Abstract
This research is focused on the analysis of a container terminal common in industrial zones in urban areas, called the logistic services container terminal (LSCT), defined as a small to medium size inland terminal with no intermodal facilities. The main function of an LSCT is to provide services to a hinterland market. The material handling equipment used is limited to reach stacker cranes and front loaders, and ground transportation in and out is performed using trucks exclusively. Moreover, there are many operations related to servicing customers in special service areas within the yard. In this research, we explicitly recognize the limitations and features associated with the operations within LSCT yards, which means that the problems and solutions must be conceived and addressed in a very unique way, considering that services have to be coordinated with the strategies proposed for managing the movement of containers in and out. This paper characterizes the different aspects present in the treatment of an LSCT and proposes an adapted version of three known greedy decision rules for determining the location for an arriving container. We evaluate the performances of these rules using a discrete-event simulator: the min-max rule outperforms the other tested rules. These rules are also extended to consider the cost of moving relocating containers. The results show that using cost-aware rules while increasing the expected number of relocations reduces the total expected cost significantly.
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References
Akyüz MH, Lee C (2014) A mathematical formulation and efficient heuristics for the dynamic container relocation problem. Naval Res Log (NRL) 61(2):101–118. https://doi.org/10.1002/nav.21569
Borgman B, Asperen EV, Dekker R (2010) Online rules for container stacking. OR Spect 32(3):687–716. https://doi.org/10.1007/s00291-010-0205-4
Boysen N, Emde S (2016) The parallel stack loading problem to minimize blockages. Eur J Oper Res 249(2):618–627. https://doi.org/10.1016/j.ejor.2015.09.033
Carlo HJ, Vis IFA, Roodbergen KJ (2014) Storage yard operations in container terminals: literature overview, trends, and research directions. Eur J Oper Res 235(2):412–430. https://doi.org/10.1016/j.ejor.2013.10.054
Caserta M, VoB (2009) A corridor method-based algorithm for the pre-marshalling problem. In: Applications of evolutionary computing. Springer, Berlin, Heidelberg, pp 788–797. https://doi.org/10.1007/978-3-642-01129-0
Caserta M, Schwarze S, VoB (2011) Container rehandling at maritime container terminals. In: Handbook of terminal planning. Springer, New York, pp 247–269
Caserta M, Schwarze S, VoB S (2012) A mathematical formulation and complexity considerations for the blocks relocation problem. Eur J Oper Res 219(1):96–104. https://doi.org/10.1016/j.ejor.2011.12.039
Dekker R, Voogd P, Asperen EV (2007) Advanced methods for container stacking. Contain Termin Cargo Syst Desi Oper Manag Log Control Issues 1999:131–154. https://doi.org/10.1007/978-3-540-49550-5
Expósito-Izquierdo C, Melián-Batista B, Moreno-Vega JM (2014) A domain-specific knowledge-based heuristic for the blocks relocation problem. Adv Eng Inform 28(4):327–343. https://doi.org/10.1016/j.aei.2014.03.003
Expósito-Izquierdo C, Melián-Batista B, Moreno-Vega JM (2015) An exact approach for the blocks relocation problem. Expert Syst Appl 42(17–18):6408–6422. https://doi.org/10.1016/j.eswa.2015.04.021
Forster F, Bortfeldt A (2012) A tree search procedure for the container relocation problem. Comput Oper Res 39(2):299–309. https://doi.org/10.1016/j.cor.2011.04.004
Guven C, Eliiyi DT (2014) Trip allocation and stacking policies at a container terminal. In: Transportation research procedia, vol 3, pp 565–573. https://doi.org/10.1016/j.trpro.2014.10.035
Jang D-W, Kim SW, Kim KH (2013) The optimization of mixed block stacking requiring relocations. Int J Prod Econ 143(2):256–262. https://doi.org/10.1016/j.ijpe.2012.06.033
Kim KH (1997) Evaluation of the number of rehandles in container yards. Comput Ind Eng 32(4):701–711. https://doi.org/10.1016/s0360-8352(97)00024-7
Kim KH, Hong G-P (2006) A heuristic rule for relocating blocks. Comput Oper Res 33(4):940–954. https://doi.org/10.1016/j.cor.2004.08.005
Kim KH, Park KT (2003) A note on a dynamic space-allocation method for outbound containers. Eur J Oper Res 148(1):92–101. https://doi.org/10.1016/s0377-2217(02)00333-8
Kim KH, Park YM, Ryu K-R (2000) Deriving decision rules to locate export containers in container yards. Eur J Oper Res 124(1):89–101. https://doi.org/10.1016/s0377-2217(99)00116-2
Lee BK, Kim KH (2010) Optimizing the block size in container yards. Transp Res Part E Log Transp Rev 46(1):120–135. https://doi.org/10.1016/j.tre.2009.07.001
