρ approaches 1. Standard results on the probabilistic (but static) bin-packing problem can be readily applied to produce policies under which the queue size scales as O(h2), where h = 1 / (1 - ρ), which raises the question of whether this is the best possible. We establish that the average queue size scales as Ω(hlogh), under any policy. Furthermore, we provide an easily implementable policy, which packs at most two items per bin. Under that policy, the average queue size scales as O(hlog3/2h), which is nearly optimal. On the other hand, if we impose the additional requirement that any two items packed together must have near-complementary sizes (in a sense to be made precise), we show that the average queue size must scale as Θ(h2).
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Shah, Devavrat, and John N. Tsitsiklis. "Bin Packing with Queues." Journal of Applied Probability 45, no. 04 (December 2008): 922–39. http://dx.doi.org/10.1017/s0021900200004885.

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We study the best achievable performance (in terms of the average queue size and delay) in a stochastic and dynamic version of the bin-packing problem. Items arrive to a queue according to a Poisson process with rate 2ρ, where ρ ∈ (0, 1). The item sizes are independent and identically distributed (i.i.d.) with a uniform distribution in [0, 1]. At each time unit, a single unit-size bin is available and can receive any of the queued items, as long as their total size does not exceed 1. Coffman and Stolyar (1999) and Gamarnik (2004) have established that there exist packing policies under which the average queue size is finite for every ρ ∈ (0, 1). In this paper we study the precise scaling of the average queue size, as a function of ρ, with emphasis on the critical regime where ρ approaches 1. Standard results on the probabilistic (but static) bin-packing problem can be readily applied to produce policies under which the queue size scales as O(h 2), where h = 1 / (1 - ρ), which raises the question of whether this is the best possible. We establish that the average queue size scales as Ω(hlogh), under any policy. Furthermore, we provide an easily implementable policy, which packs at most two items per bin. Under that policy, the average queue size scales as O(hlog3/2 h), which is nearly optimal. On the other hand, if we impose the additional requirement that any two items packed together must have near-complementary sizes (in a sense to be made precise), we show that the average queue size must scale as Θ(h 2).
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Dissertations / Theses on the topic "Bin packing"

1

Burcea, Mihai. "Online dynamic bin packing." Thesis, University of Liverpool, 2014. http://livrepository.liverpool.ac.uk/2005382/.

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In this thesis we study online algorithms for dynamic bin packing. An online algorithm is presented with input throughout time and must make irrevocable decisions without knowledge of future input. The classical bin packing problem is a combinatorial optimization problem in which a set of items must be packed into a minimum number of uniform-sized bins without exceeding their capacities. The problem has been studied since the early 1970s and many variants continue to attract researchers’ attention today. The dynamic version of the bin packing problem was introduced by Coffman, Garey and Johnson in 1983. The problem is a generalization of the bin packing problem in which items may arrive and depart dynamically. In this setting, an online algorithm for bin packing is presented with one item at a time, without knowledge of its departure time, nor arrival and departure times of future items, and must decide in which bin the item should be packed. Migration of items between bins is not allowed, however rearrangement of items within a bin is permitted. The objective of problem is to minimize the maximum number of bins used over all time. In multi-dimensional generalizations of the problem, multi-dimensional items must be packed without overlap in multi-dimensional bins of uniform size in each dimension. In this work, we study the setting where items are oriented and cannot be rotated. We first consider online one-dimensional dynamic bin packing and present a lower bound of 8/3 ∼ 2.666 on the achievable competitive ratio of any deterministic online algorithm, improving the best known 2.5-lower bound. Since the introduction of the problem by Coffman, Garey and Johnson, the progress on the problem has focused on improving the original lower bound of 2.388 to 2.428, and to the best known 2.5-lower bound. Our improvement from 2.5 to 8/3 ∼ 2.666 makes a big step forward in closing the gap between the lower bound and the upper bound, which currently stands at 2.788. Secondly we study the online two- and three-dimensional dynamic bin packing problem by designing and analyzing algorithms for special types of input. Bar-Noy et al. initiated the study of the one-dimensional unit fraction bin packing problem, a restricted version where all sizes of items are of the form 1/k, for some integer k > 0. Another related problem is for power fraction items, where sizes are of the form 1/2k, for some integer k ≥ 0. We initiate the study of online multi-dimensional dynamic bin packing of unit fraction items and power fraction items, where items have lengths unit fraction and power fraction in each dimension, respectively. While algorithms for general input are suitable for unit fraction and power fraction items, their worst-case performance guarantees are the same for special types of input. For unit fraction and power fraction items, we design and analyze online algorithms that achieve better worst-case performance guarantees compared to their classical counterparts. Our algorithms give careful consideration to unit and power fraction items, which allows us to reduce the competitive ratios for these types of inputs. Lastly we focus on obtaining lower bounds on the performance of the family of Any- Fit algorithms (Any-Fit, Best-Fit, First-Fit, Worst-Fit) for online multi-dimensional dynamic bin packing. Any-Fit algorithms are classical online algorithms initially studied for the one-dimensional version of the bin packing problem. The common rule that the algorithms use is to never pack a new item to a new bin if the item can be packed in any of the existing bins. While the family of Any-Fit algorithms is always O(1)-competitive for one-dimensional dynamic bin packing, we show that this is no longer the case for multi-dimensional dynamic bin packing when using Best-Fit and Worst-Fit, even if the input consists of power fraction items or unit fraction items. For these restricted inputs, we prove that Best-Fit and Worst-Fit have unbounded competitive ratios, while for First-Fit we provide lower bounds that are higher than the lower bounds for any online algorithm. Furthermore, for general input we show that all classical Any-Fit algorithms are not competitive for online multi-dimensional dynamic bin packing.
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Nielsen, Torben Noerup. "Combinatorial Bin Packing Problems." Diss., The University of Arizona, 1985. http://hdl.handle.net/10150/187536.

