Mathematical approach for fleet planning under complicated circumstances

Yang, Qiuping; Zhang, Hao; Sang, Huiyun; Xie, Xuhui

doi:10.1007/s12204-014-1495-5

Cited by 5 publications

(3 citation statements)

References 8 publications

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“…A series of methods were proposed. [3][4][5][6][7][8] Yang et al 9 introduced a new method in 2014. Some effective methods on route choice and operation area can be found in these literatures.…”

Section: Literature Reviewmentioning

confidence: 99%

Fleet route selection prediction problem based on support vector machine

Guan

Shen²,

et al. 2019

Advances in Mechanical Engineering

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Bulk shipping transport is an important part of ocean transportation. One of the most important aspects for bulk ship owners is to make choices about the ship's operational area for the next planning period. In this article, based on the Baltic Supermax Index and historical decision data of different companies, the support vector machine model is used to predict the dry bulk carrier route selection, which provides a solution to this problem. The numerical results show that the model and the algorithm proposed in the paper can well work and can achieve good precision. Via comparative analysis, we prove that the model we proposed in this article has better performance than some other common methods in the research area. The proposed model would support dry bulk shipping company in route choice so as to balance the profit and potential opportunity when making choice for ships' route selection.

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Section: Literature Reviewmentioning

confidence: 99%

Fleet route selection prediction problem based on support vector machine

Guan

Shen²,

et al. 2019

Advances in Mechanical Engineering

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“…39,40 Meng et al 41 proposed a solution method which combined a dual decomposition with Lagrangian relaxation method to integrate the sample average approximation (SAA) method under uncertain demand. Yang et al 42 established a mixedinteger programming model based on multiple influencing factors. In front of complicated future circumstance, Halvorsen-Weare et al 43 put forward a solution algorithm and several robustness strategies with the uncertain problems.…”

Section: Literature Reviewmentioning

confidence: 99%

Fleet routing and scheduling problem based on constraints of chance

Guan

Peng

Chen

et al. 2017

Advances in Mechanical Engineering

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Tramp shipping transport is an important part of ocean transportation. However, facing the spot market with many uncertain conditions, it is not easy for fleet operators to plan vessel's routes and schedule in the later period time, especially considering the situation that loading time window for a lot of cargoes has strong randomness. This article designed a linear programming model with chance constraints for the time window of loading cargo. Before the optimization, a survey for the waiting time of ships for berths is carried out in some of the ports with large export volume. Combined with the degree of acceptance how long ship owners can wait for the berth, the uncertain time window constraints can be transformed into deterministic constraints. The model is solved by column generation optimization technique. The model and algorithm are verified by a case of Panamax bulker fleet planning in real market. The results show that the model and the algorithm proposed in the article can well work on large-scale problem and can achieve good precision. Also, via sensitivity analysis, we provide decision makers good reference to balance profit and risks coming from randomness.

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“…Most existing methods of conventional multi-objective simulation-optimisation approach are originally designed and used for deterministic optimisation problems (See e.g. (Yang 2014)). They aim to find 'optimal' decisions for maximising one group of objectives while minimising another groups of objectives given that future unfolds as we projected in our imaginable reference scenarios.…”

Section: Introductionmentioning

confidence: 99%

Comparing Implications of ‘Robustness’ and ‘Optimality’ for Decision Support under Uncertainty

Turan

Elsawah

Ryan

2019

INCOSE International Symp

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Conventional simulation‐optimisation approach in decision support under uncertainty aims to find optimal decisions which can meet decision objective(s) under reference scenarios with well‐characterised uncertainty. However, futures often emerge with unexpected circumstances which cannot be predicted in our pre‐specified reference scenarios, and their uncertainty cannot be fully characterised a priori. This article uses robust optimisation based on simulation models as an alternative approach to address such uncertainties of decision support. To show how the two approaches work and how better robust optimisation can be, we formulate a fleet mix problem under uncertainty and implement it in two experiments to compare the results. We conclude that robust optimisation could lead to decisions with probably lower performance than conventional optimisation at imagined future scenarios. However, the advantage of robust optimisation is that the results have better performance in overall and are more reliable over a wide variety of scenarios in the future.

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Mathematical approach for fleet planning under complicated circumstances

Cited by 5 publications

References 8 publications

Fleet route selection prediction problem based on support vector machine

Fleet route selection prediction problem based on support vector machine

Fleet routing and scheduling problem based on constraints of chance

Comparing Implications of ‘Robustness’ and ‘Optimality’ for Decision Support under Uncertainty

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