Scheduling wagons to unload in bulk cargo ports with uncertain processing times
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{FERREIRA:2023:cor,
-
author = "Cristiane Ferreira and Goncalo Figueira and
Pedro Amorim and Alexandre Pigatti",
-
title = "Scheduling wagons to unload in bulk cargo ports with
uncertain processing times",
-
journal = "Computer \& Operations Research",
-
volume = "160",
-
pages = "106364",
-
year = "2023",
-
ISSN = "0305-0548",
-
DOI = "doi:10.1016/j.cor.2023.106364",
-
URL = "https://www.sciencedirect.com/science/article/pii/S0305054823002289",
-
keywords = "genetic algorithms, genetic programming, Dynamic
scheduling, Bulk cargo ports, Dispatching rules",
-
abstract = "Optimising operations in bulk cargo ports is of great
relevance due to their major participation in
international trade. In inbound operations, which are
critical to meet due dates, the product typically
arrives by train and must be transferred to the
stockyard. This process requires several machines and
is subject to frequent disruptions leading to uncertain
processing times. This work focuses on the scheduling
problem of unloading the wagons to the stockyard,
approaching both the deterministic and the stochastic
versions. For the deterministic problem, we compare
three solution approaches: a Mixed Integer Programming
model, a Constraint Programming model and a Greedy
Randomised algorithm. The selection rule of the latter
is evolved by Genetic Programming. The stochastic
version is tackled by dispatching rules, also evolved
via Genetic Programming. The proposed approaches are
validated using real data from a leading company in the
mining sector. Results show that the new heuristic
presents similar results to the company's algorithm in
a considerably shorter computational time. Moreover, we
perform extensive computational experiments to validate
the methods on a wide spectrum of randomly generated
instances. Finally, as managing uncertainty is
fundamental for the effectiveness of these operations,
distinct strategies are compared, ranging from purely
predictive to completely reactive scheduling. We
conclude that re-scheduling with high frequency is the
best approach to avoid performance deterioration under
schedule disruptions, and using the evolved dispatching
rules incur fewer deviations from the original
schedule",
- }
Genetic Programming entries for
Cristiane Maria Santos Ferreira
Luis Goncalo Rodrigues Reis Figueira
Pedro Sanches Amorim
Alexandre Pigatti
Citations