Condition-based maintenance optimization via stochastic programming with endogenous uncertainty

Leo, Egidio; Engell, Sebastian

doi:10.1016/j.compchemeng.2021.107550

Cited by 8 publications

(3 citation statements)

References 28 publications

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“…The objective of that study is to minimise fixed and variable maintenance costs with a feasible schedule. A similar study that integrates production planning with maintenance has been investigated by [8]. In that study, production planning was used to forecast machine degradation, which was then used to determine the maintenance strategy.…”

Section: Related Workmentioning

confidence: 99%

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

Purnomo,

Abdul Wahab,

Singh

2023

IJIE

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In a mass customisation manufacturing system, the production schedule is tailored to the customer's specifications. However, the production system must be accompanied by an effective maintenance program to ensure that the production lines operate as intended. The purpose of this study is to optimise planned preventive maintenance across multiple production lines. An optimised Weibull distribution is proposed to model the machine's Mean Time Between Failures (MTBF), and the total expected maintenance cost is calculated using this distribution, taking into account the probability of the machines remaining operational and failing. Because the optimised Weibull distribution is a continuous distribution, in order to simulate the continuous time domain, it will be divided into several sub-systems and optimised using Bayesian optimisation during simulation. The maintenance scheduling is carried out by considering available time capacity after production scheduling was arranged. The study's findings indicate that the proposed method successfully optimised the planned maintenance schedule without interfering production activity with total cost for the proposed maintenance planning as low as IDR 50.017,75/maintenance unit time.

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Section: Related Workmentioning

confidence: 99%

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

Purnomo,

Abdul Wahab,

Singh

2023

IJIE

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“…The functions k(T), S(T), and ρ(T) can be expressed as second-degree polynomial functions of temperature, where the properties coefficients (S 0 , S 1 , S 2 , ρ 0 , ρ 1 , ρ 2 , k 1 , k 2 , k 3 ) are to be determined and were introduced before in Table 1. Specifically, Equation (12) represents the polynomial expression for S(T), ρ(T), and k(T).…”

Section: Slave Stagementioning

confidence: 99%

“…The solution to MINLPs problems has been the subject of intensive research over the years, leading to the development of various optimization solvers. These solvers can be broadly classified into deterministic [10] and stochastic methods [11][12][13]. Deterministic methods aim to find the optimal solution to the MINLP problem by iteratively exploring the feasible region of the problem.…”

Section: Introductionmentioning

confidence: 99%

Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm

et al. 2023

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Thermoelectric generators (TEGs) have the potential to convert waste heat into electrical energy, making them attractive for energy harvesting applications. However, accurately estimating TEG parameters from industrial systems is a complex problem due to the mathematical complex non-linearities and numerous variables involved in the TEG modeling. This paper addresses this research gap by presenting a comparative evaluation of three optimization methods, Particle Swarm Optimization (PSO), Salps Search Algorithm (SSA), and Vortex Search Algorithm (VSA), for TEG parameter estimation. The proposed integrated approach is significant as it overcomes the limitations of existing methods and provides a more accurate and rapid estimation of TEG parameters. The performance of each optimization method is evaluated in terms of root mean square error (RMSE), standard deviation, and processing time. The results indicate that all three methods perform similarly, with average RMSE errors ranging from 0.0019 W to 0.0021 W, and minimum RMSE errors ranging from 0.0017 W to 0.0018 W. However, PSO has a higher standard deviation of the RMSE errors compared to the other two methods. In addition, we present the optimized parameters achieved through the proposed optimization methods, which serve as a reference for future research and enable the comparison of various optimization strategies. The disparities observed in the optimized outcomes underscore the intricacy of the issue and underscore the importance of the integrated approach suggested for precise TEG parameter estimation.

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Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties

Liu,

Cao,

Zhou

et al. 2024

J. Marine. Sci. Appl.

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Condition-based maintenance optimization via stochastic programming with endogenous uncertainty

Cited by 8 publications

References 28 publications

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

Optimisation of Capacitated Planned Preventive Maintenance in Multiple Production Lines Using Optimisation-in-the-Loop Simulation

Parameter Estimation of a Thermoelectric Generator by Using Salps Search Algorithm

Integrated Optimization Scheduling Model for Ship Outfitting Production with Endogenous Uncertainties

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