Optimal spare parts management for vessel maintenance scheduling

Kian, Ramez; Bektaş, Tolga; Ouelhadj, Djamila

doi:10.1007/s10479-018-2907-y

Cited by 20 publications

(11 citation statements)

References 21 publications

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“…The paper [8] describes a mathematical programming model of the problem, as well as a shortest path dynamic programming formulation for a single part which solves the problem in polynomial time complexity.…”

Section: Methods Algorithmmentioning

confidence: 99%

Method for Optimizing Maintenance Location Within the Industrial Plant

David¹,

Tuhý²,

Jančíková³

2019

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Nowadays, more and more emphasis is placed on the overall maintenance concept and strategy, not only in all industrial sectors, but increasingly, maintenance management is becoming part of a long-term strategy for both businesses and other institutions. One of the priority objectives of all types of maintenance is the return on investment that has been put into maintenance and, of course, the smooth operation of machines and equipment without failures that negatively affect the entire product manufacturing process. Obviously, a company with a poorly developed maintenance structure, will only be very hardly competitive in today's market, compared to a company with the same or similar manufacturing program, which, due to a more sophisticated maintenance system, often has lower maintenance costs. Less attention is paid to maintenance in industrial enterprises. In the article, it describes and characterizes this basic reliability feature and proposes a method for optimizing the maintenance location within an industrial enterprise. The proposed method is universal and can be used to support maintenance assurance management in both industrial and non-industrial sectors.

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Section: Methods Algorithmmentioning

confidence: 99%

Method for Optimizing Maintenance Location Within the Industrial Plant

David¹,

Tuhý²,

Jančíková³

2019

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“…Third, the work of Kian et al, (2019), is considered marginally related to the work presented in this paper in the sense that it also provides an integrated solution for the challenge of spare parts management for maintenance scheduling in the maritime industry. But this is where the relevancy with the research presented in our paper stops, since the authors focus on a specific problem of Condition-Based Monitoring predictive maintenance dealing with a vessel operating on a given route that is defined by a sequence of port visits.…”

Section: Literature Reviewmentioning

confidence: 99%

“…The maintenance of the machinery onboard a vessel is a critical task, since any engine failure results in delays and down-times in the voyage of a vessel, which translates into additional cost and penalties (Kian, Bektaş & Ouelhadj, 2019). Therefore, ship management companies establish full proof and robust planned maintenance frameworks and systems, whilst giving high prioritization to the planned maintenance of their vessels and undertaking cross departmental projects to ensure the timely delivery of high-quality spare parts, with as low a cost to the ownership as possible.…”

Section: Introductionmentioning

confidence: 99%

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Anglou

Ponis

Spanos

2021

JIEM

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Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions.Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and machine learning algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels.Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years.Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom line.

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“…Besides machine learning techniques, statistical approaches also have been widely used in PdM. Since the spare part management is a key issue in the industry, Khan et al (2019) [16] deployed a mathematical programming model to formulate the failure time of a vessel to optimize the spare part management based on sensor data. Meanwhile, the shortest path dynamic programming formulation is deployed to address the polynomial-time complexity.…”

Section: Automobile Maintenance Modelling Using Gcforestmentioning

confidence: 99%

Automobile Maintenance Modelling Using gcForest

Chen

Liu

Sun

et al. 2020

2020 IEEE 16th International Conference on Automation Science and Engineering (CASE)

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Automobile maintenance has gained increasing attention in recent years. If the failure time of an automobile can be predicted, it can bring tangible benefits to automobile fleet management. The Multi-Grained Cascade Forest (gcForest) is a tree-based deep learning algorithm, which was originally developed for image classification, but is potentially a helpful tool in automobile maintenance. This study aims to introduce the gcForest into automobile maintenance based on automobile historical maintenance data and geographical information system (GIS) data. The experimental results reveal that the gcForest shows merits in automobile time-between-failure (TBF) modelling, while it requires less computational cost.

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Optimal spare parts management for vessel maintenance scheduling

Cited by 20 publications

References 21 publications

Method for Optimizing Maintenance Location Within the Industrial Plant

Method for Optimizing Maintenance Location Within the Industrial Plant

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Automobile Maintenance Modelling Using gcForest

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