• norsk
    • English
  • English 
    • norsk
    • English
  • Login
View Item 
  •   Home
  • Høgskolen i Molde
  • Øvrige samlinger
  • Publikasjoner fra Cristin
  • View Item
  •   Home
  • Høgskolen i Molde
  • Øvrige samlinger
  • Publikasjoner fra Cristin
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Robust optimization for a maritime inventory routing problem

Agra, Agostinho; Christiansen, Marielle; Hvattum, Lars Magnus; Rodrigues, Filipe
Peer reviewed, Journal article
Accepted version
Thumbnail
View/Open
hvattum_accepted_version (428.5Kb)
URI
https://hdl.handle.net/11250/3026588
Date
2018
Metadata
Show full item record
Collections
  • Artikler [422]
  • Publikasjoner fra Cristin [439]
Original version
Transportation Science. 2018, 52 (3), 509-525.   10.1287/trsc.2017.0814
Abstract
We consider a single product maritime inventory routing problem in which the production and consumption rates are constant over the planning horizon. The problem involves a heterogeneous fleet and multiple production and consumption ports with limited storage capacity. Maritime transportation is characterized by high levels of uncertainty, and sailing times can be severely influenced by varying and unpredictable weather conditions. To deal with the uncertainty, this paper investigates the use of adaptable robust optimization where the sailing times are assumed to belong to the well-known budget polytope uncertainty set. In the recourse model, the routing, the order of port visits, and the quantities to load and unload are fixed before the uncertainty is revealed, while the visit time to ports and the stock levels can be adjusted to the scenario. We propose a decomposition algorithm that iterates between a master problem that considers a subset of scenarios and an adversarial separation problem that searches for scenarios that make the solution from the master problem infeasible. Several improvement strategies are proposed aiming at reducing the running time of the master problem and reducing the number of iterations of the decomposition algorithm. An iterated local search heuristic is also introduced to improve the decomposition algorithm. A computational study is reported based on a set of real instances.
Journal
Transportation Science

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit
 

 

Browse

ArchiveCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsDocument TypesJournalsThis CollectionBy Issue DateAuthorsTitlesSubjectsDocument TypesJournals

My Account

Login

Statistics

View Usage Statistics

Contact Us | Send Feedback

Privacy policy
DSpace software copyright © 2002-2019  DuraSpace

Service from  Unit