Bunker purchasing with contracts

The cost for bunker fuel represents a major part of the daily running costs of liner shipping vessels. The vessels, sailing on a fixed roundtrip of ports, can lift bunker at these ports, having differing and fluctuating prices. The stock of bunker on a vessel is subject to a number of operational constraints such as capacity limits, reserve requirements and sulphur content. Contracts are often used for bunker purchasing, ensuring supply and often giving a discounted price. A contract can supply any vessel in a period and port, and is thus a shared resource between vessels, which must be distributed optimally to reduce overall costs. The Bunker Purchasing with Contracts Problem has been formulated as a mixed integer programme, which has been Dantzig-Wolfe decomposed. To solve it, a novel column generation algorithm has been developed. The algorithm has been run on a series of real-world instances with up to 500+ vessels and 500+ contracts, and provide near optimal solutions. This makes it possible for a major liner shipping company to plan bunker purchasing on a global level, and provides an efficient tool for assessing new contracts.

[1]  Qiang Meng,et al.  Robust schedule design for liner shipping services , 2012 .

[2]  Juan José Salazar González,et al.  Single liner shipping service design , 2014, Comput. Oper. Res..

[3]  T. Notteboom The Time Factor in Liner Shipping Services , 2006 .

[4]  Loo Hay Lee,et al.  A study on bunker fuel management for the shipping liner services , 2012, Comput. Oper. Res..

[5]  T. Notteboom,et al.  The effect of high fuel costs on liner service configuration in container shipping , 2009 .

[6]  Zhiyuan Liu,et al.  Bunker consumption optimization methods in shipping: A critical review and extensions , 2013 .

[7]  David Pisinger,et al.  A service flow model for the liner shipping network design problem , 2014, Eur. J. Oper. Res..

[8]  James J. Corbett,et al.  The effectiveness and costs of speed reductions on emissions from international shipping , 2009 .

[9]  José Fernando Álvarez,et al.  Joint Routing and Deployment of a Fleet of Container Vessels , 2009 .

[10]  Jesper Larsen,et al.  Tramp ship routing and scheduling with integrated bunker optimization , 2014, EURO J. Transp. Logist..

[11]  R. E. Marsten,et al.  The Boxstep Method for Large-Scale Optimization , 2011, Oper. Res..

[12]  Inge Norstad,et al.  Reducing fuel emissions by optimizing speed on shipping routes , 2010, J. Oper. Res. Soc..

[13]  D. Ronen,et al.  The effect of oil price on containership speed and fleet size , 2011, J. Oper. Res. Soc..

[14]  Sergei Savin,et al.  Going Bunkers: The Joint Route Selection and Refueling Problem , 2009, Manuf. Serv. Oper. Manag..

[15]  Manuel Acosta,et al.  Bunkering competition and competitiveness at the ports of the Gibraltar Strait , 2011 .

[16]  Hwa-Joong Kim,et al.  An epsilon-optimal algorithm considering greenhouse gas emissions for the management of a ship’s bunker fuel , 2012 .

[17]  Kjetil Fagerholt,et al.  Ship Routing and Scheduling: Status and Perspectives , 2004, Transp. Sci..

[18]  Loo Hay Lee,et al.  Dynamic determination of vessel speed and selection of bunkering ports for liner shipping under stochastic environment , 2014, OR Spectr..

[19]  David Pisinger,et al.  A Base Integer Programming Model and Benchmark Suite for Liner-Shipping Network Design , 2014, Transp. Sci..

[20]  Angelos Boutsikas The bunkering industry and its effect on shipping tanker operations , 2004 .

[21]  Qiang Meng,et al.  Network Design for Shipping Service of Large-Scale Intermodal Liners , 2012 .