Parallel ant colony optimization for resource constrained job scheduling

In mining supply chains, large combinatorial optimization problems arise. These are NP-hard and typically require a large number of computing resources to solve them. In particular, the run-time overheads can become increasingly prohibitive with increasing problem sizes. Parallel methods provide a w...

Full description

Saved in:
Bibliographic Details
Published in:Annals of operations research 2016-07, Vol.242 (2), p.355-372
Main Authors: Thiruvady, Dhananjay, Ernst, Andreas T., Singh, Gaurav
Format: Article
Language:English
Subjects:
Algorithms
Ant colony optimization
Business and Management
Combinatorics
Constraints
Cooling
Management
Mathematical optimization
Methods
Mines
Mining
Operations research
Operations Research/Decision Theory
Optimization
Pheromones
Ports
Project management
Resource scheduling
Run time (computers)
Scheduling
Scheduling (Management)
Searching
Software
Supply chains
Theory of Computation
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In mining supply chains, large combinatorial optimization problems arise. These are NP-hard and typically require a large number of computing resources to solve them. In particular, the run-time overheads can become increasingly prohibitive with increasing problem sizes. Parallel methods provide a way to manage such run-time issues by utilising several processors in independent or shared memory architectures. However it is not obvious how to adapt serial optimisation algorithms to perform best in a parallel environment. Here, we consider a resource constrained scheduling problem which is motivated in mining supply chains and present two popular meta-heuristics, ant colony optimization (ACO) and simulated annealing and investigate how best to parallelize these methods on a shared memory architecture consisting of several cores. ACO’s solution construction framework is inherently parallel allowing a relatively straightforward parallel implementation. However, for best performance, ACO needs an element of local search. This significantly complicates the paralellization. Several alternative schemes for parallel ACO with elements of local search are considered and evaluated empirically. We find that ACO with local search is the most effective single-threaded algorithm. The best parallel implementation can obtain similar quality results to the serial method in significantly less elapsed time.
ISSN:0254-5330
1572-9338
DOI:10.1007/s10479-014-1577-7