A hybrid method for transportation with stochastic demand

Leandro Luis Corso, Mark Wallace

    Research output: Contribution to journalArticleResearchpeer-review

    Abstract

    In industrial transportation, the forecast demand at each destination may be affected by a number of factors. Consequently, a conventional transport plan often fails to match the reality, and the planned transport capacity is either insufficient to meet the demand or wastefully excessive. In this paper, we introduce a new algorithm to generate a minimal cost transport plan that meets a given level of reliability. The reliability of a candidate solution is measured through simulating each candidate solution against a large number of scenarios. To search for reliable solutions, a genetic algorithm method is applied as an external loop. The minimal transport cost is achieved through a deterministic optimisation algorithm. We show that this problem decomposition in principle enables the optimal solution of the original non-deterministic problem to be found. Experimental results establish the practical usefulness of the proposed algorithm.
    Original languageEnglish
    Pages (from-to)342 - 354
    Number of pages13
    JournalInternational Journal of Logistics
    Volume18
    Issue number4
    DOIs
    Publication statusPublished - 2015

    Cite this

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    title = "A hybrid method for transportation with stochastic demand",
    abstract = "In industrial transportation, the forecast demand at each destination may be affected by a number of factors. Consequently, a conventional transport plan often fails to match the reality, and the planned transport capacity is either insufficient to meet the demand or wastefully excessive. In this paper, we introduce a new algorithm to generate a minimal cost transport plan that meets a given level of reliability. The reliability of a candidate solution is measured through simulating each candidate solution against a large number of scenarios. To search for reliable solutions, a genetic algorithm method is applied as an external loop. The minimal transport cost is achieved through a deterministic optimisation algorithm. We show that this problem decomposition in principle enables the optimal solution of the original non-deterministic problem to be found. Experimental results establish the practical usefulness of the proposed algorithm.",
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    language = "English",
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    A hybrid method for transportation with stochastic demand. / Corso, Leandro Luis; Wallace, Mark.

    In: International Journal of Logistics, Vol. 18, No. 4, 2015, p. 342 - 354.

    Research output: Contribution to journalArticleResearchpeer-review

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    AU - Corso, Leandro Luis

    AU - Wallace, Mark

    PY - 2015

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    AB - In industrial transportation, the forecast demand at each destination may be affected by a number of factors. Consequently, a conventional transport plan often fails to match the reality, and the planned transport capacity is either insufficient to meet the demand or wastefully excessive. In this paper, we introduce a new algorithm to generate a minimal cost transport plan that meets a given level of reliability. The reliability of a candidate solution is measured through simulating each candidate solution against a large number of scenarios. To search for reliable solutions, a genetic algorithm method is applied as an external loop. The minimal transport cost is achieved through a deterministic optimisation algorithm. We show that this problem decomposition in principle enables the optimal solution of the original non-deterministic problem to be found. Experimental results establish the practical usefulness of the proposed algorithm.

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