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1

Pisinger, David. "Heuristics for the container loading problem." European Journal of Operational Research 141, no. 2 (September 2002): 382–92. http://dx.doi.org/10.1016/s0377-2217(02)00132-7.

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2

MOHAMMED, SHARIF, HUSAIN AZHAR, LATEEF MOHAMMED, and DAYOUB M. "Optimization of Makespan of Container Loading -Unloading Problem Using Mixed Integer Programming." International Journal of Earth Sciences and Engineering 10, no. 01 (March 6, 2017): 53–57. http://dx.doi.org/10.21276/ijee.2017.10.0108.

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3

Mohamed. "Ant Colony Optimization for Container Loading Problem." Journal of Mathematics and Statistics 8, no. 2 (February 1, 2012): 169–75. http://dx.doi.org/10.3844/jmssp.2012.169.175.

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4

Hifi, M. "Approximate algorithms for the container loading problem." International Transactions in Operational Research 9, no. 6 (November 2002): 747–74. http://dx.doi.org/10.1111/1475-3995.00386.

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5

Tian, Tian, Wenbin Zhu, Andrew Lim, and Lijun Wei. "The multiple container loading problem with preference." European Journal of Operational Research 248, no. 1 (January 2016): 84–94. http://dx.doi.org/10.1016/j.ejor.2015.07.002.

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6

Che, Chan Hou, Weili Huang, Andrew Lim, and Wenbin Zhu. "The multiple container loading cost minimization problem." European Journal of Operational Research 214, no. 3 (November 2011): 501–11. http://dx.doi.org/10.1016/j.ejor.2011.04.017.

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7

Torra, Vicenç, and Sadaaki Miyamoto. "Container Loading for Nonorthogonal Objects: Detecting Collisions." Journal of Advanced Computational Intelligence and Intelligent Informatics 12, no. 5 (September 20, 2008): 422–25. http://dx.doi.org/10.20965/jaciii.2008.p0422.

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This paper considers the problem of container loading. That is, the problem of locating a predefined set of objects into containers so that the number of containers is minimized. In this paper we outline our system, which includes as a distinguished aspect the possibility of considering objects that are nonorthogonal. That is, non rectangular objects are permitted to be located in the container.
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8

Scheithauer, G. "LP-based bounds for the container and multi-container loading problem." International Transactions in Operational Research 6, no. 2 (March 1999): 199–213. http://dx.doi.org/10.1111/j.1475-3995.1999.tb00151.x.

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9

Ramos, A. Galrão, José F. Oliveira, José F. Gonçalves, and Manuel P. Lopes. "Dynamic stability metrics for the container loading problem." Transportation Research Part C: Emerging Technologies 60 (November 2015): 480–97. http://dx.doi.org/10.1016/j.trc.2015.09.012.

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10

Moura, A., and J. F. Oliveira. "A GRASP Approach to the Container-Loading Problem." IEEE Intelligent Systems 20, no. 4 (July 2005): 50–57. http://dx.doi.org/10.1109/mis.2005.57.

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11

Chen, C. S., S. M. Lee, and Q. S. Shen. "An analytical model for the container loading problem." European Journal of Operational Research 80, no. 1 (January 1995): 68–76. http://dx.doi.org/10.1016/0377-2217(94)00002-t.

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12

Li, Xueping, and Kaike Zhang. "A hybrid differential evolution algorithm for multiple container loading problem with heterogeneous containers." Computers & Industrial Engineering 90 (December 2015): 305–13. http://dx.doi.org/10.1016/j.cie.2015.10.007.

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13

Guerra-Olivares, Roberto, Rosa G. González-Ramírez, and Neale R. Smith. "A Heuristic Procedure for the Outbound Container Relocation Problem during Export Loading Operations." Mathematical Problems in Engineering 2015 (2015): 1–13. http://dx.doi.org/10.1155/2015/201749.

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During export ship loading operations, it is often necessary to perform relocation movements with containers that interfere with access to the desired container in the ship loading sequence. This paper presents a real-time heuristic procedure for the container relocation problem employing reachstacker vehicles as container handling equipment. The proposed heuristic searches for good relocation coordinates within a set of nearby bays. The heuristic has a parameter that determines how far from the original bay a container may be relocated. The tradeoff between reducing relocation movements and limiting vehicle travel distances is examined and the performance of the heuristic is compared with a common practice in the smaller container terminals in Chile and Mexico. Finally, a mathematical model for the container relocation problem is presented.
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14

Pan, Li, Sydney C. K. Chu, Guangyue Han, and Joshua Zhexue Huang. "A Heuristic Algorithm for the Inner-City Multi-Drop." International Journal of Operations Research and Information Systems 2, no. 3 (July 2011): 1–19. http://dx.doi.org/10.4018/joris.2011070101.

