Parallel ant colony optimization for resource constrained job scheduling

    Research output: Contribution to journalArticleResearchpeer-review

    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 languageEnglish
    Pages (from-to)355-372
    Number of pages18
    JournalAnnals of Operations Research
    Volume242
    Issue number2
    DOIs
    Publication statusPublished - 2016

    Keywords

    • Ant colony optimization
    • Parallel ACO
    • Resource constrained job scheduling
    • Simulated annealing

    Cite this

    @article{06a631ef3b464cf39065ce54308aad72,
    title = "Parallel ant colony optimization for resource constrained job scheduling",
    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.",
    keywords = "Ant colony optimization, Parallel ACO, Resource constrained job scheduling, Simulated annealing",
    author = "Dhananjay Thiruvady and Ernst, {Andreas T.} and Gaurav Singh",
    year = "2016",
    doi = "10.1007/s10479-014-1577-7",
    language = "English",
    volume = "242",
    pages = "355--372",
    journal = "Annals of Operations Research",
    issn = "0254-5330",
    publisher = "Springer-Verlag London Ltd.",
    number = "2",

    }

    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 journalArticleResearchpeer-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 -