Storage Space Allocation for Containers in a Container Terminal under Uncertainty Condition

Document Type : Original Article


1 Associate professor, Islamic Azad University, Arak Branch.

2 Master Student, Islamic Azad University, Arak Branch.


By globalization of commerce and extension of communications, container terminals play important roles in countries’ economies. Most of   influential and industrial countries owe their prosperity and welfare on transit industry. The time duration of clearance containers is the global criterion of measuring terminal efficiency. In addition, this criterion is considered to assess organizational structures and managerial performances so, managers have been constantly searching for remedies to decline clearance time. In this paper, a multi-objective fuzzy non-linear mathematical model under uncertainty condition of input parameters is presented, which attempts to reduce clearance time through proper allocation space to containers. After solving the model, based on the proposed three-step procedure, a numerical example is examined using exact and Lagrange relaxation methods. Our finding demonstrates that if Decision-makers want to meet uncertainty with lowered risk, they have to choose a high minimum constraint feasibility degree even though the objective function will be worse.


1. رسول سرابی، ا؛ و بهرامی‌نیا، غ (1388). بررسی علل رسوب کانتینر در اسکله شهید‌ رجایی و ارائه راهکارها جهت حل مشکلات. یازدهمین همایش صنایع دریایی، جزیره کیش، 5-1.
2. کاظمی آسیابر، علیرضا؛ سعیدی، سید ناصر؛ و نورامین،امیر سعید (1390). بررسی آماری عوامل مؤثر بر ترخیص کانتینر در بنادر ایران. نشریه علمی و پژوهشی اقیانوس شناسی، سال دوم، شماره8.
3. (1389).کتابچه هزینه های مترتب با کشتی ها و کالاها در بنادر جمهوری اسلامی ایرانن. اداره کل ترانزیت و تعرفه های بندری سازمان بنادر و دریانوردی ایران.
4. کیانی مقدم، منصور؛ تهمک، حمیدرضا؛ مشایخی، افشین؛ و ایرانشاهی، سبحان (1391). مدلسازی عناصر اثرگذار بر زمان انتظار کشتی های تجاری با استفاده از تئوری تصمیم گیری MADM و روش سلسه مراتبی AHP. اولین همایش ملی توسعه مکران و اقتدار دریایی جمهوری اسلامی ایران.
5. Bazzazi, M., Safaei, N., & Javadian, N. (2009). A genetic algorithm to solve the storage space allocation problem. Computer & Industrial Engineering56, 44-52.
6. Chen, L., & Lu, ZH. (2012). the storage location assignment problem for outbound containers in a marine terminals. Int.J.Production Economics, 135, 73-80.
7. Deb, K. (2001). Multi-objective optimization using evolutionary algorithms. United States, John Wiley & Sons.
8. Fisher, M. L. (2004). The lagrangian relaxation method for solving integer programming problems. Manage. Sci50( 12), 1861–1871.
9. Ha, M.S. (2003). A comparison ports: Implications for Korean ports. J.Transport.Georger, 11(2), 131-137.
10. Hirashima,Y. (2008).A Q-Learning system for container transfer scheduling based on shipping order at container terminals. International Journal of Innovative Computing, 14.
11. Ing Hsu, C., Hung, H., & Wang, W. (2009). Applying RFID to reduce delay in import cargo customs clearance process. Journal of Computer and Industrial Engineering, 57, 506-519.
12.  Jiménez, M., Arenas, M., & Bilbao, A. (2007). Linear programming with fuzzy parameters: an interactive method resolution. European Journal of Operational Research, 177(3), 1599-1609.
13. Kent, P.E. December (2003). A Tale of two ports.
14. Kim, K.H., Park, Y.M., & Ryu, K-R. (2000). Deriving decision rules to locate export containers in container yard. European Journal of Operation Research, 124(1), 89-101.
15. Llala-Ruiz, E.,Gonzalez-velarde, J., & Milian-Batista, B. (2014). Biased random key genetic algorithm for the Tactical Berth Allocation Problem. Applied soft computing22, 60-76.
16. Lee, D-H., Jin, J., & Chen, J. (2012). Terminal and yard allocation problem for a container transshipment hub with multiple terminals.Transportation Research, 48, part E, 516-528.
17. Le,Y., & Ieda, H. (2010). Evaluation dynamics of container ports system with a geo-Economic consentration. Asian Transport Studies, 11.
18. Lee,Y., & Lee, Y-J. (2009).A heuristic for retrieving containers from a yard.Computer & Operation Research, 37, 1139-1147.
19. Pishvaee, M. S., & Torabi, S. A. (2010). A possibilistic programming approach for closed-loop supply chain network design under uncertainty. Fuzzy sets and systems, 161(20), 2668-2683.
20. Romero, C., Tamiz, M., & Jones, D. F. (1998). Goal programming, compromise programming and reference point method formulations: linkages and utility interpretations. Journal of the Operational Research Society, 986-991.
21. Rudriguez-Molins, M., Salido, M.A., & Barber, F. (2012). Intelligent
planning for allocating containers in maritime terminals.Expert System with Applications, 39, 978-989.
22. Sharif, O., & Huyne, N. (2013). Storage space allocation at marine container terminals using ant-based control. Expert Systems with Applications, 40, 2323-2330.
23. Vacca, I., Bierlaire, M., & salani, M. (2007). Optimization at container terminals: status,trends and perspectives,swiss. Transport Research Conference.
24. Yu, M., & Qi, X. (2013). Storage space allocation models for inbound containers in an automatic container terminals. European Journal of Operation Research, 226, 32-45.
25. Zhang, C., Liu, J., Wan, Y-W., G.Murty, k., & J.Linn, R. (2003).Storage space allocation in container terminals. Transportation Research, 37, part B, 883-903.
26. Zhang, C., Chen, W., & Shi, l. (2010). A note on deriving decision rules to locate export containers on container yard.European Journal of Operation Research, 205(2), 483-485.
27. Zhang, C.,Wu,T., Kim, K.H., & Miao, L. (2014). Conservative allocation models for outbound containers in container terminals. European Journal of Operation Research, 238, 155-165.