Mathematical models for the berth allocation problem in dry bulk terminals

Andreas T. Ernst (), Ceyda Oğuz (), Gaurav Singh () and Gita Taherkhani ()
Additional contact information
Andreas T. Ernst: Monash University
Ceyda Oğuz: Koç University
Gaurav Singh: BHP Billiton
Gita Taherkhani: Koç University

Journal of Scheduling, 2017, vol. 20, issue 5, No 3, 459-473

Abstract: Abstract Port terminals processing large cargo vessels play an important role in bulk material supply chains. This paper addresses the question of how to allocate vessels to a location on a berth and the sequence in which the vessels should be processed in order to minimize delays. An important consideration in the berth allocation is the presence of tidal constraints that limit the departure of fully loaded vessels from the terminal. We show how the berth allocation problem can be modeled as an integer program and discuss a number of ways to tighten the formulation in order to make it computationally tractable. In addition, a two-phase method is developed for solving these problems. Empirical computational results demonstrate an order of magnitude improvement in performance. The two new approaches can solve significantly larger instances, producing faster solutions for small instances and much tighter bounds for large instances.

Keywords: Berth allocation; Bulk material; Mixed integer linear programming; Valid inequalities (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

Downloads: (external link)
http://link.springer.com/10.1007/s10951-017-0510-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:jsched:v:20:y:2017:i:5:d:10.1007_s10951-017-0510-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10951

DOI: 10.1007/s10951-017-0510-8

Access Statistics for this article

Journal of Scheduling is currently edited by Edmund Burke and Michael Pinedo

More articles in Journal of Scheduling from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().