The load optimization of a repairable system with gamma-distributed time-to-failure

Filus, Jerzy K.

doi:10.1016/0143-8174(87)90032-1

Cited by 9 publications

(4 citation statements)

References 2 publications

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“…A more short-term operational option is to control the equipment's deterioration process by adjusting the production rate. This applies, for instance, to wind turbine gearboxes and generators that deteriorate faster at higher speeds (Feng et al 2013, Zhang et al 2015, conveyor belts that fail more often when used at higher rotational speeds (Nourelfath and Yalaoui 2012), trucks that fail earlier when heavier loaded (Filus 1987), large computer clusters that fail more often under higher workloads (Ang andTham 2007, Iyer andRossetti 1986), and cutting tools that wear faster at higher speeds (Dolinšek et al 2001). In addition, the recent advent of the Internet of Things (IoT) allows to control production rates remotely and in real-time, thereby making it practically viable to exploit this relation between production and deterioration.…”

Section: Introductionmentioning

confidence: 99%

Condition-Based Production Planning: Adjusting Production Rates to Balance Output and Failure Risk

Broek

Teunter

Jonge

et al. 2020

M&SOM

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Problem Definition: Many production systems deteriorate over time as a result of load and stress caused by production. The deterioration rate of these systems typically depends on the production rate, implying that the equipment’s deterioration rate can be controlled by adjusting the production rate. We introduce the use of condition monitoring to dynamically adjust the production rate to minimize maintenance costs and maximize production revenues. We study a single-unit system for which the next maintenance action is scheduled upfront. Academic/Practical Relevance: Condition-based maintenance decisions are frequently seen in the literature. However, in many real-life systems, maintenance planning has limited flexibility and cannot be done last minute. As an alternative, we are the first to propose using condition information to optimize the production rate, which is a more flexible short-term decision. Methodology: We derive structural optimality results from the analysis of deterministic deterioration processes. A Markov decision process formulation of the problem is used to obtain numerical results for stochastic deterioration processes. Results: The structure of the optimal policy strongly depends on the (convex or concave) relation between the production rate and the corresponding deterioration rate. Condition-based production rate decisions result in significant cost savings (by up to 50%), achieved by better balancing the failure risk and production output. For several systems a win-win scenario is observed, with both reduced failure risk and increased expected total production. Furthermore, condition-based production rates increase robustness and lead to more stable profits and production output. Managerial Implications: Using condition information to dynamically adjust production rates provides opportunities to improve the operational performance of systems with production-dependent deterioration.

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Section: Introductionmentioning

confidence: 99%

Condition-Based Production Planning: Adjusting Production Rates to Balance Output and Failure Risk

Broek

Teunter

Jonge

et al. 2020

M&SOM

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“…Some working systems, such as cargo transporting trucks, trains, electric power lines, highways, computer processors, or other systems supporting varying amounts of load, require control of that load. On the one hand more load yields more gain, but, on the other hand, load (as stress) increment yields a corresponding increment of the system's failure rate which, in turn, depends on certain parameters [20]. The optimization problem can be formulated in several ways ( [20] [21]) but, in all cases, the main idea is to balance between an expected gain that follows a good (load) transportation and the loss in reliability of the transporting medium which decreases the overall gain.…”

Section: Load Control and Load Sharingmentioning

confidence: 99%

“…On the one hand more load yields more gain, but, on the other hand, load (as stress) increment yields a corresponding increment of the system's failure rate which, in turn, depends on certain parameters [20]. The optimization problem can be formulated in several ways ( [20] [21]) but, in all cases, the main idea is to balance between an expected gain that follows a good (load) transportation and the loss in reliability of the transporting medium which decreases the overall gain. Thus, for some (optimal) value of the load to be found, the pure expected gain earned by the system is maximal.…”

Section: Load Control and Load Sharingmentioning

confidence: 99%

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Parameter Dependence in Stochastic Modeling—Multivariate Distributions

Filus¹

2014

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We start with analyzing stochastic dependence in a classic bivariate normal density framework. We focus on the way the conditional density of one of the random variables depends on realizations of the other. In the bivariate normal case this dependence takes the form of a parameter (here the "expected value") of one probability density depending continuously (here linearly) on realizations of the other random variable. The point is, that such a pattern does not need to be restricted to that classical case of the bivariate normal. We show that this paradigm can be generalized and viewed in ways that allows one to extend it far beyond the bivariate or multivariate normal probability distributions class.

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