## Abstract / Description of output

This paper describes a stochastic ship routing problem with inventory

management. The problem involves finding a set of least cost routes for

a fleet of ships transporting a single commodity when the demand for the

commodity is uncertain. Storage at supply and consumption ports is limited

and inventory levels are monitored in the model. Consumer demands are at

a constant rate within each time period in the deterministic problem, and in

the stochastic problem, the demand rate for a period is not known until the

beginning of that period. The demand situation in each time period can be

described by a scenario tree with corresponding probabilities.

A decomposition formulation is given and it is solved using a Branch and

Price framework. A master problem (set partitioning with extra inventory

constraints) is built, and the subproblems, one for each ship, are solved

by stochastic dynamic programming and yeild the columns for the master

problem. Each column corresponds to one possible tree of actions for

one ship giving its schedule loading/unloading quantities for all demand

scenarios. Computational results are given showing that medium sized

problems can be solved successfully.

management. The problem involves finding a set of least cost routes for

a fleet of ships transporting a single commodity when the demand for the

commodity is uncertain. Storage at supply and consumption ports is limited

and inventory levels are monitored in the model. Consumer demands are at

a constant rate within each time period in the deterministic problem, and in

the stochastic problem, the demand rate for a period is not known until the

beginning of that period. The demand situation in each time period can be

described by a scenario tree with corresponding probabilities.

A decomposition formulation is given and it is solved using a Branch and

Price framework. A master problem (set partitioning with extra inventory

constraints) is built, and the subproblems, one for each ship, are solved

by stochastic dynamic programming and yeild the columns for the master

problem. Each column corresponds to one possible tree of actions for

one ship giving its schedule loading/unloading quantities for all demand

scenarios. Computational results are given showing that medium sized

problems can be solved successfully.

Original language | English |
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Title of host publication | Springer Optimization and Its Applications |

Subtitle of host publication | 2016 Advances in Stochastic and Deterministic Global Optimization |

Editors | Panos M. Pardalos, Anatoly Zhigljavsky, Julius Žilinskas |

Publisher | Springer |

Volume | 107 |

ISBN (Electronic) | 978-3-319-29975-4 |

ISBN (Print) | 978-3-319-29973-0 |

DOIs | |

Publication status | Published - 8 Apr 2011 |