×

zbMATH — the first resource for mathematics

Dynamic determination of vessel speed and selection of bunkering ports for liner shipping under stochastic environment. (English) Zbl 1290.90016
Summary: We study a liner shipping operational problem which considers how to dynamically determine the vessel speed and refueling decisions, for a single vessel in one service route. Our model is a multi-stage dynamic model, where the stochastic nature of the bunker prices is represented by a scenario tree structure. Also, we explicitly incorporate the uncertainty of bunker consumption rates into our model. As the model is a large-scale mixed integer programming model, we adopt a modified rolling horizon method to tackle the problem. Numerical results show that our framework provides a lower overall cost and more reliable schedule compared with the stationary model of a related work.

MSC:
90B06 Transportation, logistics and supply chain management
PDF BibTeX XML Cite
Full Text: DOI
References:
[1] Baker, KR, An experimental study of the effectiveness of rolling schedules in production planning, Decis Sci, 8, 19-27, (1977)
[2] Besbes, O; Savin, S, Going bunkers: the joint route selection and refueling problem, Manuf Serv Oper Manag, 11, 694-711, (2009)
[3] Dougherty J, Kohavi R, Sahami M (1995) Supervised and unsupervised discretization of continuous features. In: Machine learning-international workshop then conference. Morgan Kaufmann Publishers, Inc, pp 194-202
[4] Dupačová, J; Gröwe-Kuska, N; Römisch, W, Scenario reduction in stochastic programming, Math Program, 95, 493-511, (2003) · Zbl 1023.90043
[5] Growe-Kuska N, Heitsch H, Romisch W (2003) Scenario reduction and scenario tree construction for power management problems. In : Power tech conference proceedings, vol 3. 2003 IEEE Bologna, IEEE · Zbl 1023.90043
[6] Heitsch, H; Römisch, W, Scenario reduction algorithms in stochastic programming, Comput Optim Appl, 24, 187-206, (2003) · Zbl 1094.90024
[7] Heitsch, H; Römisch, W, Scenario tree reduction for multistage stochastic programs, Comput Manag Sci, 6, 117-133, (2009) · Zbl 1171.90485
[8] Kotsiantis, S; Kanellopoulos, D, Discretization techniques: a recent survey, GESTS Int Trans Comput Sci Eng, 32, 47-58, (2006)
[9] Maersk (2010) Super slow steaming customer presentation. http://shippersassociation.org/ihsa/NewsLetterItems/MaerskSteaming.pdf. Accessed 15 Feb 2011 · Zbl 0920.90008
[10] Mulvey, JM; Rosenbaum, DP; Shetty, B, Strategic financial risk management and operations research, Eur J Oper Res, 97, 1-16, (1997) · Zbl 0920.90008
[11] Notteboom, TE; Vernimmen, B, The effect of high fuel costs on liner service configuration in container shipping, J Transp Geogr, 17, 325-337, (2009)
[12] Oh, HC; Karimi, IA, Operation planning of multiparcel tankers under fuel price uncertainty, Ind Eng Chem Res, 49, 6104-6114, (2010)
[13] Perakis, AN; Papadakis, N, Fleet deployment optimization models. part 1, Marit Policy Manag, 14, 127-144, (1987)
[14] Perakis, AN; Papadakis, N, Fleet deployment optimization models. part 2, Marit Policy Manag, 14, 145-155, (1987)
[15] Ronen, D, The effect of oil price on the optimal speed of ships, J Oper Res Soc, 33, 1035-1040, (1982)
[16] Ronen, D, The effect of oil price on containership speed and fleet size, J Oper Res Soc, 62, 211-216, (2011)
[17] Yao Z, Ng SH, Lee LH (2012) A study on bunker fuel management for the shipping liner services. Comput Oper Res 39(5):1160-1172
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.