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Economic globalization, increasing fuel cost, and environmental problems provide a strong stimulation for inner-city container carriers to utilize container space more efficiently in transporting goods for multiple clients during a single round trip. A wall-building heuristic algorithm based on the binary tree data structure is proposed to solve the container loading problem with multi-drop constraints. A dynamic space decomposition approach, together with a repacking and space amalgamation strategy, permits an efficient and effective loading plan to pack containers, illustrated by numerical experiments.
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15

Wang, Yan Lin, Hong Sheng Lu, and Ai Bing Yu. "A New Optimal Algorithm for Multi-Containers Packing Problem." Applied Mechanics and Materials 201-202 (October 2012): 983–86. http://dx.doi.org/10.4028/www.scientific.net/amm.201-202.983.

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Container packing has become more and more important for long-distance transportation as the development of logistics industry. A good container load plan will help to improve efficiency, reduce transportation cost and increase economic benefits. In this paper, we proposed a new optimal algorithm for multi-containers packing problem. The algorithm consists of a heuristic method which is for single-bin loading, and a genetic algorithm which is for multi-containers packing. A new placement nodes strategy was designed according to the container loading characteristics in the heuristics. Through this method, the number of placement nodes was reduced effectively. In genetic algorithm, an innovative chromosome encoding was presented, which is efficient to decrease the chromosome length. The simulation results show that the new algorithm is available and efficient. The proposed algorithm performs better in terms of space utilizations and running time.
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16

IWASAWA, Hiroki, Yannan HU, Hideki HASHIMOTO, Shinji IMAHORI, and Mutsunori YAGIURA. "A heuristic algorithm for the container loading problem with complex loading constraints." Journal of Advanced Mechanical Design, Systems, and Manufacturing 10, no. 3 (2016): JAMDSM0041. http://dx.doi.org/10.1299/jamdsm.2016jamdsm0041.

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17

Arefi, Mohammad Sadegh, and Hassan Rezaei. "Problem solving of container loading using genetic algorithm based on modified random keys." Journal of Advanced Computer Science & Technology 4, no. 1 (April 15, 2015): 190. http://dx.doi.org/10.14419/jacst.v4i1.4348.

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<p>This article presents a solution to the container loading problem. Container loading problem deals with how to put the cube boxes with different sizes in a container. Our proposed method is based on a particular kind of genetic algorithm based on biased random keys. In the proposed algorithm, we will face generations' extinction. Population decreases with time and with the staircase changes in the rate of elitism, the algorithm is guided towards the global optimum. Biased random keys in the proposed method are provided as discrete. The algorithm also provides the chromosomes that store more than one ability. In order to solve container loading using a placement strategy, due to the size of the boxes and containers, the containers are classified as small units and equal unites in size. Finally the algorithm presented in this paper was compared with three other methods that are based on evolutionary algorithms. The results show that the proposed algorithm has better performance in terms of results and performance time in relation to other methods.</p>
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18

LIU, Yuan, Yajie TIAN, and Tetsuo SAWARAGI. "Applying DBR and MAS to Solving Container Loading Problem." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 18, no. 6 (2006): 837–48. http://dx.doi.org/10.3156/jsoft.18.837.

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19

TAKAHARA, Shigeyuki. "Container Loading Problem with Load Stability and Weight Constraints." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 18, no. 6 (2006): 849–58. http://dx.doi.org/10.3156/jsoft.18.849.

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20

ENDO, Yasunori, Koki HANZAWA, and Yukihiro HAMASUNA. "Metaheuristic Algorithms for Container Loading Problem by Grouping Objects." Journal of Japan Society for Fuzzy Theory and Intelligent Informatics 18, no. 6 (2006): 859–66. http://dx.doi.org/10.3156/jsoft.18.859.

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21

Lim, Andrew, Hong Ma, Chaoyang Qiu, and Wenbin Zhu. "The single container loading problem with axle weight constraints." International Journal of Production Economics 144, no. 1 (July 2013): 358–69. http://dx.doi.org/10.1016/j.ijpe.2013.03.001.

