Highlights A two-stage stochastic programming model adopts the Conditional-Value-at-Risk to measure profit variations. A risk-averse Benders decomposition method for the model is proposed for the model. The model significantly reduces the profit variations with little profit loss.

    Abstract Liner shipping companies need to satisfy the shipping demand from both the long-term contracts and the spot market. In general, the container shipping demand of the long-term contract has more stable volumes but lower freight rates compared with the spot market demand. It is thus a critical problem for the shipping companies how to provide discriminative shipping services to these two types of shipping demand through contract selection and container slot allocation to simultaneously increase the shipping profit and control its variations resulting from the demand variations. To handle this problem, a two-stage stochastic programming model is constructed in this paper which adopts the Conditional-Value-at-Risk (CVaR) to measure the profit variations. In the first stage, the shipping company selects the long-term contracts from a candidate contract set to sign with customers under the uncertainties in the show-up rates of containers in long-term contracts, and the volumes and the freight rates of the spot market demands. In the second stage, after all uncertainties have been realized, the shipping company determines the slot allocation for both the contract and spot demands. With the introduction of the CVaR, classical Benders decomposition cannot be directly applied to solve the risk-averse model. This paper thus develops a risk-averse Benders decomposition method tailored for the model. Numerical experiments are also conducted to verify the effectiveness of the solution method and provide some meaningful managerial insights.


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    Title :

    A risk-averse approach for joint contract selection and slot allocation in liner container shipping


    Contributors:
    Wang, Yadong (author) / Gu, Yuyun (author) / Wang, Tingsong (author) / Zhang, Jun (author)


    Publication date :

    2022-05-27




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English