Integrated maintenance and spare part optimization for moving assets

Eruguz, Ayse Sena; Tan, Tarkan; Houtum, Geert‐Jan van

doi:10.1080/24725854.2017.1312037

Cited by 32 publications

(14 citation statements)

References 36 publications

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“…Still, according to our review and to the best of our knowledge, only three journal papers are published in the last five years that are directly relevant to the research presented in this paper. First, the paper by Eruguz, Tan and van Houtum (2018) attempts to minimize the expected total discounted cost of spare part deliveries, part replacements, and inventory holding over an infinite planning horizon. To do so, the authors formulate the problem as a Markov decision process and use numerical experiments to show that the cost savings obtained by the integrated optimization of spare part inventory and part replacement decisions are significant.…”

Section: Literature Reviewmentioning

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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Section: Literature Reviewmentioning

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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“…Multi echelon has a more complex system that consist of several warehouse and service facilities, multiple suppliers, and several customers. Therefore at an early stage this survey will categorize primary studies based on its networks (single echelon or multi echelon), because this is the first step in identifying real cases that shows in Table V. [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19] Multi Echelon [20], [21], [22], [23], [24] H…”

Section: G Research Topics In Joint Optimization Of Inventory and Prmentioning

confidence: 99%

Joint Optimization of Inventory and Preventive Maintenance: Systematic Literature Review and Research Agenda

Muttaqin¹,

Damayanti²

2018

jiii

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Recently, integration between activities in operational areas has more attention to improve and optimize in supply chain management. Joint optimization of inventory and maintenance are one of important activities in achieve operational excellence. Good spare part inventory management aims to minimize buffer time between supply time and demand needs in maintenance activities and operational activities by increasing availability of spare parts at the time required. The authors only focusing on joint spare parts inventory and preventive maintenance. This paper uses sytematic literature review (SLR) as a methodology in reviewing literature. This paper will classified of each research on primary studies by objective function, decision variables, variables, and methods. From the literature review results it is known that joint optimization of spare parts inventory and preventive maintenance can be explored further on multi echelon networks and considering several factors such as deterioration and obsolence, moreover some papers doing integrated calculations simultaneously not sequential or separately. 

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“…For many moving assets, the sequence and the duration of operating modes are more realistically modelled as random variables since there exists various uncertainties about the future operations and environments . The operation process of moving assets can be well‐presented by a Markov process . In this respect, we assume that the operation process evolves as a continuous‐time Markov chain (CTMC) on discrete state space

I

.…”

Section: Markov Decision Process Formulationmentioning

confidence: 99%

Optimizing usage and maintenance decisions for k‐out‐of‐n systems of moving assets

Eruguz

Tan

Houtum

2017

Naval Research Logistics

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Abstract:We consider an integrated usage and maintenance optimization problem for a k-outof-n system pertaining to a moving asset. The k-out-of-n systems are commonly utilized in practice to increase availability, where n denotes the total number of parallel and identical units and k the number of units required to be active for a functional system. Moving assets such as aircrafts, ships, and submarines are subject to different operating modes. Operating modes can dictate not only the number of system units that are needed to be active, but also where the moving asset physically is, and under which environmental conditions it operates. We use the intrinsic age concept to model the degradation process. The intrinsic age is analogous to an intrinsic clock which ticks on a different pace in different operating modes. In our problem setting, the number of active units, degradation rates of active and standby units, maintenance costs, and type of economic dependencies are functions of operating modes. In each operating mode, the decision maker should decide on the set of units to activate (usage decision) and the set of units to maintain (maintenance decision). Since the degradation rate differs for active and standby units, the units to be maintained depend on the units that have been activated, and vice versa. In order to minimize maintenance costs, usage and maintenance decisions should be jointly optimized. We formulate this problem as a Markov decision process and provide some structural properties of the optimal policy. Moreover, we assess the performance of usage policies that are commonly implemented for maritime systems. We show that the cost increase resulting from these policies is up to 27% for realistic settings. Our numerical experiments demonstrate the cases in which joint usage and maintenance optimization is more valuable.

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Integrated maintenance and spare part optimization for moving assets

Cited by 32 publications

References 36 publications

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

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

Joint Optimization of Inventory and Preventive Maintenance: Systematic Literature Review and Research Agenda

Optimizing usage and maintenance decisions for k‐out‐of‐n systems of moving assets

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