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In the past few years, there has been a strong and growing interest in evaluating the expected behavior of what we call combinatorial bin packing problems. A combinatorial bin packing problem consists of a number of items of various sizes and value ratios (value per unit of size) along with a collection of bins of fixed capacity into which the items are to be packed. The packing must be done in such a way that the sum of the sizes of the items into a given bin does not exceed the capacity of that bin. Moreover, an item must either be packed into a bin in its entirety or not at all: this "all or nothing" requirement is why these problems are characterized as being combinatorial. The objective of the packing is to optimize a given criterion Junction. Here optimize means either maximize or minimize, depending on the problem. We study two problems that fit into this framework: the Knapsack Problem and the Minimum Sum of Squares Problem. Both of these problems are known to be in the class of NP-hard problems and there is ample reason to suspect that these problems do not admit of efficient exact solution. We obtain results concerning the performance of heuristics under the assumption that the inputs are random samples from some distribution. For the Knapsack Problem, we develop four heuristics, two of which are on-line and two off-line. All four heuristics are shown to be asymptotically optimal in expectation when the item sizes and value ratios are assumed to be independent and uniform. One heuristic is shown to be asymptotically optimal in expectation when the item sizes are uniformly distributed and the value ratios are exponentially distributed. The amount of time required by these heuristics is no more than proportional to the amount of time required to sort the items in order of nonincreasing value ratios. For the Minimum Sum of Squares Problem, we develop two heuristics, both of which are off-line. Both of these heuristics are shown to be asymptotically optimal in expectation when the sizes of the items input are assumed uniformly distributed.
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BALDI, MAURO MARIA. "Generalized Bin Packing Problems." Doctoral thesis, Politecnico di Torino, 2013. http://hdl.handle.net/11583/2507776.

