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Journal articles on the topic 'Bin packing'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

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

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2

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

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3

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

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4

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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5

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 (2018): 467–76. http://dx.doi.org/10.1007/s10898-018-0696-0.

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6

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

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7

Yang, Jianglong, Kaibo Liang, Huwei Liu, et al. "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

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8

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 (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

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9

Shah, Devavrat, and John N. Tsitsiklis. "Bin Packing with Queues." Journal of Applied Probability 45, no. 4 (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 t

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10

Shah, Devavrat, and John N. Tsitsiklis. "Bin Packing with Queues." Journal of Applied Probability 45, no. 04 (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 t

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11

Kim, Jong-Kyou, H. Lee-Kwang, and Seung W. Yoo. "Fuzzy bin packing problem." Fuzzy Sets and Systems 120, no. 3 (2001): 429–34. http://dx.doi.org/10.1016/s0165-0114(99)00073-1.

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12

Dosa, Gyorgy. "Batched bin packing revisited." Journal of Scheduling 20, no. 2 (2015): 199–209. http://dx.doi.org/10.1007/s10951-015-0431-3.

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13

Coffman, Edward G., János Csirik, Lajos Rónyai, and Ambrus Zsbán. "Random-order bin packing." Discrete Applied Mathematics 156, no. 14 (2008): 2810–16. http://dx.doi.org/10.1016/j.dam.2007.11.004.

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14

Friesen, D. K., and M. A. Langston. "Variable Sized Bin Packing." SIAM Journal on Computing 15, no. 1 (1986): 222–30. http://dx.doi.org/10.1137/0215016.

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15

Epstein, Leah, David S. Johnson, and Asaf Levin. "Min-Sum Bin Packing." Journal of Combinatorial Optimization 36, no. 2 (2018): 508–31. http://dx.doi.org/10.1007/s10878-018-0310-x.

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16

Baldi, Mauro Maria. "Generalized Bin Packing Problems." 4OR 12, no. 3 (2013): 293–94. http://dx.doi.org/10.1007/s10288-013-0252-1.

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17

Fukunaga, A. S., and R. E. Korf. "Bin Completion Algorithms for Multicontainer Packing, Knapsack, and Covering Problems." Journal of Artificial Intelligence Research 28 (March 30, 2007): 393–429. http://dx.doi.org/10.1613/jair.2106.

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Many combinatorial optimization problems such as the bin packing and multiple knapsack problems involve assigning a set of discrete objects to multiple containers. These problems can be used to model task and resource allocation problems in multi-agent systems and distributed systms, and can also be found as subproblems of scheduling problems. We propose bin completion, a branch-and-bound strategy for one-dimensional, multicontainer packing problems. Bin completion combines a bin-oriented search space with a powerful dominance criterion that enables us to prune much of the space. The performan

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18

Wong, Ching-Chang, Tai-Ting Tsai, and Can-Kun Ou. "Integrating Heuristic Methods with Deep Reinforcement Learning for Online 3D Bin-Packing Optimization." Sensors 24, no. 16 (2024): 5370. http://dx.doi.org/10.3390/s24165370.

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This study proposes a method named Hybrid Heuristic Proximal Policy Optimization (HHPPO) to implement online 3D bin-packing tasks. Some heuristic algorithms for bin-packing and the Proximal Policy Optimization (PPO) algorithm of deep reinforcement learning are integrated to implement this method. In the heuristic algorithms for bin-packing, an extreme point priority sorting method is proposed to sort the generated extreme points according to their waste spaces to improve space utilization. In addition, a 3D grid representation of the space status of the container is used, and some partial supp

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19

Chekanin, Vladislav A., and Alexander V. Chekanin. "Multilevel Linked Data Structure for the Multidimensional Orthogonal Packing Problem." Applied Mechanics and Materials 598 (July 2014): 387–91. http://dx.doi.org/10.4028/www.scientific.net/amm.598.387.

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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 applica

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20

Rhee, W. T. "Some inequalities for bin packing." Optimization 20, no. 3 (1989): 299–304. http://dx.doi.org/10.1080/02331938908843445.

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21

Rhee, W. T. "Inequalities for bin packing-III." Optimization 29, no. 4 (1994): 381–85. http://dx.doi.org/10.1080/02331939408843965.

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22

Angelopoulos, Spyros, Shahin Kamali, and Kimia Shadkami. "Online Bin Packing with Predictions." Journal of Artificial Intelligence Research 78 (December 20, 2023): 1111–41. http://dx.doi.org/10.1613/jair.1.14820.

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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, which is the competitive ratio assuming no prediction error, and the robustness, w

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23

Baldi, Mauro Maria, Teodor Gabriel Crainic, Guido Perboli, and Roberto Tadei. "The generalized bin packing problem." Transportation Research Part E: Logistics and Transportation Review 48, no. 6 (2012): 1205–20. http://dx.doi.org/10.1016/j.tre.2012.06.005.

