Optimization of ship’s crew change schedule

• Autorzy: Bošnjak Rino , Bukljaš Mihaela , Medić Dario , Vukša Srđan

Identyfikatory

DOI

10.17402/349

• Języki publikacji: EN

Abstrakty

EN

Seaborne shipping must often cope with issues related to planning, ship scheduling, and arranging crews and optimal shipping routes between ports. Human resources departments typically plan ship crew shifts with regards to the seafarers’ right to vacation days. It is difficult to harmonize all the requirements and to satisfy both the seafarers and the company. Ideally, arrangements are made for the crewmember to sign off upon completion of a contract, with the vessel being at a port convenient to change the crew at a minimum cost. The latter may vary greatly, depending on the size of the crew to be replaced, the distance of the port from the crew destination, and the available taking-over crew at a specific place and time, etc. In these situations, linear programming (LP) is frequently used as a mathematical method to determine the optimal results. This study suggests the use of a linear-binary programming model in LINGO software to arrange the ship’s crew change schedule.

Słowa kluczowe

EN

ship’s crew; crew change schedule; linear programming; Lingo software; optimization; shipping lines

• Wydawca: Wydawnictwo Naukowe Akademii Morskiej w Szczecinie
• Czasopismo: Zeszyty Naukowe Akademii Morskiej w Szczecinie
• Rocznik: 2019
• Tom: nr 59 (131)
• Strony: 29--33
• Opis fizyczny: Bibliogr. 12 poz., tab.

Twórcy

autor

Bošnjak Rino

• University of Split, Faculty of Maritime Studies

autor

Bukljaš Mihaela

• University of Zagreb; Faculty of Transport and Traffic Science

autor

Medić Dario

• University of Split, Faculty of Maritime Studies

autor

Vukša Srđan

• University of Split, Faculty of Maritime Studies

Bibliografia

• 1. Cao, G., Li, C., Yang, S. & Han, H. (2018) Location Optimization Modelling of Cascade Dam System Based on LINGO. DOI: 10.12677/MOS.2018.71003
• 2. Guo, H., Wang, X. & Zhou, S. (2015) A Transportation Problem with Uncertain Costs and Random Supplies. International Journal of e-Navigation and Maritime Economy 2, pp. 1–11.
• 3. Gupta, N. & Bari, A. (2017) Fuzzy multi-objective optimization for optimum allocation in multivariate stratified sampling with quadratic cost and parabolic fuzzy numbers. Journal of Statistical Computation and Simulation 87, 12, pp. 2372–2383.
• 4. Hillier, F.S. & Lieberman, G.J. (2001) Introduction to Operations Research. Seventh Edition. The McGraw-Hill Companies.
• 5. Maras, V. (2008) Determining Optimal Transport Routes of Inland Waterway Container Ships. Transportation Research Record: Journal of the Transportation Research Board 2062, pp. 50–58.
• 6. Reeb, J. & Leavengood, S. (2002) Transportation Problem: A Special Case for Linear Programming Problems. EM 8779. Corvallis: Oregon State University Extension Service, Performance Excellence in the Wood Products Industry, Operations Research.
• 7. Schrage, L. (2002) Optimization modelling with LINGO. Fifth edition. LINDO systems Inc., Chicago, USA.
• 8. Tan, R.R., Aviso, K.B., Promentilla, M.A.B., Yu, K.D.S. & Santos, J.R. (2019) Input-Output Models for Sustainable Industrial Systems. Implementation Using LINGO. Springer.
• 9. Wang, Y., Han, B.-M. & Wang, J.-K. (2018) A Passenger Flow Routing Model for High-speed Railway Network in Different Transportation Organization Modes. Promet – Traffic & Transportation 30(6), pp. 671–682.
• 10. Winston, W.L. (2004) Operations research – Applications and Algorithms. Third edition. International Thomson Publishing.
• 11. Zelenika, R., Vukmirovic, S. & Mujic, H. (2007) Computer-supported modelling of multimodal transportation networks rationalization. Promet – Traffic & Transportation 19(5), pp. 277–288.
• 12. Zenzerović, Z. & Bešlić, S. (2003) Optimization of cargo transport with a view to cost efficient operation of container ship. Proceedings of the 25th International Conference on Information Technology Interfaces, 19 June 2003, Cavtat, Croatia. DOI: 10.1109/ITI.2003.1225398.

Uwagi

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).