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Packing problems make up a fundamental topic of combinatorial optimization. Their importance is confirmed both by their wide range of scientific and technological applications they are able to address and by their theoretical implications. In fact, they are exploited in many fields such as computer science and technologies, industrial applications, transportation and logistics, and telecommunications. From a theoretical perspective, packing problems often appear as sub-problems in order to iteratively solve bigger problems. Although packing problems play a fundamental role in all these settings, there is a gap in terms of comprehensive study in the literature. In fact, the joint presence of both compulsory and non-compulsory items has not been considered yet. This particular setting arises in many real-life applications, not yet addressed or only partially addressed by the current state-of-the-art packing problems. Furthermore, little has been done in terms of unified methodologies, and different techniques have been used in order to solve packing problems with different objective functions. In particular, none of these techniques is able to address the presence of compulsory and non-compulsory items at the same time. In order to overcome a noteworthy portion of this gap, we formulated a new packing problem, named the Generalized Bin Packing Problem (GBPP), characterized by both compulsory and non-compulsory items, and multiple item and bin attributes. Packing problems have also been studied within stochastic settings where the items are affected by uncertainty. In these settings, there are fundamentally two kinds of stochasticity concerning the items: 1) stochasticity of the item attributes, where one attribute is affected by uncertainty and modeled as a random variable or 2) stochasticity of the item availability, i.e., the items are not known a priori but they arrive on-line in an unpredictable way to a decision maker. Although packing problems have been studied according to these stochastic variants, the GBPP with uncertainty on the items is still an open problem. Therefore, we have also studied two stochastic variants of the GBPP, named the Stochastic Generalized Bin Packing Problem (S-GBPP) and the On-line Generalized Bin Packing Problem (OGBPP). Our main results concern the development of models and unified methodologies of these new packing problems, making up, as done for the Vehicle Routing Problem (VRP) with the definition of the so called Rich Vehicle Routing Problems, a new family of advanced packing problems named Generalized Bin Packing Problems.
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Ilicak, Isil. "Bi-objective Bin Packing Problems." Master's thesis, METU, 2003. http://etd.lib.metu.edu.tr/upload/2/1079987/index.pdf.

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In this study, we consider two bi-objective bin packing problems that assign a number of weighted items to bins having identical capacities. Firstly, we aim to minimize total deviation over bin capacity and minimize number of bins. We show that these two objectives are conflicting. Secondly, we study the problem of minimizing maximum overdeviation and minimizing the number of bins. We show the similarities of these two problems to parallel machine scheduling problems and benefit from the results while developing our solution approaches. For both problems, we propose exact procedures that generate efficient solutions relative to two objectives. To increase the efficiency of the solutions, we propose some lower and upper bounding procedures. The results of our experiments show that total overdeviation problem is easier to solve compared to maximum overdeviation problem and the bin capacity, the weight of items and the number of items are important factors that effect the solution time and quality. Our procedures can solve the problems with up to 100 items in reasonable solution times.
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Lundanes, Petter Olsen. "Bin packing problem with order constraints." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2014. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-27334.

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This paper presents an algorithm to solve a variant of the bin packing problem with additional constraints on the order of items. The performance of this algorithm is tested, both for optimal solutions and approximations given by early termination, and is found to be limited for optimal solutions, but fairly efficient for decent approximations.
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Khan, Arindam. "Approximation algorithms for multidimensional bin packing." Diss., Georgia Institute of Technology, 2015. http://hdl.handle.net/1853/54371.

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The bin packing problem has been the corner stone of approximation algorithms and has been extensively studied starting from the early seventies. In the classical bin packing problem, we are given a list of real numbers in the range (0, 1], the goal is to place them in a minimum number of bins so that no bin holds numbers summing to more than 1. In this thesis we study approximation algorithms for three generalizations of bin packing: geometric bin packing, vector bin packing and weighted bipartite edge coloring. In two-dimensional (2-D) geometric bin packing, we are given a collection of rectangular items to be packed into a minimum number of unit size square bins. Geometric packing has vast applications in cutting stock, vehicle loading, pallet packing, memory allocation and several other logistics and robotics related problems. We consider the widely studied orthogonal packing case, where the items must be placed in the bin such that their sides are parallel to the sides of the bin. Here two variants are usually studied, (i) where the items cannot be rotated, and (ii) they can be rotated by 90 degrees. We give a polynomial time algorithm with an asymptotic approximation ratio of $\ln(1.5) + 1 \approx 1.405$ for the versions with and without rotations. We have also shown the limitations of rounding based algorithms, ubiquitous in bin packing algorithms. We have shown that any algorithm that rounds at least one side of each large item to some number in a constant size collection values chosen independent of the problem instance, cannot achieve an asymptotic approximation ratio better than 3/2. In d-dimensional vector bin packing (VBP), each item is a d-dimensional vector that needs to be packed into unit vector bins. The problem is of great significance in resource constrained scheduling and also appears in recent virtual machine placement in cloud computing. Even in two dimensions, it has novel applications in layout design, logistics, loading and scheduling problems. We obtain a polynomial time algorithm with an asymptotic approximation ratio of $\ln(1.5) + 1 \approx 1.405$ for 2-D VBP. We also obtain a polynomial time algorithm with almost tight (absolute) approximation ratio of $1+\ln(1.5)$ for 2-D VBP. For $d$ dimensions, we give a polynomial time algorithm with an asymptotic approximation ratio of $\ln(d/2) + 1.5 \approx \ln d+0.81$. We also consider vector bin packing under resource augmentation. We give a polynomial time algorithm that packs vectors into $(1+\epsilon)Opt$ bins when we allow augmentation in (d - 1) dimensions and $Opt$ is the minimum number of bins needed to pack the vectors into (1,1) bins. In weighted bipartite edge coloring problem, we are given an edge-weighted bipartite graph $G=(V,E)$ with weights $w: E \rightarrow [0,1]$. The task is to find a proper weighted coloring of the edges with as few colors as possible. An edge coloring of the weighted graph is called a proper weighted coloring if the sum of the weights of the edges incident to a vertex of any color is at most one. This problem is motivated by rearrangeability of 3-stage Clos networks which is very useful in various applications in interconnected networks and routing. We show a polynomial time approximation algorithm that returns a proper weighted coloring with at most $\lceil 2.2223m \rceil$ colors where $m$ is the minimum number of unit sized bins needed to pack the weight of all edges incident at any vertex. We also show that if all edge weights are $>1/4$ then $\lceil 2.2m \rceil$ colors are sufficient.
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Shor, Peter Williston. "Random planar matching and bin packing." Thesis, Massachusetts Institute of Technology, 1985. https://hdl.handle.net/1721.1/128792.

