Supply vessel routing and scheduling under uncertain demand

Yauheni Kisialiou, Irina Gribkovskaia, Gilbert Laporte

Research output: Contribution to journalArticlepeer-review

23 Citations (SciVal)


We solve a supply vessel planning problem arising in upstream offshore petroleum logistics. A fleet of supply vessels delivers all the necessary equipment and materials to a set of offshore installations from an onshore supply base, according to a delivery schedule or sailing plan. Supply vessels, being the major cost contributor, are chartered on a long-term basis. The planning of supply vessels implies resolving the trade-off between the cost of the delivery schedule and the reliability of deliveries on the scheduled voyages, i.e. the service level. The execution of a sailing plan is affected by stochastic demands at the installations since a high demand fluctuation quite often leads to insufficient vessel capacity to perform a voyage according to the sailing plan. In addition, the average demand level at the installations may change over time, while the number of vessels in the sailing plan remains the same. Maintaining a reliable flow of supplies under stochastic demand therefore leads to additional costs and reduced service level. We present a novel methodology for reliable supply vessel planning and scheduling, enabling planners to construct delivery schedules having a low expected total cost. The methodology involves the construction of delivery schedules with different reliability levels using an adaptive large neighborhood search metaheuristic algorithm combined with a discrete event simulation procedure for the computation of the expected solution cost.

Original languageEnglish
Pages (from-to)305-316
Number of pages12
JournalTransportation Research Part C: Emerging Technologies
Publication statusPublished - Jul 2019


  • Maritime logistics
  • Metaheuristic
  • Recourse
  • Reliable vessel schedules
  • Simulation
  • Supply vessel planning

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Automotive Engineering
  • Transportation
  • Computer Science Applications


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