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24

Berndt, Sebastian, Klaus Jansen, and Kim-Manuel Klein. "Fully dynamic bin packing revisited." Mathematical Programming 179, no. 1-2 (2018): 109–55. http://dx.doi.org/10.1007/s10107-018-1325-x.

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25

Epstein, Leah, and Meital Levy. "Dynamic multi-dimensional bin packing." Journal of Discrete Algorithms 8, no. 4 (2010): 356–72. http://dx.doi.org/10.1016/j.jda.2010.07.002.

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26

Mao, W. "Besk-k-Fit bin packing." Computing 50, no. 3 (1993): 265–70. http://dx.doi.org/10.1007/bf02243816.

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27

Rao, R. L., and S. S. Iyengar. "Bin-packing by simulated annealing." Computers & Mathematics with Applications 27, no. 5 (1994): 71–82. http://dx.doi.org/10.1016/0898-1221(94)90077-9.

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28

Epstein, Leah. "More on batched bin packing." Operations Research Letters 44, no. 2 (2016): 273–77. http://dx.doi.org/10.1016/j.orl.2016.02.006.

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29

BUJTÁS, CSILLA, GYÖRGY DÓSA, CSANÁD IMREH, JUDIT NAGY-GYÖRGY, and ZSOLT TUZA. "THE GRAPH-BIN PACKING PROBLEM." International Journal of Foundations of Computer Science 22, no. 08 (2011): 1971–93. http://dx.doi.org/10.1142/s012905411100915x.

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We deal with a very general problem: a given graph G is to be "packed" into a host graph H, and we are asked about some natural optimization questions concerning this packing. The problem has never been investigated before in this general form. The input of the problem is a simple graph G = (V, E) with lower and upper bounds on its edges and weights on its vertices. The vertices correspond to items which have to be packed into the vertices (bins) of a host graph, such that each host vertex can accommodate at most L weight in total, and if two items are adjacent in G, then the distance of their

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30

Epstein, Leah, Csanád Imreh, and Asaf Levin. "Class constrained bin packing revisited." Theoretical Computer Science 411, no. 34-36 (2010): 3073–89. http://dx.doi.org/10.1016/j.tcs.2010.04.037.

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31

Azar, Boyar, Favrholdt, Larsen, Nielsen, and Epstein. "Fair versus Unrestricted Bin Packing." Algorithmica 34, no. 2 (2002): 181–96. http://dx.doi.org/10.1007/s00453-002-0965-6.

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32

Epstein, Leah. "Bin Packing with Rejection Revisited." Algorithmica 56, no. 4 (2008): 505–28. http://dx.doi.org/10.1007/s00453-008-9188-9.

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33

Boyar, Joan, Shahin Kamali, Kim S. Larsen, and Alejandro López-Ortiz. "Online Bin Packing with Advice." Algorithmica 74, no. 1 (2014): 507–27. http://dx.doi.org/10.1007/s00453-014-9955-8.

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34

Rhee, Wansoo T. "Inequalities for Bin Packing—II." Mathematics of Operations Research 18, no. 3 (1993): 685–93. http://dx.doi.org/10.1287/moor.18.3.685.

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35

Epstein, Leah, and Asaf Levin. "On Bin Packing with Conflicts." SIAM Journal on Optimization 19, no. 3 (2008): 1270–98. http://dx.doi.org/10.1137/060666329.

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36

Liu, Mozhengfu, and Xueyan Tang. "Dynamic Bin Packing with Predictions." Proceedings of the ACM on Measurement and Analysis of Computing Systems 6, no. 3 (2022): 1–24. http://dx.doi.org/10.1145/3570605.

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The MinUsageTime Dynamic Bin Packing (DBP) problem aims to minimize the accumulated bin usage time for packing a sequence of items into bins. It is often used to model job dispatching for optimizing the busy time of servers, where the items and bins match the jobs and servers respectively. It is known that the competitiveness of MinUsageTime DBP has tight bounds of Θ(√łog μ ) and Θ(μ) in the clairvoyant and non-clairvoyant settings respectively, where μ is the max/min duration ratio of all items. In practice, the information about the items' durations (i.e., job lengths) obtained via predictio

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37

Kinnerseley, Nancy G., and Michael A. Langston. "Online variable-sized bin packing." Discrete Applied Mathematics 22, no. 2 (1988): 143–48. http://dx.doi.org/10.1016/0166-218x(88)90089-3.

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38

Wang, Zhenbo, and Kameng Nip. "Bin packing under linear constraints." Journal of Combinatorial Optimization 34, no. 4 (2017): 1198–209. http://dx.doi.org/10.1007/s10878-017-0140-2.

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39

Bilò, Vittorio, Francesco Cellinese, Giovanna Melideo, and Gianpiero Monaco. "Selfish colorful bin packing games." Journal of Combinatorial Optimization 40, no. 3 (2020): 610–35. http://dx.doi.org/10.1007/s10878-020-00599-9.

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40

Bódis, Attila, and János Balogh. "Bin packing problem with scenarios." Central European Journal of Operations Research 27, no. 2 (2018): 377–95. http://dx.doi.org/10.1007/s10100-018-0574-3.