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22

Bortfeldt, Andreas, and Hermann Gehring. "A hybrid genetic algorithm for the container loading problem." European Journal of Operational Research 131, no. 1 (May 2001): 143–61. http://dx.doi.org/10.1016/s0377-2217(00)00055-2.

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23

Parreño, F., R. Alvarez-Valdes, J. M. Tamarit, and J. F. Oliveira. "A Maximal-Space Algorithm for the Container Loading Problem." INFORMS Journal on Computing 20, no. 3 (August 2008): 412–22. http://dx.doi.org/10.1287/ijoc.1070.0254.

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24

Castellucci, Pedro B., Franklina M. B. Toledo, and Alysson M. Costa. "Output maximization container loading problem with time availability constraints." Operations Research Perspectives 6 (2019): 100126. http://dx.doi.org/10.1016/j.orp.2019.100126.

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25

Gehring, H., and A. Bortfeldt. "A Genetic Algorithm for Solving the Container Loading Problem." International Transactions in Operational Research 4, no. 5-6 (November 1997): 401–18. http://dx.doi.org/10.1111/j.1475-3995.1997.tb00095.x.

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26

Bandyopadhayay, Asit, and Dilip Roy. "A heuristic modelling for three-dimensional container loading problem." International Journal of Modelling in Operations Management 2, no. 4 (2012): 345. http://dx.doi.org/10.1504/ijmom.2012.049121.

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27

Lurkin, Virginie, and Michaël Schyns. "The Airline Container Loading Problem with pickup and delivery." European Journal of Operational Research 244, no. 3 (August 2015): 955–65. http://dx.doi.org/10.1016/j.ejor.2015.02.027.

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28

Araya, I., and M. C. Riff. "A beam search approach to the container loading problem." Computers & Operations Research 43 (March 2014): 100–107. http://dx.doi.org/10.1016/j.cor.2013.09.003.

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29

Liu, Sheng, Wei Tan, Zhiyuan Xu, and Xiwei Liu. "A tree search algorithm for the container loading problem." Computers & Industrial Engineering 75 (September 2014): 20–30. http://dx.doi.org/10.1016/j.cie.2014.05.024.

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30

Xiang, Xianbo, Caoyang Yu, He Xu, and Stuart X. Zhu. "Optimization of Heterogeneous Container Loading Problem with Adaptive Genetic Algorithm." Complexity 2018 (November 1, 2018): 1–12. http://dx.doi.org/10.1155/2018/2024184.

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This paper studies an optimized container loading problem with the goal of maximizing the 3D space utilization. Based on the characteristics of the mathematical loading model, we develop a dedicated placement heuristic integrated with a novel dynamic space division method, which enables the design of the adaptive genetic algorithm in order to maximize the loading space utilization. We use both weakly and strongly heterogeneous loading data to test the proposed algorithm. By choosing 15 classic sets of test data given by Loh and Nee as weakly heterogeneous data, the average space utilization of our algorithm reaching 70.62% outperforms those of 13 algorithms from the related literature. Taking a set of test data given by George and Robinson as strongly heterogeneous data, the space utilization in this paper can be improved by 4.42% in comparison with their heuristic algorithm.
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31

Cao, Fei. "Study on Container Loading Problem Based on NSGA-α Algorithm." Advanced Materials Research 753-755 (August 2013): 2954–58. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2954.

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Container loading problem with multi-constraints is usually modeled as a three-dimensional packing problem which is known to be NP-complete. This paper presents a novel approach to resolve the container loading problem via multi-objective optimization algorithm (NSGA-II). Specific encoding method and genetic operators are designed based on the mathematics description of the problem. The process also takes several practical constraints into account. Simulation results show that this approach is feasible and effective.
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32

Sahar, Dian Pratiwi, and Mohammad Thezar Afifudin. "PEMROGRAMAN LINEAR INTEGER DENGAN ENAM VARIABEL ORIENTASI KARGO UNTUK MASALAH MINIMISASI BIAYA PEMUATAN MULTI-KONTAINER." J@ti Undip : Jurnal Teknik Industri 15, no. 3 (August 29, 2020): 162–68. http://dx.doi.org/10.14710/jati.15.3.162-168.

