Relevant bibliographies by topics / Container relocation problem

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  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Container relocation problem'

Author: Grafiati
Published: 4 June 2021
Last updated: 14 February 2022

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Journal articles on the topic "Container relocation problem"

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Maglić, Livia, Marko Gulić, and Lovro Maglić. "OPTIMIZATION OF CONTAINER RELOCATION OPERATIONS IN PORT CONTAINER TERMINALS." Transport 35, no. 1 (December 9, 2019): 37–47. http://dx.doi.org/10.3846/transport.2019.11628.

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The relocation of containers is a crucial operation in container ports all around the world. The Container Relocation Problem (CRP) is focused upon to find a sequence of container retrievals in a defined order from a single yard container bay with a minimum number of relocations. The goal of this paper is to find out if Genetic Algorithm (GA) can give new insights in the problem of solving the CRP. In this paper we focus on the two-dimensional, static, offline and restricted CRP of real-world yard container bays. Four rules are proposed for determining the position of relocated containers. We applied GA to find the best sequence of container retrievals according to these four rules in order to minimize the number of relocations within the bay. The experimental testing was run on a total of 800 different instances with varying bay sizes and number of containers. The given results are compared with the results of different authors using other heuristic methods. The results show that the proposed model solves CRP and achieves near optimal solutions.
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Galle, V., V. H. Manshadi, S. Borjian Boroujeni, C. Barnhart, and P. Jaillet. "The Stochastic Container Relocation Problem." Transportation Science 52, no. 5 (October 2018): 1035–58. http://dx.doi.org/10.1287/trsc.2018.0828.

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Li, Jing, and Yong Bo Lv. "Optimizing Container Reshuffle Operations in Container Yards Based on Dynamic Programming." Applied Mechanics and Materials 556-562 (May 2014): 5972–75. http://dx.doi.org/10.4028/www.scientific.net/amm.556-562.5972.

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Based on the multiple phase characteristics of container reshuffle operations, the paper establishes a dynamic programming model according to both the initial stock positions and the picking sequence of containers. Then a directed weight figure is put forward through connecting different states by the order. The paper uses the bi-recursive algorithm, which combines the sequential recursive algorithm with the inverted recursive algorithm, to solve the problem. The optimization of container relocation scheduling is realized by avoiding the second relocation for the same container and minimizing the total relocating time.
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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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Ku, Dusan, and Tiru S. Arthanari. "Container relocation problem with time windows for container departure." European Journal of Operational Research 252, no. 3 (August 2016): 1031–39. http://dx.doi.org/10.1016/j.ejor.2016.01.055.

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Lin, Dung-Ying, Yen-Ju Lee, and Yusin Lee. "The container retrieval problem with respect to relocation." Transportation Research Part C: Emerging Technologies 52 (March 2015): 132–43. http://dx.doi.org/10.1016/j.trc.2015.01.024.

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Zweers, Bernard G., Sandjai Bhulai, and Rob D. van der Mei. "Optimizing pre-processing and relocation moves in the Stochastic Container Relocation Problem." European Journal of Operational Research 283, no. 3 (June 2020): 954–71. http://dx.doi.org/10.1016/j.ejor.2019.11.067.

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Zhang, Canrong, Hao Guan, Yifei Yuan, Weiwei Chen, and Tao Wu. "Machine learning-driven algorithms for the container relocation problem." Transportation Research Part B: Methodological 139 (September 2020): 102–31. http://dx.doi.org/10.1016/j.trb.2020.05.017.

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Feng, Yuanjun, Dong-Ping Song, Dong Li, and Qingcheng Zeng. "The stochastic container relocation problem with flexible service policies." Transportation Research Part B: Methodological 141 (November 2020): 116–63. http://dx.doi.org/10.1016/j.trb.2020.09.006.

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Zhu, Wenbin, Hu Qin, Andrew Lim, and Huidong Zhang. "Iterative Deepening A* Algorithms for the Container Relocation Problem." IEEE Transactions on Automation Science and Engineering 9, no. 4 (October 2012): 710–22. http://dx.doi.org/10.1109/tase.2012.2198642.