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Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1985.
Bibliography: leaves 123-124.
by Peter Williston Shor.
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Mathematics, 1985.
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Clautiaux, François. "New collaborative approaches for bin-packing problems." Habilitation à diriger des recherches, Université de Technologie de Compiègne, 2010. http://tel.archives-ouvertes.fr/tel-00749419.

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Ce document décrit de nouvelles modélisations et approches de résolution que nous appliquons à des problèmes de découpe et de conditionnement. Nous étudions dans un premier temps plusieurs techniques de décomposition alliées à différentes méta-heuristiques basées sur des stratégies d'oscillation. Nous étudions ensuite le concept de fonctions dual-réalisables qui permettent d'obtenir des évaluations par défaut polynomiales pour des problèmes de conditionnement. Finalement, nous proposons des modèles originaux pour des problèmes de placement de rectangles. Nous utilisons ces modèles dans des méthodes de programmation par contraintes.
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Ben, Mohamed Ahmed Mohamed Abdellahi. "Résolution approchée du problème de bin-packing." Le Havre, 2009. http://www.theses.fr/2009LEHA0031.

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Pasha, Arfath. "Geometric bin packing algorithm for arbitrary shapes." [Gainesville, Fla.] : University of Florida, 2003. http://purl.fcla.edu/fcla/etd/UFE0000907.

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Books on the topic "Bin packing"

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Anderson, R. J. Parallel approximation algorithms for bin packing. Stanford, Calif: Dept. of Computer Science, Stanford University, 1988.

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Csirik, J. On the multidimensional vector bin packing. Brussels: European Institute for Advanced Studies in Management, 1990.

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Labbé, Martine. An exact algorithm for the dual bin packing problem. Brussels: European Institute for Advanced Studies in Management, 1993.

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Scheithauer, Guntram. 3 notes on cutting stock, bin packing, and container loading. Wrocław: University of Wrocław, 1990.

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Vanderbeck, François. Computational study of a column generation algorithm for bin packing and cutting stock problems. Cambridge: Judge Institute, 1996.

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Goessens, Walter. An exact calculation of the expected waste for a bin-packing algorithm using items that are exponentially distributed. Antwerpen: Universiteit Antwerpen, 1992.

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Tuenter, Hans J. H. Worst-case bounds for bin-packing heuristics with applications to the duality gap of the one-dimensional cutting stock problem. Birmingham: University of Birmingham, 1996.

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Packing for the big trip: Enhancing your life through awareness of death. Ventura, Calif: Pathfinder Pub., 1997.

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United States. Congress. House. Committee on Agriculture. Subcommittee on Livestock and Horticulture. Proposals to ban packer ownership of livestock: Hearing before the Subcommittee on Livestock and Horticulture of the Committee on Agriculture, House of Representatives, One Hundred Eighth Congress, first session, June 21, 2003, Grand Island, NE. Washington: U.S. G.P.O., 2003.

