Fleet deployment with time-chartered and voyage-chartered tankers for a refined oil shipping company

Abstract

With ongoing global industrialization, the demand for refined oil products, particularly in developing countries, is increasing significantly. Shipping companies typically transport refined oil from a primary refinery to multiple oil depots, addressing various demand tasks. To manage uncertain refined oil demand, shipping companies use both self-owned tankers and outsourced tankers, including time-chartered and voyage-chartered tankers. A time charter is a contract where the shipping company pays charter money for a specific period, while a voyage charter involves payments based on voyage frequency. This paper develops a nonlinear programming model to optimize fleet deployment, considering transportation costs and penalty costs for capacity loss during a planning period. Additionally, the model is extended to allow flexible charter types, meaning that time-chartered and voyage-chartered tankers are interchangeable based on shipping demands. A heuristic algorithm based on tabu search is designed to solve the proposed models, and four search operators are incorporated to enhance algorithm efficiency. The models and algorithms are validated using a real tanker fleet. Numerical experiments demonstrate the efficiency of the improved tabu search algorithm in obtaining exact solutions for small-scale instances. The case study indicates that the shipping company prefers waiting for tasks to avoid ship delay penalties and provide high-quality services. Moreover, the flexible charter strategy can reduce shipping costs by 16.34%. These findings offer management insights for determining charter contracts for ship fleets.

Graphical abstract

Keywords

fleet deployment / refined oil shipping / time-chartered / voyage-chartered / tabu search

References

[1] Arslan A N, Papageorgiou D J, (2017). Bulk ship fleet renewal and deployment under uncertainty: A multi-stage stochastic programming approach. Transportation Research Part E, Logistics and Transportation Review, 97: 69–96

[2] Bellmore M, Bennington G, Lubore S, (1971). A multivehicle tanker scheduling problem. Transportation Science, 5( 1): 36–47

[3] Brown G G, Graves G W, Ronen D, (1987). Scheduling ocean transportation of crude oil. Management Science, 33( 3): 335–346

[4] Chen X, Lv S, Shang W L, Wu H, Xian J, Song C, (2024). Ship energy consumption analysis and carbon emission exploitation via spatial-temporal maritime data. Applied Energy, 360: 122886

[5] ChiF (2023). A high level of opening up will create good expectations for the all-round revitalization of northeast china. http://www.rmzxb.com.cn/c/2023-09-19/3411301.shtml, 2024-5-18 (in Chinese)

[6] Fagerholt K, Hvattum L M, Johnsen T A, Korsvik J E, (2013). Routing and scheduling in project shipping. Annals of Operations Research, 207( 1): 67–81

[7] He Y, Lin B, (2023). Is market power the cause of asymmetric pricing in china’s refined oil market. Energy Economics, 124: 106778

[8] Huang K, Liao F, (2023). A novel two-stage approach for energy-efficient timetabling for an urban rail transit network. Transportation Research Part E, Logistics and Transportation Review, 176: 103212

[9] Huang K, Wu J, Liao F, Sun H, He F, Gao Z, (2021). Incorporating multimodal coordination into timetabling optimization of the last trains in an urban railway network. Transportation Research Part C, Emerging Technologies, 124: 102889

[10] Kavussanos M G, (1996). Comparisons of volatility in the dry-cargo ship sector: Spot versus time charters, and smaller versus larger vessels. Journal of Transport Economics and Policy, 30: 67–82

[11] Köhn S, Thanopoulou H, (2011). A gam assessment of quality premia in the dry bulk time–charter market. Transportation Research Part E, Logistics and Transportation Review, 47( 5): 709–721

[12] Koza D F, Ropke S, Boleda Molas A, (2017). The liquefied natural gas infrastructure and tanker fleet sizing problem. Transportation Research Part E, Logistics and Transportation Review, 99: 96–114

[13] Li M, Fagerholt K, Schütz P, (2022). Stochastic tramp ship routing with speed optimization: Analyzing the impact of the northern sea route on CO₂ emissions. Annals of Operations Research, 08: 1–25

[14] LiaoningDaily (2023). Dalian has made every effort to promote the construction of northeast asia international shipping center and international logistics center. Avaible at the websife of the People’s Government of Liaoning Province (in Chinese)

[15] Lin D Y, Liu H Y, (2011). Combined ship allocation, routing and freight assignment in tramp shipping. Transportation Research Part E, Logistics and Transportation Review, 47( 4): 414–431

[16] Meng Q, Wang S, Lee C Y, (2015). A tailored branch-and-price approach for a joint tramp ship routing and bunkering problem. Transportation Research Part B: Methodological, 72: 1–19

[17] MordorIntelligence (2024). Market share of ship leasing industry source. Avaible at the websife of mordorintelligence