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Penelitian ini bertujuan untuk mengembangkan model matematika untuk masalah minimisasi biaya pemuatan multi-kontainer dengan enam variabel orientasi kargo. Masalah ini dirumuskan sebagai model pemrograman linier biner integer untuk meminimalkan biaya. Faktor-faktor yang dipertimbangkan dalam formulasi termasuk alokasi kargo, lokasi kargo, hubungan kargo, dan orientasi kargo. Sedangkan, biaya yang dipertimbangkan termasuk biaya muatan volume kontainer ke kargo dan biaya transportasi kargo ke kontainer. Validasi model dilakukan melalui percobaan numerik pada ukuran kecil kargo dan kontainer. Hasil penelitian menunjukkan bahwa model dengan konsep orientasi kargo yang dikembangkan dapat menyelesaikan masalah sesuai dengan parameter numerik yang diberikan. Abstract[Integer Linear Programming with Six Cargo Orientation Variables for Multi-Container Loading Cost Minimization Problem] This research aims to develop the mathematic model for multi-container loading cost minimization problems with six cargo orientation variables. The problem is formulated as a binary integer linear programming model to minimize costs. The factors considered in the formulation include cargo allocation, cargo location, cargo relations, and cargo orientation. Whereas, the costs considered include the container volume load cost to cargo and the cargo transport cost to the container. Model validation is performed through numerical experiments on the small size of cargo and containers. The results show that the model with developed cargo orientation concept can solve the problem according to the given numerical parameters.Keywords: integer programming; cargo orientation; container loading; cost minimization
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33

Wang, Shuaian, Xiaobo Qu, Tingsong Wang, and Wen Yi. "Optimal Container Routing in Liner Shipping Networks Considering Repacking 20 ft Containers into 40 ft Containers." Journal of Advanced Transportation 2017 (2017): 1–9. http://dx.doi.org/10.1155/2017/8608032.

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The volume of a 40 ft container is twice as large as that of a 20 ft container. However, the handling cost (loading, unloading, and transshipment) of a 40 ft container is much lower than twice the corresponding handling cost of two 20 ft containers. Enlightened by this observation, we propose a novel container routing with repacking problem in liner shipping, where two 20 ft containers can be repacked to a 40 ft container in order to reduce the handling cost. We develop a mixed-integer linear programming model that formulates the routing decisions and the repacking decisions in a holistic manner. An illustrative example is reported to demonstrate the applicability of the model. Results show that the benefit of repacking is the most significant when containers are transshipped several times.
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34

Dereli, Türkay, and Gülesin Sena Daş. "DEVELOPMENT OF A DECISION SUPPORT SYSTEM FOR SOLVING CONTAINER LOADING PROBLEMS." TRANSPORT 25, no. 2 (June 30, 2010): 138–47. http://dx.doi.org/10.3846/transport.2010.17.

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The globalization of supply chains and rising fuel costs are forcing container carriers both to minimize the number of trips and to maximize available container space. This makes container loading (CL) a critical process especially in real‐life applications. Container loading (CL), which is a difficult problem to be solved, has many applications in container transportation and distribution industries. This article presents a container loading support system (CLSS). The proposed CLSS composes of three main components, including a hybrid Bees Algorithm as the main computational algorithm, the graphical user interface (GUI) and a simulation program. The aim of the designed system is to make the packing pattern more visible to the user in order to simplify the loading process. An illustrative example ‐a CL problem from literature ‐ is also provided to introduce the operation of the system and to prove its efficiency.
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35

Xia, Mengjue, Youmei Li, Yifan Shen, and Ning Zhao. "Loading Sequencing Problem in Container Terminal with Deep Q-Learning." Journal of Coastal Research 103, sp1 (June 23, 2020): 817. http://dx.doi.org/10.2112/si103-169.1.

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36

Parreño, F., R. Alvarez-Valdes, J. F. Oliveira, and J. M. Tamarit. "Neighborhood structures for the container loading problem: a VNS implementation." Journal of Heuristics 16, no. 1 (May 24, 2008): 1–22. http://dx.doi.org/10.1007/s10732-008-9081-3.

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37

Ceschia, Sara, and Andrea Schaerf. "Local search for a multi-drop multi-container loading problem." Journal of Heuristics 19, no. 2 (February 19, 2011): 275–94. http://dx.doi.org/10.1007/s10732-011-9162-6.

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38

Fanslau, Tobias, and Andreas Bortfeldt. "A Tree Search Algorithm for Solving the Container Loading Problem." INFORMS Journal on Computing 22, no. 2 (May 2010): 222–35. http://dx.doi.org/10.1287/ijoc.1090.0338.