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Dissertations / Theses on the topic "Container relocation problem"

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Zehendner, Elisabeth. "Operations management at container terminals using advanced information technologies." Phd thesis, Ecole Nationale Supérieure des Mines de Saint-Etienne, 2013. http://tel.archives-ouvertes.fr/tel-00972071.

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Container terminals use intelligent freight technologies (e.g., EDI, RFID and GPS) to exchange data with their partners, to locate containers and equipment within the terminal, and to automate tasks. This thesis illustrated, via two examples, how this data may be used to optimize operations at the terminal.The first part uses information on announced volumes to allocate internal handling equipment. The objective is to minimize overall delays at the terminal. The problem is represented as a network flow problem and implemented as a linear mixed integer programming model. A case study for a terminal at the Grand Port Maritime de Marseille is carried out. We also showed that combining the allocation problem with the dimensioning of a truck appointment system may reduce overall delays at the terminal. The second part uses information on announced container retrievals and container positions to improve retrieval operations. The objective is to retrieve containers from a bay in a given sequence with a minimum number of parasite relocations. We improve an existing binary programming model and introduce an exact branch and price approach - with a binary subproblem and two variants of an enumerative subproblem - and a heuristic branch and price approach - with a heuristic subproblem. The exact approach solves only small instances; the heuristic approach performs well on several instances, but should be improved further. We also deal with a dynamic version of the problem where the retrieval order becomes revealed over time and evaluate different relocation strategies for this case.
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Yang, Wen-Fu, and 楊文富. "An Artificial Neural Network-Based Method for the Container Relocation Problem." Thesis, 2019. http://ndltd.ncl.edu.tw/handle/rvknha.

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碩士
國立東華大學
運籌管理研究所
107
Container transportation have become more important in modern world because goods in containers are more valuable than other means of maritime transportation. Thus, people hope to effectively transport containers. Container terminals help transship containers between vessels and trucks. The Container Relocation Problem (CRP) is an issue related to the improvement of container terminals. There have been methods proposed for the CRP. Here, we first choose two different heuristics, Look-ahead N and Min-Max for the CRP, and apply Artificial Neural Network (ANN) to imitate how these two heuristics reshuffle containers; then by learning from the best of the two heuristics we check whether the performance of ANN can surpass them. We do experiments on two types of bay size: 4-row, 3-column, and 7-container bay size (small bay) and 4-row, 6-row, and 18-container bay size (large bay). Besides following the logic of two heuristics to generate datasets, we form a new type of datasets by combining best data instances of two heuristics. We train many ANNs for Min-Max, Look-ahead N and Best-of-Two to set their parameter values. Then we use the trained parameters from different ANNs to reshuffle containers and compare the results with the original reshuffle results of heuristics. ANN perfectly imitates the two heuristics and surpasses them in combined datasets that we generate for small bay size. For large bay size, ANN is unable to imitate nor surpass the two heuristics but the results are very close to them. In the end, we do further analysis on two methods to reduce computational time of training ANNs.
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Wu, Kun-Chih, and 吳崑誌. "Heuristics and Branch-and-Bound Algorithms for Container Relocation Problems." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/986sgu.