[18] Norstad I, Fagerholt K, Laporte G, (2011). Tramp ship routing and scheduling with speed optimization. Transportation Research Part C, Emerging Technologies, 19( 5): 853–865

[19] Pérez Lespier L, Long S, Shoberg T, Corns S, (2019). A model for the evaluation of environmental impact indicators for a sustainable maritime transportation systems. Frontiers of Engineering Management, 6( 3): 368–383

[20] Ronen D, (2011). The effect of oil price on containership speed and fleet size. Journal of the Operational Research Society, 62( 1): 211–216

[21] Santos A M P, Guedes Soares C, (2024). Cost optimization of shuttle tanker offloading operations. Ocean Engineering, 301: 117378

[22] Shang W L, Chen Y, Yu Q, Song X, Chen Y, Ma X, Chen X, Tan Z, Huang J, Ochieng W, (2023). Spatio-temporal analysis of carbon footprints for urban public transport systems based on smart card data. Applied Energy, 352: 121859

[23] Shao S, Tan Z, Liu Z, Shang W L, (2022). Balancing the GHG emissions and operational costs for a mixed fleet of electric buses and diesel buses. Applied Energy, 328: 120188

[24] Siddiqui A W, Verma M, (2017). A conditional value-at-risk based methodology to intermediate-term planning of crude oil tanker fleet. Computers & Industrial Engineering, 113: 405–418

[25] Tan Z, Shao S, Zhang X, Shang W L, (2023). Sustainable urban mobility: Flexible bus service network design in the post-pandemic era. Sustainable Cities and Society, 97: 104702

[26] Tsioumas V, Papadimitriou S, (2015). Excess returns in the spot market for bulk carriers. Maritime Economics & Logistics, 17( 4): 399–415

[27] Wan Z, Su Y, Li Z, Zhang X, Zhang Q, Chen J, (2023). Analysis of the impact of suez canal blockage on the global shipping network. Ocean and Coastal Management, 245: 106868

[28] Wang C, Xu C, (2015). Sailing speed optimization in voyage chartering ship considering different carbon emissions taxation. Computers & Industrial Engineering, 89: 108–115

[29] Wang H, Huang S, Liu Z, Zheng L, (2013). Optimal tanker chartering decisions with spot freight rate dynamics considerations. Transportation Research Part E, Logistics and Transportation Review, 51: 109–116

[30] Wang J, Zhou Y, Zhuang L, Shi L, Zhang S, (2022). Study on the critical factors and hot spots of crude oil tanker accidents. Ocean and Coastal Management, 217: 106010

[31] Wang S, Zhen L, Psaraftis H N, (2021a). Three potential benefits of the EU and IMO’s landmark efforts to monitor carbon dioxide emissions from shipping. Frontiers of Engineering Management, 8( 2): 310–311

[32] Wang X, Shao S, Tang J, (2021b). Iterative local-search heuristic for weighted vehicle routing problem. IEEE Transactions on Intelligent Transportation Systems, 22( 6): 3444–3454

[33] Wen M, Ropke S, Petersen H L, Larsen R, Madsen O B G, (2016). Full-shipload tramp ship routing and scheduling with variable speeds. Computers & Operations Research, 70: 1–8

[34] Xin X, Wang X, Tian X, Chen Z, Chen K, (2019). Green scheduling model of shuttle tanker fleet considering carbon tax and variable speed factor. Journal of Cleaner Production, 234: 1134–1143

[35] Yao F, Du Y, Li L, Xing L, Chen Y, (2023). General modeling and optimization technique for real-world earth observation satellite scheduling. Frontiers of Engineering Management, 10( 4): 695–709

[36] Ye Y, Liang S, Zhu Y, (2017). A mixed-integer linear programming-based scheduling model for refined-oil shipping. Computers & Chemical Engineering, 99: 106–116

[37] Zhang H, Zeng Q, (2015). A study of the relationships between the time charter and spot freight rates. Applied Economics, 47( 9): 955–965

[38] Zhao Y, Yang Z, (2018). Ship scheduling in the tramp spot market based on shipper’s choice behavior and the spatial and temporal shipping demand. Transportation Journal, 57( 3): 310–328

[39] Zhen L, Zhuge D, Murong L, Yan R, Wang S, (2019). Operation management of green ports and shipping networks: Overview and research opportunities. Frontiers of Engineering Management, 6( 2): 152–162

[40] Zhu W, Ao Z, Baldacci R, Qin H, Zhang Z, (2023). Enhanced solution representations for vehicle routing problems with split deliveries. Frontiers of Engineering Management, 10( 3): 483–498

RIGHTS & PERMISSIONS

Higher Education Press