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39

Pino, Raul, Isabel Fernandez, David De La Fuente, and Nazario Garcia. "A genetic algorithm approach to a 3D container loading problem." International Journal of Logistics Systems and Management 10, no. 2 (2011): 192. http://dx.doi.org/10.1504/ijlsm.2011.042628.

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40

Ren, Jidong, Yajie Tian, and Tetsuo Sawaragi. "A priority-considering approach for the multiple container loading problem." International Journal of Metaheuristics 1, no. 4 (2011): 298. http://dx.doi.org/10.1504/ijmheur.2011.044314.

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41

Gehring, Hermann, and Andreas Bortfeldt. "A Parallel Genetic Algorithm for Solving the Container Loading Problem." International Transactions in Operational Research 9, no. 4 (July 2002): 497–511. http://dx.doi.org/10.1111/1475-3995.00369.

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42

Morabito, Reinaldo, and Marcos Arenalest. "An AND/OR‐graph Approach to the Container Loading Problem." International Transactions in Operational Research 1, no. 1 (January 1994): 59–73. http://dx.doi.org/10.1111/1475-3995.d01-8.

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43

Eley, Michael. "A bottleneck assignment approach to the multiple container loading problem." OR Spectrum 25, no. 1 (February 1, 2003): 45–60. http://dx.doi.org/10.1007/s002910200113.

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44

Araya, Ignacio, Mauricio Moyano, and Cristobal Sanchez. "A beam search algorithm for the biobjective container loading problem." European Journal of Operational Research 286, no. 2 (October 2020): 417–31. http://dx.doi.org/10.1016/j.ejor.2020.03.040.

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45

Morabito, R. "An AND/OR-graph approach to the container loading problem." International Transactions in Operational Research 1, no. 1 (January 1994): 59–73. http://dx.doi.org/10.1016/0969-6016(94)90046-9.

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46

Huang, Yao-Huei, F. J. Hwang, and Hao-Chun Lu. "An effective placement method for the single container loading problem." Computers & Industrial Engineering 97 (July 2016): 212–21. http://dx.doi.org/10.1016/j.cie.2016.05.008.

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47

Sheng, Liu, Shang Xiuqin, Cheng Changjian, Zhao Hongxia, Shen Dayong, and Wang Feiyue. "Heuristic algorithm for the container loading problem with multiple constraints." Computers & Industrial Engineering 108 (June 2017): 149–64. http://dx.doi.org/10.1016/j.cie.2017.04.021.

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48

Huang, Wenqi, and Kun He. "A caving degree approach for the single container loading problem." European Journal of Operational Research 196, no. 1 (July 2009): 93–101. http://dx.doi.org/10.1016/j.ejor.2008.02.024.

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49

Ranck Júnior, Rodolfo, Horacio Hideki Yanasse, Reinaldo Morabito, and Leonardo Junqueira. "A hybrid approach for a multi-compartment container loading problem." Expert Systems with Applications 137 (December 2019): 471–92. http://dx.doi.org/10.1016/j.eswa.2019.07.020.

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50

Rachman, Hendriyono, and Ahmad Rusdiansyah. "PENGEMBANGAN RANCANG BANGUN GAME EDUKASI LOGISTIK "STOWAGAME" MENGENAI PENATAAN KONTAINER DI BAY KAPAL." Jurnal Teknik Industri 13, no. 1 (March 29, 2012): 67. http://dx.doi.org/10.22219/jtiumm.vol13.no1.67-77.

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Development of educational games focuss on the problem occured on bay management, primarily on loading containerat ship bay. The loading plan problem in ship bay commonly known as Master Bay Plan Problem (MBPP). The complexityof MBPP can be seen on its constraints, that are container weight, container destination, container size, container type, and the arrangement rule of container. The study case and playing rules in this game development are adjusted to contextual theory and real situation occured in container arrangement at ship bay. The prototype of this game is called StowaGame. StowaGame is a simulation-based educational games related to container arrangement in ship bay. This software are built with the integration of MBPP and entertainment concept. The effectivity of this educational game will be tested by several player. The MBPP concept of each player will be tested before and after playing the game. Paired-t test calculation shows that p-value is less than 0,05. Thus, it can be concluded that the educational game can educate people with container arrangement concept in ship bay.
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