The stochastic liner shipping fleet repositioning problem with uncertain container demands and travel times

IF 2.1 Q2 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

EURO Journal on Transportation and Logistics Pub Date : 2021-01-01 DOI:10.1016/j.ejtl.2021.100052

Stefan Kuhlemann , Jana Ksciuk , Kevin Tierney , Achim Koberstein

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引用次数: 3

Abstract

Liner shipping repositioning is the costly process of moving container ships between services in a liner shipping network to adjust the network to the changing demands of customers. Existing deterministic models for the liner shipping fleet repositioning problem (LSFRP) ignore the inherent uncertainty present in the input parameters. Assuming these parameters are deterministic could lead to extra costs when plans computed by a deterministic model are realized. We introduce an optimization model for the stochastic LSFRP that handles uncertainty regarding container demands and ship travel times. We extend existing LSFRP instances with uncertain parameters and use this new dataset to evaluate our model. We demonstrate the influence of uncertain demand and travel times on the resulting repositioning plans. Furthermore, we show that stochastic optimization generates solutions yielding up to ten times higher expected values and more robust solutions, measured against the CVaR90 objective, for decision-makers in the liner shipping industry compared to the application of deterministic optimization in the literature.

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具有不确定集装箱需求和行程时间的随机班轮船队重新定位问题

班轮运输重新定位是在班轮运输网络中移动集装箱船以调整网络以适应客户不断变化的需求的昂贵过程。现有的班轮船队重新定位问题的确定性模型忽略了输入参数中存在的固有不确定性。当实现由确定性模型计算的计划时，假设这些参数是确定性的可能会导致额外的成本。我们介绍了一个随机LSFRP的优化模型，该模型处理关于集装箱需求和船舶旅行时间的不确定性。我们扩展了现有的具有不确定参数的LSFRP实例，并使用这个新的数据集来评估我们的模型。我们论证了不确定的需求和出行时间对重新定位方案的影响。此外，我们表明，与文献中的确定性优化应用相比，随机优化产生的解决方案产生高达10倍的期望值和更稳健的解决方案，针对CVaR90目标进行测量。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

EURO Journal on Transportation and Logistics Multiple-

CiteScore

4.60

自引率

0.00%

发文量

审稿时长

129 days

期刊介绍： The EURO Journal on Transportation and Logistics promotes the use of mathematics in general, and operations research in particular, in the context of transportation and logistics. It is a forum for the presentation of original mathematical models, methodologies and computational results, focussing on advanced applications in transportation and logistics. The journal publishes two types of document: (i) research articles and (ii) tutorials. A research article presents original methodological contributions to the field (e.g. new mathematical models, new algorithms, new simulation techniques). A tutorial provides an introduction to an advanced topic, designed to ease the use of the relevant methodology by researchers and practitioners.