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United States. Congress. House. Committee on Agriculture. Subcommittee on Livestock and Horticulture. Proposals to ban packer ownership of livestock: Hearing before the Subcommittee on Livestock and Horticulture of the Committee on Agriculture, House of Representatives, One Hundred Eighth Congress, first session, June 21, 2003, Grand Island, NE. Washington: U.S. G.P.O., 2003.

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Book chapters on the topic "Bin packing"

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Korte, Bernhard, and Jens Vygen. "Bin-Packing." In Algorithms and Combinatorics, 471–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24488-9_18.

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Johnson, David S. "Bin Packing." In Encyclopedia of Algorithms, 207–11. New York, NY: Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-2864-4_49.

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Korte, Bernhard, and Jens Vygen. "Bin-Packing." In Kombinatorische Optimierung, 499–516. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-25401-7_18.

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Johnson, David S. "Bin Packing." In Encyclopedia of Algorithms, 94–97. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-30162-4_49.

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Korte, Bernhard, and Jens Vygen. "Bin-Packing." In Algorithms and Combinatorics, 407–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-21708-5_18.

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Korte, Bernhard, and Jens Vygen. "Bin-Packing." In Algorithms and Combinatorics, 407–22. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/978-3-662-21711-5_18.

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Vazirani, Vijay V. "Bin Packing." In Approximation Algorithms, 74–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-04565-7_9.

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Korte, Bernhard, and Jens Vygen. "Bin-Packing." In Algorithms and Combinatorics, 489–507. Berlin, Heidelberg: Springer Berlin Heidelberg, 2018. http://dx.doi.org/10.1007/978-3-662-56039-6_18.

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Coleman, Nastaran, and Pearl Wang. "Bin-Packing." In Encyclopedia of Operations Research and Management Science, 116–26. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_75.

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Korte, Bernhard, and Jens Vygen. "Bin-Packing." In UNITEXT, 475–93. Milano: Springer Milan, 2011. http://dx.doi.org/10.1007/978-88-470-1523-4_18.

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Conference papers on the topic "Bin packing"

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Miyazawa, Flavio K., and Andre L. Vignatti. "Distributed selfish bin packing." In Distributed Processing (IPDPS). IEEE, 2009. http://dx.doi.org/10.1109/ipdps.2009.5160881.

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Angelopoulos, Spyros, Shahin Kamali, and Kimia Shadkami. "Online Bin Packing with Predictions." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/635.

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Bin packing is a classic optimization problem with a wide range of applications from load balancing to supply chain management. In this work, we study the online variant of the problem, in which a sequence of items of various sizes must be placed into a minimum number of bins of uniform capacity. The online algorithm is enhanced with a (potentially erroneous) prediction concerning the frequency of item sizes in the sequence. We design and analyze online algorithms with efficient tradeoffs between the consistency (i.e., the competitive ratio assuming no prediction error) and the robustness (i.e., the competitive ratio under adversarial error), and whose performance degrades near-optimally as a function of the prediction error. This is the first theoretical and experimental study of online bin packing in the realistic setting of learnable predictions. Previous work addressed only extreme cases with respect to the prediction error, and relied on overly powerful and error-free oracles.
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Liu, Mozhengfu, and Xueyan Tang. "Dynamic Bin Packing with Predictions." In SIGMETRICS '23: ACM SIGMETRICS International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2023. http://dx.doi.org/10.1145/3578338.3593538.

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Bozejko, Wojciech, Lukasz Kacprzak, and Mieczyslaw Wodecki. "Parallel packing procedure for three dimensional bin packing problem." In 2015 20th International Conference on Methods and Models in Automation and Robotics (MMAR ). IEEE, 2015. http://dx.doi.org/10.1109/mmar.2015.7284036.

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Hasan, Jasim, Jihene Kaabi, and Youssef Harrath. "Multi-objective 3D bin-packing problem." In 2019 8th International Conference on Modeling Simulation and Applied Optimization (ICMSAO). IEEE, 2019. http://dx.doi.org/10.1109/icmsao.2019.8880442.

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Eisenbrand, Friedrich, Dömötör Pálvölgyi, and Thomas Rothvoß. "Bin Packing via Discrepancy of Permutations." In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611973082.38.

