Next Article in Journal
Blind Quality Evaluation for Screen Content Images Based on Regionalized Structural Features
Next Article in Special Issue
Algorithms for Finding Shortest Paths in Networks with Vertex Transfer Penalties
Previous Article in Journal
A Weighted Ensemble Learning Algorithm Based on Diversity Using a Novel Particle Swarm Optimization Approach
Previous Article in Special Issue
The Use of an Exact Algorithm within a Tabu Search Maximum Clique Algorithm
Open AccessArticle

Solution Merging in Matheuristics for Resource Constrained Job Scheduling

1
School of Information Technology, Deakin University, Geelong 3126, Australia
2
Artificial Intelligence Research Institute (IIIA-CSIC), Campus of the UAB, 08193 Bellaterra, Spain
3
School of Mathematics, Monash University, Melbourne 3800, Australia
*
Author to whom correspondence should be addressed.
Algorithms 2020, 13(10), 256; https://doi.org/10.3390/a13100256
Received: 7 September 2020 / Revised: 29 September 2020 / Accepted: 1 October 2020 / Published: 9 October 2020
(This article belongs to the Special Issue Algorithms for Graphs and Networks)
Matheuristics have been gaining in popularity for solving combinatorial optimisation problems in recent years. This new class of hybrid method combines elements of both mathematical programming for intensification and metaheuristic searches for diversification. A recent approach in this direction has been to build a neighbourhood for integer programs by merging information from several heuristic solutions, namely construct, solve, merge and adapt (CMSA). In this study, we investigate this method alongside a closely related novel approach—merge search (MS). Both methods rely on a population of solutions, and for the purposes of this study, we examine two options: (a) a constructive heuristic and (b) ant colony optimisation (ACO); that is, a method based on learning. These methods are also implemented in a parallel framework using multi-core shared memory, which leads to improving the overall efficiency. Using a resource constrained job scheduling problem as a test case, different aspects of the algorithms are investigated. We find that both methods, using ACO, are competitive with current state-of-the-art methods, outperforming them for a range of problems. Regarding MS and CMSA, the former seems more effective on medium-sized problems, whereas the latter performs better on large problems. View Full-Text
Keywords: merge search; construct, solve, merge and adapt; mixed integer programming; ant colony optimisation; resource constrained job scheduling merge search; construct, solve, merge and adapt; mixed integer programming; ant colony optimisation; resource constrained job scheduling
Show Figures

Figure 1

MDPI and ACS Style

Thiruvady, D.; Blum, C.; Ernst, A.T. Solution Merging in Matheuristics for Resource Constrained Job Scheduling. Algorithms 2020, 13, 256.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop