A multi-product, multi-period and multi-hub routing and scheduling model for offshore logistics

Subject Areas : Mathematical Optimization

Alireza Rashidi Komijan ¹ (Department of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran)
Mehdi Razi ² (School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Peyman Afzal ³ (School of Mine Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Vahidreza Ghezavati ⁴ (School of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)
Kaveh Khalili Damghani ⁵ (Department of Industrial Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran)

Received: 2023-10-25 Accepted : 2024-05-04 Published : 2024-04-01

Keywords: Routing, Scheduling, Mathematical model, Offshore logistics, Genetic Algorithm,

Abstract :

Logistics in upstream oil industry is a critical task as rigs need consistent support for ongoing production. In this paper, a multi-period, multi-product and multi-hub routing and scheduling model is presented for offshore logistics problem. As rigs can be served in specific time intervals, time windows constraints are considered in the proposed model. Despite classic VRP models, vessels are not forced to return hubs at the end of duty days. Also, a vessel may leave and return back to hubs several times during the planning horizon. Moreover, the model determines which vessels are applied in each day. In other words, a vessel may be applied in some days and be inactive in other days of planning horizon. To develop a compromise model, fueling issue is considered in the model. As a rig can be supplied by different vessels in real world cases, the proposed model is split delivery. Based on these challenges and contributions, this research deploys an integrated optimization of routing and scheduling of vessels for offshore logistics. This paper deals with a combinatorial optimization model which is NP-hard. Hence, Genetic Algorithm is applied as the solution approach. The average gap between objective functions of GAMS and GA is only 1.18 percent while saving CPU time in GA is much more than GAMS (about 78.16 percent on average). The results confirm the applicability and efficiency of the GA.

References:

[1] Prokopowicz, A. and Berg-Andreassen, J., (2016). An evaluation of current trends in container shipping industry, very large container ships (VLCSs), and port capacities to accommodate TTIP increased trade. Transportation Research Procedia,14, 2910-2919, https://doi.org/10.1016/j.trpro.2016.05.409.
[2] Dulebenets, M.A., (2018). The vessel scheduling problem in a liner shipping route with heterogeneous fleet. International Journal of Civil Engineering, 16(1), 19-32, https://doi.org/10.1007/s40999-016-0060-z.
[3] Meng, Q., Wang, S., Andersson, H., and Thun, K., (2014). Containership routing and scheduling in liner shipping: overview and future research directions. Transportation Science, 48(2), 265-280, https://doi.org/10.1287/trsc.2013.0461.
[4] Giovannini, M. and Psaraftis, H., (2019). The profit maximizing liner shipping problem with flexible frequencies: logistical and environmental considerations. Flexible Services and Manufacturing Journal, 31, 567-597,https://doi.org/10.1007/s10696-018-9308-z.
[5] Huang, Y., Hu, J., and Yang, B., (2015). Liner services network design and fleet deployment with empty container repositioning. Computers & Industrial Engineering, 89, 116-124, https://doi.org/10.1016/j.cie.2015.01.021.
[6] Ozcan, S., Eliiyi, D., and Reinhardt, L., (2020). Cargo allocation and vessel scheduling on liner shipping with synchronization of transshipments. Applied Mathematical Modelling, 77(1), 235-252, https://doi.org/10.1016/j.apm.2019.06.033.
[7] Zhang, B., Zheng, Z., and Wang, D., (2020). A model and algorithm for vessel scheduling through a two-way tidal channel. Maritime Policy & Management, 47(2), 188-202, https://doi.org/10.1080/03088839.2019.1676477
[8] Alehashemi, A., & Hajiyakhchali, S. (2018). Optimizing Fleet Composition and Routing Plan Scheduling for Offshore Supply Operations: Case Study of an Offshore Drilling Firm. The Asian Journal of Shipping and Logistics, 34(3), 218-226, https://doi.org/10.1016/j.ajsl.2018.09.005.
[9] Aimri, M., Sagjadi, S.J., Tavakkoli-Moghaddam, R. & Jabbarzadeh, A. (2019-a). Optimal eet composition and mix periodic location-routing problem with time windows in an offshore oil and gas industry: A case study of National Iranian Oil Company. Scientia Iranica, 26(1), 522-537, https://doi.org/10.24200/SCI.2018.20412.
[10] Amiri, M., Amin, S. H., & Tavakkoli-Moghaddam, R. (2019-b). A Lagrangean decomposition approach for a novel two-echelon node-based location-routing problem in an offshore oil and gas supply chain. Transportation Research Part E: Logistics and Transportation Review, 128, 96-114, https://doi.org/10.1016/j.tre.2019.05.014.
[11] Borthen, T., Loennechen, H., Fagerholt, K., Wang, X., & Vidal, T. (2019). Bi-objective offshore supply vessel planning with costs and persistence objectives. Computers & Operations Research, 111, 285-296, https://doi.org/10.1016/j.cor.2019.06.014.
[12] Abbasi Pooya, A. & Husseinzadeh Kashan, A. (2017). New mathematical models and a hybrid grouping evolution strategy algorithm for optimal helicopter routing and crew pickup and delivery. Computers and Industrial Engineering, 112, 35-56, https://doi.org/10.1016/j.cie.2017.08.007.
[13] Leggate, A., Sucu, S., Akartunal, K. & Meer, R. (2018). Modelling crew scheduling in offshore supply Vessels. Journal of the Operational Research Society, 69(6), 959-970, https://doi.org/10.1080/01605682.2017.1390531.
[14] Astoures, H. F., Rosa, R. A. & Silva Rosa, A. L. (2016). Planning the diesel supply for offshore platforms by a mathematical model based on the vehicle routing problem with replenishment. Transportation Research Procedia, 18, 11-18, https://doi.org/10.1016/j.trpro.2016.12.002.
[15] Cuesta, E. F., Andersson, H., Fagerholt, K. & Laporte, G. (2017).Vessel routing with pickups and deliveries: an application to the supply of offshore oil platforms. Computers and Operation Research, 79, 140-147, https://doi.org/10.1016/j.cor.2016.10.014.
[16] Silva, V. A., Wang, A., Ferreria Filho, V. J. & Gounaris, C. E.(2024). Routing and scheduling of platform supply vessels in offshore oil and gas logistics, Computers and Operation Research, 164, 106556, https://doi.org/10.1016/j.cor.2024.106556.
[17] Bittencourt, G. C., Chagas, R. D. S., Silva, V. A., Vianna, I. G. P., Longhi, R. P., Ribas, P. C. & Ferreria Filho, V. J. (2021). A solution framework for the integrated problem of cargo assignment, fleet sizing, and delivery planning in offshore logistics, Computers and Industrial Engineering, 161, 107653, https://doi.org/10.1016/j.cie.2021.107653.
[18] Kondratenko, A. A., Bergstrom, M., Suominen, M. & Kujala, P. (2022). An Artificial Bee Colony optimization-based approach for sizing and composition of Arctic offshore drilling support fleets considering cost-efficiency, Ship Technology Research, 69(2), 65-88, https://doi.org/10.1080/09377255.2021.2022906.
[19] Kisialiou, Y., Gribkovskaia, I., & Laporte, G. (2021). Periodic supply vessel planning under demand and weather uncertainty. INFOR: Information Systems and Operational Research, 59(3), 411–438, https://doi.org/10.1080/03155986.2021.1907872.
[20] Nafstad, G. M., Haugseth, A., Hoyland, V. & Stalhane, M. (2021). An exact solution method for a rich helicopter flight scheduling problem arising in offshore oil and gas logistics, Computers and Operation Research, 128, 105158, https://doi.org/10.1016/j.cor.2020.105158.