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博士
元智大學
工業工程與管理學系
105
Container terminal faces a huge challenge of handling the growing of container volume. Because of the increase in worldwide container trade and the ever-growing mega ships, container terminals have to improve their productivity and efficiency. One of the critical issues for optimizing terminal operations is container relocation, which is a time consuming but a non-value-added activity. Hence, reducing container relocation is an important issue in the operational aspect of the terminal. Container relocation problem (CRP) is considered to minimize the number of relocations when containers are retrieved from a container yard, which has been proven as an NP-hard problem. CRP can classified into restricted container relocation problem (RCRP) and unrestricted container relocation problem (UCRP) according to the restriction on the container movement. Construction heuristic is a typical method to build initial solutions for other improvement based algorithms, which plays an important role as a fundamental component for many heuristics. In this study, two construction heuristics, smallest difference heuristic (SDH) and virtual relocation heuristic (VRH), are proposed for the RCRP. A beam search algorithm (BS) is also applied to the RCRP for further improvement, in which the sum of VRH and the lower bound is used for the evaluation of beam nodes. Then, the unrestricted versions of the VRH and the BS are also developed to solve the UCRP, by taking the characteristics of UCRP into account. The experimental results show the proposed VRH and BS outperform the existing heuristics from the literature. Branch and bound (B&B), which is based on the depth-first tree search, is also developed to obtain the optimal solution for both RCRP and UCRP. To further reduce the search space, this study proposes a pattern tree and recycle strategy to recognize and eliminate the duplicate patterns. The experimental results show the proposed B&B is better than the existing exact solution methods for RCRP, but it does not perform well in the large size instances of UCRP. Nevertheless, the proposed pattern tree strategy can successfully reduce the search space around 70% in the large size instances of UCRP.
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Book chapters on the topic "Container relocation problem"

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Jin, Bo, Andrew Lim, and Wenbin Zhu. "A Greedy Look-Ahead Heuristic for the Container Relocation Problem." In Recent Trends in Applied Artificial Intelligence, 181–90. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38577-3_19.

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Zhang, Huidong, Songshan Guo, Wenbin Zhu, Andrew Lim, and Brenda Cheang. "An Investigation of IDA* Algorithms for the Container Relocation Problem." In Trends in Applied Intelligent Systems, 31–40. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13022-9_4.

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Karpuzoğlu, Osman, M. Hakan Akyüz, and Temel Öncan. "A Tabu Search Based Heuristic Approach for the Dynamic Container Relocation Problem." In Operations Research Proceedings, 165–71. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-42902-1_22.

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Conference papers on the topic "Container relocation problem"

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Zhang, Canrong, and Hao Guan. "A data-driven exact algorithm for the container relocation problem." In 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, 2020. http://dx.doi.org/10.1109/case48305.2020.9216846.

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ElWakil, Mohamed, Mohamed Gheith, and Amr Eltawil. "A New Simulated Annealing Based Method for the Container Relocation Problem." In 2019 6th International Conference on Control, Decision and Information Technologies (CoDIT). IEEE, 2019. http://dx.doi.org/10.1109/codit.2019.8820687.

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Xiang, Qingan, Jian Deng, Dahuan Zhu, Xiaoli Wu, Jinsheng Bi, Baowen Chen, Rong Cai, Libo Qian, and Yugao Ma. "Stratification and Heat Transfer of Molten Corium Pool for In-Vessel Retention." In 2020 International Conference on Nuclear Engineering collocated with the ASME 2020 Power Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/icone2020-16742.

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Abstract In-vessel retention (IVR) consists in cooling the molten corium contained in the lower head of reactor vessel by natural convection and reactor cavity flooding. The general approach which is used to study IVR problems is a “bounding” approach which consists in assuming a specified corium pool stratification in the vessel and then demonstrating that the vessel can cope with the resulting thermal and mechanical loads. Thermal loading on the vessel is controlled by the convective heat transfer inside the molten corium pool. Traditionally molten corium pool in the lower head was expected to stratify into two-layer with the dense oxide pool at the bottom and the light metal layer on the top. Based on the MASCA experiments, the increased density of the metal layer attributed to a transfer of uranium metal leads to inverse stratification with a heavy metal layer relocating below the oxide pool. This behavior can be explained by physicochemical interaction between the oxidic and metallic phases of the corium pool. Therefore, a methodology which couples physicochemical effects and thermal hydraulics has been developed to address the IVR issue. The main purpose of this paper is to present this methodology and its application for calculate stratification probability of two-layer and three-layer configuration, analyze the safety margin of IVR in two-layer and three-layer configuration, and evaluate the lower head heat thermal failure probability.
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