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41

Liu, Mozhengfu, and Xueyan Tang. "Dynamic Bin Packing with Predictions." ACM SIGMETRICS Performance Evaluation Review 51, no. 1 (2023): 57–58. http://dx.doi.org/10.1145/3606376.3593538.

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The MinUsageTime Dynamic Bin Packing (DBP) problem aims to minimize the accumulated bin usage time for packing a sequence of items into bins. It is often used to model job dispatching for optimizing the busy time of servers, where the items and bins match the jobs and servers respectively. It is known that the competitiveness of MinUsageTime DBP has tight bounds of Θ(√, log μ) and Θ(μ) in the clairvoyant and non-clairvoyant settings respectively, where μ is the max/min duration ratio of all items. In practice, the information about items' durations (i.e., job lengths) obtained via predictions

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42

Gai, Ling, Weiwei Zhang, and Zhao Zhang. "Selfish bin packing with punishment." Theoretical Computer Science 982 (January 2024): 114276. http://dx.doi.org/10.1016/j.tcs.2023.114276.

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43

Wu, Yong, Wenkai Li, Mark Goh, and Robert de Souza. "Three-dimensional bin packing problem with variable bin height." European Journal of Operational Research 202, no. 2 (2010): 347–55. http://dx.doi.org/10.1016/j.ejor.2009.05.040.

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44

Li, Chung-Lun, and Zhi-Long Chen. "Bin-packing problem with concave costs of bin utilization." Naval Research Logistics 53, no. 4 (2006): 298–308. http://dx.doi.org/10.1002/nav.20142.

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45

Courcobetis, Coastas, and Richard Weber. "Stability of On-Line Bin Packing with Random Arrivals and Long-Run-Average Constraints." Probability in the Engineering and Informational Sciences 4, no. 4 (1990): 447–60. http://dx.doi.org/10.1017/s0269964800001753.

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Items of various types arrive at a bin-packing facility according to random processes and are to be combined with other readily available items of different types and packed into bins using one of a number of possible packings. One might think of a manufacturing context in which randomly arriving subassemblies are to be combined with subassemblies from an existing inventory to assemble a variety of finished products. Packing must be done on-line; that is, as each item arrives, it must be allocated to a bin whose configuration of packing is fixed. Moreover, it is required that the packing be ma

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46

Perić, Nikica, Anđelko Kolak, and Vinko Lešić. "Modular Coordination of Vehicle Routing and Bin Packing Problems in Last Mile Logistics." Logistics 9, no. 2 (2025): 70. https://doi.org/10.3390/logistics9020070.

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Background: Logistics and transport, core of many business processes, are continuously optimized to improve efficiency and market competitiveness. The paper describes a modular coordination of vehicle routing and bin packing problems that enables independent instances of the problems to be joined together, with the aim that the vehicle routing solution satisfies all the constraints from real-world applications. Methods: The vehicle routing algorithm is based on an adaptive memory procedure that also incorporates a simple, one-dimensional bin packing problem. This preliminary packing solution i

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47

Jansen, Klaus, and Kim-Manuel Klein. "About the Structure of the Integer Cone and Its Application to Bin Packing." Mathematics of Operations Research 45, no. 4 (2020): 1498–511. http://dx.doi.org/10.1287/moor.2019.1040.

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We consider the bin packing problem with d different item sizes and revisit the structure theorem given by Goemans and Rothvoß about solutions of the integer cone. We present new techniques on how solutions can be modified and give a new structure theorem that relies on the set of vertices of the underlying integer polytope. As a result of our new structure theorem, we obtain an algorithm for the bin packing problem with running time [Formula: see text], where V is the set of vertices of the integer knapsack polytope, and [Formula: see text] is the encoding length of the bin packing instance.

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48

Qiao, Gaoyang. "A variable neighborhood search framework combining improved simulated annealing search to solve the one-dimensional crating problem." Applied and Computational Engineering 4, no. 1 (2023): 364–69. http://dx.doi.org/10.54254/2755-2721/4/20230489.

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The N-P problem, similar to bin packing, has a great impact on us in real life. Similar to the classical combinatorial problem, with the expansion of the problem size, usually there is no way to solve it by enumeration. The common solution is to solve by heuristic methods, in addition to the improved local search method by forbidden search or simulated annealing to solve. However, due to the multivariate nature of the bin packing problem, it is difficult to solve most of the bin packing problems by the above single method. In this paper, a variable neighborhood search combined with an improved

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49

Peeters, Marc, and Zeger Degraeve. "Branch-and-price algorithms for the dual bin packing and maximum cardinality bin packing problem." European Journal of Operational Research 170, no. 2 (2006): 416–39. http://dx.doi.org/10.1016/j.ejor.2004.06.034.

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50

Wei, Lijun, Wee-Chong Oon, Wenbin Zhu, and Andrew Lim. "A goal-driven approach to the 2D bin packing and variable-sized bin packing problems." European Journal of Operational Research 224, no. 1 (2013): 110–21. http://dx.doi.org/10.1016/j.ejor.2012.08.005.

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