Preventive maintenance and replacement scheduling : models and algorithms.

Moghaddam, Kamran S.

doi:10.18297/etd/994

Cited by 6 publications

(8 citation statements)

References 77 publications

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“…The combination of the two weights results in a total cost that tends to decrease. This result proved research from Moghaddam [6]. Fig.…”

Section: Resultssupporting

confidence: 84%

“…A match function is a type of objective function that quantifies the optimality of the solution. Matching functions are used for weighting between functions of total costs (total costs) and functions of reliability (reliability) by Moghaddam [6]. Weighting is based on the needs of the company.…”

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

“…It is used to optimize single component preventive maintenance schedules. Moghaddam [6] compared SA with other algorithms such as Genetic (GA) Algorithm and a Steady State Genetic Algorithm (SSGA). Allaoui and Artiba [7] used a combination simulation model and machine scheduling optimization with more than one objective function.…”

Section: Introductionmentioning

confidence: 99%

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The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

Franty

Handoko

2019

JTI

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This study aims to determine the machine maintenance schedule. We use the Simulated Annealing Algorithm. Fitness and reliability functions are functions that are used in the optimization process. Several weighting scenarios are done to see the unity of the function. The results of the scenario produce several alternative schedules. This algorithm is implemented on machines that have more than one sub-machine. This sub-machine is a smaller engine system part. This sub-machine also has one particular function. The results of the study show that the optimal engine maintenance period to use is six periods. There are five scheduling scenarios used in this problem. The resulting schedule can increase the value of reliability and can minimize costs.

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“…The combination of the two weights results in a total cost that tends to decrease. This result proved research from Moghaddam [6]. Fig.…”

Section: Resultssupporting

confidence: 84%

Section: Simulated Annealing Algorithmmentioning

confidence: 99%

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The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

Franty

Handoko

2019

JTI

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“…Hilber (2008) introduced an optimization model that minimized the life cycle cost and optimized the maintenance performance through a multi-objective problem, and as a conclusion he provided a number of heuristic solutions through an optimization algorithm. Moghaddam (2010) presented a multi objectives optimization models that were developed based on a set of economical and engineering considerations.…”

Section: Previous Studies Reviewmentioning

confidence: 99%

An Optimization Approach to the Preventive Maintenance Planning Process

Altehmazi¹,

Suliman²,

Al-Alawi³

2017

MAS

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Creating a good preventive maintenance schedule is essential to perform an efficient shutdown. This paper is presenting a mathematical non-linear model that is formulated for the turnaround maintenance scheduling problem, and proposing an algorithmic optimization approach that combines the scheduling and workforce allocation in one phase. The strategy used here mainly aims to filter the uncompleted tasks from the tasks set and then to filter again from the resulted uncompleted tasks the ones which are satisfying the precedence constraint. If a task is not completed because of its preceding task, then it is put under hold until the precedence is finished. Once these two conditions are satisfied, the allocation of processors (workers in departments) starts considering the available ones. The algorithmic optimization approach is based on customized objective function and a number of constraints. It is coded in MATLAB format and solved using a modified genetic solver. It provides an optimized or pseudo-optimized schedule and workforce allocation plan, saves time and effort, and as a consequence it improves the efficiency and effectiveness of the maintenance system. The efficiency of the proposed algorithm in terms of computation time is affected mostly by the number of assigned tasks and the branching density of the dependent tasks.

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“…Adjustment activities could change the aging characteristics of components, and if done correctly, ultimately decreases the rate of failure, while after replacement, the components would be as good as new. A preventive maintenance scheduling problem generally tries to find the best sequence of maintenance actions for each component in each period over a planning horizon such that overall cost is minimized subject to a constraint on a reliability, or the reliability of the system is maximized subject to a constraint on budget (Moghaddam, 2008;Sheikhalishahi, Ebrahimipour, & Farahani, 2013;Sheikhalishahi, Ebrahimipour, Shiri, Zaman, & Jeihoonian, 2013).…”

Section: Introductionmentioning

confidence: 99%

Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error

et al. 2016

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An analytical multiobjective maintenance planning model that maximizes reliability while minimizing cost and human error is proposed. In order to incorporate human error, the model minimizes the maximum human error over the planning horizon. Human Error Assessment and Reduction Technique (HEART) is used to quantify the human error. Maintenance activities include adjustment and replacement activities, in which each of them consumes a certain amount of human resource, spare parts, and budget and brings about a specified level of reliability and human error. Economic dependence is also considered, in which grouping maintenance activities reduces total cost. However, this may increase human error probability due to operator fatigue or time pressure. The main purpose is to investigate the relationship between human factors and maintenance activities to find the preferred maintenance plan. A multiple production line is considered as a case study. A sensitivity analysis is performed, and the effects of grouping and human factors on the preferred maintenance plan are discussed. It is shown how human proficiency may affect reliability and cost.

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Preventive maintenance and replacement scheduling : models and algorithms.

Cited by 6 publications

References 77 publications

The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

The Determination of Preventive Maintenance using Simulated Annealing Algorithm based on Weighted Fitness Function

An Optimization Approach to the Preventive Maintenance Planning Process

Maintenance Scheduling Optimization in a Multiple Production Line Considering Human Error

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