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Bansal, Nikhil, Marek Eliáš, and Arindam Khan. "Improved Approximation for Vector Bin Packing." In Proceedings of the Twenty-Seventh Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2015. http://dx.doi.org/10.1137/1.9781611974331.ch106.

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Gupta, Varun, and Ana Radovanovic. "Lagrangian-based Online Stochastic Bin Packing." In SIGMETRICS '15: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2015. http://dx.doi.org/10.1145/2745844.2745897.

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Balslev, Ivar, and Ren‰ D. Eriksen. "From belt picking to bin packing." In Optomechatronic Systems III, edited by Toru Yoshizawa. SPIE, 2002. http://dx.doi.org/10.1117/12.467380.

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Zhu, Zhilin, Jinxue Sui, and Li Yang. "Bin-packing Algorithms for Periodic Task Scheduling." In 2010 WASE International Conference on Information Engineering (ICIE 2010). IEEE, 2010. http://dx.doi.org/10.1109/icie.2010.145.

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Reports on the topic "Bin packing"

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Gaver, Donald P., and Patricia A. Jacobs. Asymptotic Properties of Stochastic Greedy Bin-Packing. Fort Belvoir, VA: Defense Technical Information Center, November 1993. http://dx.doi.org/10.21236/ada273378.

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van den Oever, Martien, Karin Molenveld, Maarten van der Zee, and Harriëtte Bos. Bio-based and biodegradable plastics : facts and figures : focus on food packaging in the Netherlands. Wageningen: Wageningen Food & Biobased Research, 2017. http://dx.doi.org/10.18174/408350.

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Staples, P. Details of the analysis algorithms provided for the implementation of the packaging program at the BN-350 reactor, MAEC, AKTAU, Kasakhstan. Office of Scientific and Technical Information (OSTI), April 1999. http://dx.doi.org/10.2172/334289.

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Rahimipour, Shai, and David Donovan. Renewable, long-term, antimicrobial surface treatments through dopamine-mediated binding of peptidoglycan hydrolases. United States Department of Agriculture, January 2012. http://dx.doi.org/10.32747/2012.7597930.bard.

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There is a need for renewable antimicrobial surface treatments that are semi- permanent, can eradicate both biofilms and planktonic pathogens over long periods of time and that do not select for resistant strains. This proposal describes a dopamine binding technology that is inexpensive, bio-friendly, non-toxic, and uses straight-forward commercially available products. The antimicrobial agents are peptidoglycanhydrolase enzymes that are non-toxic and highly refractory to resistance development. The goal of this project is to create a treatment that will be applicable to a wide variety of surfaces and will convey long-lasting antimicrobial activity. Although the immediate goal is to create staphylolytic surfaces, the technology should be applicable to any pathogen and will thus contribute to no less than 3 BARD priorities: 1) increased animal production by protecting animals from invasive and emerging diseases, 2) Antimicrobial food packaging will improve food safety and security and 3) sustainable bio- energy systems will be supported by coating fermentation vats with antimicrobials that could protect ethanolic fermentations from Lactobacillus contamination that reduces ethanol yields. The dopamine-based modification of surfaces is inspired by the strong adhesion of mussel adhesion proteins to virtually all types of surfaces, including metals, polymers, and inorganic materials. Peptidoglycanhydrolases (PGHs) meet the criteria of a surface bound antimicrobial with their site of action being extracellular peptidoglycan (the structural basis of the bacterial cell wall) that when breached causes osmotic lysis. As a proof of principle, we will develop technology using peptidoglycanhydrolase enzymes that target Staphylococcus aureus, a notoriously contagious and antimicrobial-resistant pathogen. We will test for susceptibility of the coating to a variety of environmental stresses including UV light, abrasive cleaning and dessication. In order to avoid resistance development, we intend to use three unique, synergistic, simultaneous staphylococcal enzyme activities. The hydrolases are modular such that we have created fusion proteins with three lytic activities that are highly refractory to resistance development. It is essential to use multiple simultaneous activities to avoid selecting for antimicrobial resistant strains. This strategy is applicable to both Gram positive and negative pathogens. We anticipate that upon completion of this award the technology will be available for commercialization within the time required to achieve a suitable high volume production scheme for the required enzymes (~1-2 years). We expect the modified surface will remain antimicrobial for several days, and when necessary, the protocol for renewal of the surface will be easily applied in a diverse array of environments, from food processing plants to barnyards.
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