Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 355-372 |
| Number of pages | 18 |
| Journal | Annals of Operations Research |
| Volume | 242 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2016 |
Keywords
- Ant colony optimization
- Parallel ACO
- Resource constrained job scheduling
- Simulated annealing
Cite this
}
Parallel ant colony optimization for resource constrained job scheduling. / Thiruvady, Dhananjay; Ernst, Andreas T.; Singh, Gaurav.
In: Annals of Operations Research, Vol. 242, No. 2, 2016, p. 355-372.Research output: Contribution to journal › Article › Research › peer-review
TY - JOUR
T1 - Parallel ant colony optimization for resource constrained job scheduling
AU - Thiruvady, Dhananjay
AU - Ernst, Andreas T.
AU - Singh, Gaurav
PY - 2016
Y1 - 2016
N2 - 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.
AB - 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.
KW - Ant colony optimization
KW - Parallel ACO
KW - Resource constrained job scheduling
KW - Simulated annealing
UR - http://www.scopus.com/inward/record.url?scp=84896417777&partnerID=8YFLogxK
U2 - 10.1007/s10479-014-1577-7
DO - 10.1007/s10479-014-1577-7
M3 - Article
VL - 242
SP - 355
EP - 372
JO - Annals of Operations Research
JF - Annals of Operations Research
SN - 0254-5330
IS - 2
ER -