Maintenance Service Logistics

Arts, Jos; Basten, Rob; Houtum, G.J.J.A.N. van

doi:10.1007/978-3-319-92447-2_22

Cited by 7 publications

(3 citation statements)

References 44 publications

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“…However, it is often the case that not all times are convenient to do maintenance on a system. Examples include systems in production lines, the aerospace or maritime industry, or other systems in industries where unplanned downs severely interrupt operations (Arts et al 2019). In that case, the policy maker may choose to apply minimal repair until the planned maintenance moment is reached, meaning that the system is restored to a state right before the failure anytime a failure occurs.…”

Section: Literature Reviewmentioning

confidence: 99%

After-Sales Services During an Asset’s Lifetime: Collaborative Planning of System Upgrades

et al. 2023

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We consider a physical asset consisting of complex systems, where the systems may require upgrades during the lifetime of the asset. In practice, the asset owner and system supplier can make the upgrade decisions together, requiring a decision-support model that can be jointly used to optimize the total benefit for both parties. Motivated by a real-life use case including an asset owner and a system supplier, we build a continuous-time model to optimize the upgrade decisions of a system during the fixed lifetime of the asset. In our model, we capture the key critical factors that drive the upgrade decisions: increasing functionality requirements due to evolving technology, age-dependent maintenance costs, a predetermined overhaul plan of the asset, and the lifetime of the asset. A system upgrade is less costly if it is executed jointly with an asset overhaul. We first analyze the case with no additional cost of upgrading outside an overhaul. We analytically characterize the structure of the optimal upgrade policy under various realistic assumptions that lead to different types of cost functions. We then use these results as a building block to characterize the optimal policy for a generalized cost function. When there is a penalty for upgrading outside an overhaul moment, we propose a dynamic programming approach that efficiently determines the optimal upgrade policy by using our analytical results. We also prove that as this penalty increases, the optimal policy can only change to one where the number of upgrades not jointly executed with overhauls is reduced. However, the optimal number of upgrades is a nonincreasing function of this penalty. Also, surprisingly, more overhauls may lead to a smaller number of upgrades under the optimal policy. Funding: This publication is part of the project “Maritime Remote Control Tower for Service Logistics Innovation (MARCONI)” (project 439.18.309) of the research program “Integrator-Logistics as Enabler for Enhancing Society,” which is (partly) financed by the Dutch Research Council (NWO).

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Section: Literature Reviewmentioning

confidence: 99%

After-Sales Services During an Asset’s Lifetime: Collaborative Planning of System Upgrades

et al. 2023

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“…However, it is often the case that not all times are convenient to do maintenance on a system. Examples include systems in production lines, the aerospace or maritime industry, or other systems in industries where unplanned downs severely interrupt operations (Arts et al, 2019). In that case, the policy maker may choose to apply minimal repair until the planned maintenance moment is reached, i.e.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Maintaining a satisfactory operational level for assets, i.e. capital goods, during their entire lifetime is a very challenging problem (Pierskalla and Voelker, 1976;Arts et al, 2019). Assets typically have complex designs with many different systems installed.…”

Section: Introductionmentioning

confidence: 99%

After-sales services during an asset's lifetime: collaborative planning of system upgrades

Sloothaak¹,

Akçay²,

Heijden³

et al. 2022

Preprint

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We consider a continuous-time stochastic model that aims to find an optimal upgrade policy for a single system in an asset with a fixed overhaul planning. In a novel model, we include the key critical factors that may drive an asset owner to upgrade a system: increasing functionality requirements due to evolving technology, age-dependent maintenance costs, a predetermined overhaul plan, and a finite lifetime of the asset. We characterize the structure of the optimal upgrade policy through analytical results, and use these to establish an efficient solution approach based on dynamic programming. We also consider the effects of the input parameters on the optimal total costs, both analytically as well as numerically.

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Modeling Rolling Stock Maintenance Logistics at Dutch Railways with Declarative Business Artifacts

Smit

Eshuis

2019

Business Process Management Workshops

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Maintenance Service Logistics

Cited by 7 publications

References 44 publications

After-Sales Services During an Asset’s Lifetime: Collaborative Planning of System Upgrades

After-Sales Services During an Asset’s Lifetime: Collaborative Planning of System Upgrades

After-sales services during an asset's lifetime: collaborative planning of system upgrades

Modeling Rolling Stock Maintenance Logistics at Dutch Railways with Declarative Business Artifacts

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