Modified integer model for solving the master bay problem

• Autorzy: Tsymbal M. , Kamieniev K.

Identyfikatory

DOI

10.12716/1001.15.04.05

• Języki publikacji: EN

Abstrakty

EN

One of key components in keeping a ship seaworthy is a correctly prepared cargo plan. Considering the recent cost cutting measures and reduced transportation volumes relevance of optimizing such a plan cannot be understated. Though there’s a number of studies addressing the issue none of them covers all the operational and constructional constraints necessary to factor for. This article presents an integer model that tries to address some of the constraints missed by other researches. A method for solving the model is designed and developed based on a steady-state genetic algorithm. A numerical experiment is conducted showing the method’s feasibility.

Słowa kluczowe

EN

ship operation problem; master bay problem; integer model; modified integer model; genetic algorithm; steady-state genetic algorithm; numerical experiment; feasibility study

• Wydawca: Faculty of Navigation, Gdynia Maritime University
• Czasopismo: TransNav : International Journal on Marine Navigation and Safety of Sea Transportation
• Rocznik: 2021
• Tom: Vol. 15 No. 4
• Strony: 749--753
• Opis fizyczny: Bibliogr. 24 poz., rys.

Twórcy

autor

Tsymbal M.

• National University "Odesa Maritime Academy", Odessa, Ukraine

autor

Kamieniev K.

