Robust Fleet Sizing and Deployment for Industrial and Independent Bulk Ocean Shipping Companies

Alvarez, J. Fernando; Tsilingiris, Panagiotis S.; Engebrethsen, Erna S.; Kakalis, Nikolaos M.P.

doi:10.3138/infor.49.2.093

Cited by 24 publications

(25 citation statements)

References 34 publications

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“…Note that there are also some studies which tackle the FSP under uncertainty in other applications (e.g. military [21,22,23,24,25], maritime [26,27,28,29] and rail [30,31]), however, due to the differences between the nature of those problems and that of the FSP in ESTTs, we do not review these studies here. Shadrokh and Kianfar [32] proposed a genetic algorithm (GA) for the resource investment project scheduling problem (RIPSP).…”

Section: Related Literaturementioning

confidence: 99%

Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]

Kavakeb

Nguyễn

Yang

et al. 2016

IEEE Comput. Intell. Mag.

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Evolutionary fleet sizing in static and uncertain environments with shuttle transportation tasks -the case studies of container terminals http://researchonline.ljmu.ac.uk/1851/ Article LJMU has developed LJMU Research Online for users to access the research output of the University more effectively. Copyright © and Moral Rights for the papers on this site are retained by the individual authors and/or other copyright owners. Users may download and/or print one copy of any article(s) in LJMU Research Online to facilitate their private study or for non-commercial research. You may not engage in further distribution of the material or use it for any profit-making activities or any commercial gain.The version presented here may differ from the published version or from the version of the record. Please see the repository URL above for details on accessing the published version and note that access may require a subscription. These factors, however, have not been fully considered previously, leaving an important gap in the current research. This paper attempts to close this gap by taking into account the aforementioned factors. An evolutionary algorithm was proposed to solve this problem under static and uncertain situations. Two container ports were selected as case studies for this research. For the static cases, the state-of-the-art CPLEX solver was considered as the benchmark. Comparison results on real-world scenarios show that in the majority of cases the proposed algorithm outperforms CPLEX in terms of solvability and processing time. For the uncertain cases, a high-fidelity simulation model was considered as the benchmark. Comparison results on real-world scenarios with uncertainty show that in most cases the proposed algorithm could provide an accurate robust fleet size. These results also show that uncertainty can have a significant impact on the optimal fleet size.

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Section: Related Literaturementioning

confidence: 99%

Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]

Kavakeb

Nguyễn

Yang

et al. 2016

IEEE Comput. Intell. Mag.

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“…A somewhat high level abstraction of demand and service is used in this paper, which is based on trade lanes between geographic areas. Similar abstractions can be found in, e.g., Alvarez et al [1] and Pantuso et al [20]. A trade represents a transportation arrangement from one geographic area to another; and a trade lane consists of a number of loading and discharging ports at the origin and destination geographic areas, respectively.…”

Section: Problem Descriptionmentioning

confidence: 82%

“…There are not many studies on maritime fleet composition or fleet deployment problems in the literature that take uncertainty into account, some exceptions include: Meng and Wang [16] with uncertain demand, using a distribution-based model and chance constraints; Shyshou et al [27] with uncertain weather conditions and vessel rates using simulation analysis; Alvarez et al [1] with uncertain second-hand purchase and sale prices, charter rates etc., (but not demand), and using robust optimization; Loxton et al [15] with uncertain numbers of each type of vehicles needed using a method based on dynamic programming and Golden section search; Wang et al [32] with uncertain demand and chance constraints using sample average approximation; Fagerholt et al [4] with uncertain demand quantities and patterns, using simulation and a rolling-horizon framework; Pantuso et al [20] with uncertain demand, fuel costs and ship values etc., and long-term multi-period considerations in a maritime fleet renewal problem, while Mørch et al [17] also presented a similar shipping capacity renewal problem with financial factors.…”

Section: Decision Making Under Uncertaintymentioning

confidence: 99%

Planning for charters: A stochastic maritime fleet composition and deployment problem

Wang

Fagerholt

Wallace

2018

Omega

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(2017). Planning for charters: a stochastic maritime fleet composition and deployment problem. Omega, in press. doi: 10.1016/j.omega.2017.07.007 AbstractThis paper introduces a chartering problem that arises in the shipping industry. The chartering decisions determine the time-charter contracts to enter into, in particular, how many ships of each type to charter in, and for how long they are to be hired. We show that this problem can be modeled as a tactical fleet composition problem, with integrated fleet deployment and speed optimization, which also takes into account market uncertainties. We propose a two-stage stochastic programming model, and present a computational study based on the case of Odfjell, a leading chemical shipping company based in Bergen, Norway. We show how the charter plans produced can change depending on different modeling choices. We also show why and how different charter plans affect the company's overall performance, in order to provide guidance in helping the company make its chartering decisions.Planning for charters: a stochastic maritime fleet composition and deployment problem Abstract This paper introduces a chartering problem that arises in the shipping industry.The chartering decisions determine the time-charter contracts to enter into, in particular, how many ships of each type to charter in, and for how long they are to be hired. We show that this problem can be modeled as a tactical fleet composition problem, with integrated fleet deployment and speed optimization, which also takes into account market uncertainties. We propose a two-stage stochastic programming model, and present a computational study based on the case of Odfjell, a leading chemical shipping company based in Bergen, Norway. We show how the charter plans produced can change depending on different modeling choices. We also show why and how different charter plans affect the company's overall performance, in order to provide guidance in helping the company make its chartering decisions.

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“…They did not run experiments but suggested the Lagrangian relaxation for solving the MIP formulation proposed for the problem. At last, Alvarez et al (2011) explicitly considered uncertainty while studying the fleet evolution. A robust optimization model was proposed to find solutions which are feasible against random variations in the selling and purchasing prices of ships.…”

Section: Maritime Fleet Renewal Problemsmentioning

confidence: 99%

“…Only few papers explicitly treat uncertainty. If only strategic problems are considered the only exception is the robust optimization model presented by Alvarez et al (2011) (see Section 3.3). Fagerholt et al (2010), and Shyshou et al (2010) faced uncertainty by means of simulation tools.…”

Section: Research Contributions and Future Perspectivesmentioning

confidence: 99%

A survey on maritime fleet size and mix problems

Pantuso

Fagerholt

Hvattum

2014

European Journal of Operational Research

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This paper presents a literature survey on the fleet size and mix problem in maritime transportation. Fluctuations in the shipping market and frequent mismatches between fleet capacities and demands highlight the relevance of the problem and call for more accurate decision support. After analyzing the available scientific literature on the problem and its variants and extensions, we summarize the state of the art and highlight the main contributions of past research. Furthermore, by identifying important real life aspects of the problem which past research has failed to capture, we uncover the main areas where more research will be needed.

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Robust Fleet Sizing and Deployment for Industrial and Independent Bulk Ocean Shipping Companies

Cited by 24 publications

References 34 publications

Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]

Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]

Planning for charters: A stochastic maritime fleet composition and deployment problem

A survey on maritime fleet size and mix problems

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