An efficient ant colony optimization algorithm for the blocks relocation problem

Raka Jovanovic, Milan Tuba, Stefan Voß

Research output: Contribution to journalArticle

6 Citations (Scopus)

Abstract

In this paper we present an ant colony optimization (ACO) algorithm for the Blocks Relocation Problem (BRP). The method is applied to both versions of the problem most commonly considered in literature, i.e., the restricted (rBRP) and the unrestricted (uBRP) BRP with distinct due dates. In case of the uBRP a new heuristic is proposed and incorporated in a standard greedy algorithm. The performance of the basic greedy approach is enhanced by extending it to the ACO metaheuristic. In it, a novel approach for defining the pheromone matrix is proposed. More precisely, it only stores a small amount of information instead of the complete bay state. Further, we show that the proposed ACO method can easily be adapted for solving the BRP in which the objective function is related to the crane operation time. Our computational results show that the proposed approach manages to outperform existing methods for the BRP.

Original languageEnglish
JournalEuropean Journal of Operational Research
DOIs
Publication statusAccepted/In press - 1 Jan 2018

Fingerprint

Relocation
Ant colony optimization
Optimization Algorithm
Cranes
Due Dates
Pheromone
Greedy Algorithm
Metaheuristics
Optimization Methods
Computational Results
Objective function
Heuristics
Distinct

Keywords

  • Ant colony optimization
  • Blocks relocation problem
  • Heuristics
  • Heuristics
  • Maritime shipping
  • Stowage plan

ASJC Scopus subject areas

  • Computer Science(all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

Cite this

An efficient ant colony optimization algorithm for the blocks relocation problem. / Jovanovic, Raka; Tuba, Milan; Voß, Stefan.

In: European Journal of Operational Research, 01.01.2018.

Research output: Contribution to journalArticle

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