• National University "Odesa Maritime Academy", Odessa, Ukraine

Bibliografia

• 1. Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: A new three-step heuristic for the Master Bay Plan Problem. Maritime Economics & Logistics. 11, 1, 98–120 (2009). https://doi.org/10.1057/mel.2008.19.
• 2. Ambrosino, D., Anghinolfi, D., Paolucci, M., Sciomachen, A.: An Experimental Comparison of Different Heuristics for the Master Bay Plan Problem. In: Festa, P. (ed.) Experimental Algorithms. pp. 314–325 Springer Berlin Heidelberg, Berlin, Heidelberg (2010).
• 3. Ambrosino, D., Paolucci, M., Sciomachen, A.: Experimental evaluation of mixed integer programming models for the multi-port master bay plan problem. Flexible Services and Manufacturing Journal. 27, 2, 263–284 (2015). https://doi.org/10.1007/s10696-013-9185-4.
• 4. Ambrosino, D., Sciomachen, A.: Using a Bin Packing Approach for Stowing Hazardous Containers into Containerships. In: Fasano, G. and Pintér, J.D. (eds.) Optimized Packings with Applications. pp. 1–18 Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-18899-7_1.
• 5. Ambrosino, D., Sciomachen, A., Tanfani, E.: Stowing a containership: the master bay plan problem. Transportation Research Part A: Policy and Practice. 38, 2, 81–99 (2004). https://doi.org/10.1016/j.tra.2003.09.002.
• 6. D. M. Rahsed, M. S. Gheith, A. B. Eltawil: A Rule-based Greedy Algorithm to Solve Stowage Planning Problem. In: 2018 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). pp. 437–441 (2018). https://doi.org/10.1109/IEEM.2018.8607517.
• 7. Delgado, A., Jensen, R.M., Janstrup, K., Rose, T.H., Andersen, K.H.: A Constraint Programming model for fast optimal stowage of container vessel bays. European Journal of Operational Research. 220, 1, 251–261 (2012). https://doi.org/10.1016/j.ejor.2012.01.028.
• 8. Dubrovsky, O., Levitin, G., Penn, M.: A Genetic Algorithm with a Compact Solution Encoding for the Container Ship Stowage Problem. Journal of Heuristics. 8, 6, 585–599 (2002). https://doi.org/10.1023/A:1020373709350.
• 9. Hu, M., Cai, W.: Multi-objective optimization based on improved genetic algorithm for containership stowage on full route. In: 2017 4th International Conference on Industrial Engineering and Applications (ICIEA). pp. 224–228 (2017). https://doi.org/10.1109/IEA.2017.7939211.
• 10. Kamieniev, K., Kamienieva, A., Tsymbal, M.: Construction of a mathematical model and a method for arranging hazardous cargoes on a containership. EEJET. 6, 3 (102), 20–27 (2019). https://doi.org/10.15587/1729-4061.2019.183385.
• 11. Lee, Z.Q., Fan, R., Hsu, W.-J.: Optimizing Constraint Test Ordering for Efficient Automated Stowage Planning. In: Corman, F., Voß, S., and Negenborn, R.R. (eds.) Computational Logistics. pp. 343–357 Springer International Publishing, Cham (2015).
• 12. Lei, H., Ok, M.: Dangerous Goods Container Allocation in Ship Stowage Planning. In: Proceedings of the 9th International Conference on Operations Research and Enterprise Systems - ICORES,. pp. 241–246 SciTePress (2020). https://doi.org/10.5220/0009160602410246.
• 13. Liu, F., Low, M.Y.H., Hsu, W.J., Huang, S.Y., Zeng, M., Win, C.A.: Randomized Algorithm with Tabu Search for Multi-Objective Optimization of Large Containership Stowage Plans. In: Böse, J.W., Hu, H., Jahn, C., Shi, X., Stahlbock, R., and Voß, S. (eds.) Computational Logistics. pp. 256–272 Springer Berlin Heidelberg, Berlin, Heidelberg (2011).
• 14. Luke, S.: Essentials of Metaheuristics. lulu.com; Second Edition (2013).
• 15. Matsaini, Santosa, B.: Solving the Container Stowage Problem (CSP) using Particle Swarm Optimization (PSO). IOP Conf. Ser.: Mater. Sci. Eng. 337, 012002 (2018). https://doi.org/10.1088/1757-899X/337/1/012002.
• 16. Parreño, F., Pacino, D., Alvarez-Valdes, R.: A GRASP algorithm for the container stowage slot planning problem. Transportation Research Part E: Logistics and Transportation Review. 94, 141–157 (2016). https://doi.org/10.1016/j.tre.2016.07.011.
• 17. Parreño-Torres, C., Alvarez-Valdes, R., Parreño, F.: Solution Strategies for a Multiport Container Ship Stowage Problem. Mathematical Problems in Engineering. 2019, 9029267 (2019). https://doi.org/10.1155/2019/9029267.
• 18. Sciomachen, A., Tanfani, E.: A 3D-BPP approach for optimising stowage plans and terminal productivity. European Journal of Operational Research. 183, 3, 1433–1446 (2007). https://doi.org/10.1016/j.ejor.2005.11.067.
• 19. Shen, Y., Zhao, N., Xia, M., Du, X.: A Deep Q-Learning Network for Ship Stowage Planning Problem. Polish Maritime Research. 24, s3, 102–109 (2017). https://doi.org/10.1515/pomr-2017-0111.
• 20. Wilson, I.D., Roach, P.A., Ware, J.A.: Container Stowage Pre-Planning: Using Search to Generate Solutions, a Case Study. Know.-Based Syst. 14, 3–4, 137–145 (2001). https://doi.org/10.1016/S0950-7051(01)00090-9.
• 21. Yurtseven, M.A., Boulougouris, E., Turan, O.: Container ship stowage plan using steepest ascent hill climbing, genetic, and simulated annealing algorithms. In: Kujala, P. and Lu, L. (eds.) Marine Design XIII. pp. 617–623 CRC Press (2018).
• 22. Zeng, M., Low, M.Y.H., Hsu, W.J., Huang, S.Y., Liu, F., Win, C.A.: Automated stowage planning for large containerships with improved safety and stability. In: Proceedings of the 2010 Winter Simulation Conference. pp. 1976–1989 (2010). https://doi.org/10.1109/WSC.2010.5678873.
• 23. Zhu, H., Ji, M., Guo, W.: Integer Linear Programming Models for the Containership Stowage Problem. Mathematical Problems in Engineering. 2020, 4382745 (2020). https://doi.org/10.1155/2020/4382745.
• 24. Review of Maritime Transport 2020, https://unctad.org/system/files/official-document/rmt2020_en.pdf.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).