Lee BK, Kim KH (2013) Optimizing the yard layout in container terminals. OR Spect 35(2):363–398. https://doi.org/10.1007/s00291-012-0298-z
Lehnfeld J, Knust S (2014) Loading, unloading and premarshalling of stacks in storage areas: survey and classification. Eur J Oper Res 239(2):297–312. https://doi.org/10.1016/j.ejor.2014.03.011
Murty KG, Liu J, Wan Y-W, Linn R (2005) A decision support system for operations in a container terminal. Decis Support Syst 39(3):309–332. https://doi.org/10.1016/j.dss.2003.11.002
Petering MEH, Hussein MI (2013) A new mixed integer program and extended look-ahead heuristic algorithm for the block relocation problem. Eur J Oper Res 231(1):120–130. https://doi.org/10.1016/j.ejor.2013.05.037
Rodrigue J-P, Debrie J, Fremont A, Gouvernal E (2010) Functions and actors of inland ports: European and north American dynamics. J Transp Geogr 18(4):519–529. https://doi.org/10.1016/j.jtrangeo.2010.03.008
Scholl J, Boywitz D, Boysen N (2017) On the quality of simple measures predicting block relocations in container yards. Int J Prod Res 56(1–2):1–12. https://doi.org/10.1080/00207543.2017.1394595
Stahlbock R, Voß S (2008) Operations research at container terminals: a literature update. OR Spectr 30(1):1–52. https://doi.org/10.1007/s00291-007-0100-9
Ting C-J, Wu K-C (2017) Optimizing container relocation operations at container yards with beam search. Transp Res Part E Log Transp Rev 103:17–31. https://doi.org/10.1016/j.tre.2017.04.010
Unluyurt T, Aydin C (2012) Improved rehandling strategies for the container retrieval process. J Adv Transp 46(4):378–393
Watanabe I (1991) Characteristics and analysis method of efficiencies of container terminal: an approach to the optimal loading/unloading method. Contain Age 3:36–47
Wu K-C, Ting C-J (2010) A beam search algorithm for minimizing reshuffle operations at container yards. In: Proceedings of the international conference on logistics and maritime systems, pp 15–17
Zehendner E, Feillet D (2014) A branch and price approach for the container relocation problem. Int J Prod Res 52(24):7159–7176. https://doi.org/10.1080/00207543.2014.965358
Zhu W, Qin H, Lim A, Zhang H (2012) Iterative deepening A* algorithms for the container relocation problem. IEEE Trans Autom Sci Eng 9(4):710–722. https://doi.org/10.1109/tase.2012.2198642
Acknowledgements
The authors want to acknowledge the support of project ANID/FONDECYT/REGULAR 1191200 and the Complex Engineering Systems Institute ANID PIA/PUENTE AFB220003. Juan Pablo Cavada is grateful for the support of ANID BECA DOCTORADO NACIONAL 21171505.
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A: Alternative layout analysis
A: Alternative layout analysis
The yard layout used in the presented case study corresponds to the configuration found in our modeled case study for LSCT. However, to make some sensitivity, to check if the results, analysis and conclusions hold true when using certain options of alternative layout, we tested two more configurations of a potential LSCT yard. The variants in configuration are the line abreast layout, were containers blocks are positioned side by side from the widest side, and the column layout where blocks are located one after the other. See Fig. 16
Alternate yard layouts tested
We chose to test the three “staggered” rules: MM-S, RIL-S and RI-S in each configuration. One hundred replications where run for each layout-rule combination, and later a paired t-test was conducted for each combination. The results are summarized in Tables 4 and 5.
In concordance with the results for the base case presented in Sect. 7, the results of these alternative configurations show that the MM-S rule outperforms the other two in all the layouts tested in this work.
Although the average cost for the MM-S is lower than the average cost for the RIL-S in the three layouts tested, the differences are small, and not significant at 95% of significance according to a t-test for the hypothesis that the average costs for the two rules are different.
In all cases using the line abreast layout yields the lowest cost, followed by the square and finally the column layout. This is an expected result, due to the fact that when to blocks are closer on the wide side, the cost of moving blocks between them is lower.
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Cavada, J.P., Cortés, C.E. & Rey, P.A. Comparing allocation and relocation policies at a logistics service container terminal: a discrete-event simulation approach. Cent Eur J Oper Res 31, 1281–1316 (2023). https://doi.org/10.1007/s10100-023-00857-1
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DOI: https://doi.org/10.1007/s10100-023-00857-1