Optimising the Preventive Maintenance Interval Using a Semi-Markov Process, Z-Transform, and Finite Planning Horizon

2022 ◽  
pp. 137-161
Author(s):  
Antonio Sánchez-Herguedas ◽  
Adolfo Crespo-Márquez ◽  
Francisco Rodrigo-Muñoz
Keyword(s):  
Markov Process ◽  
Preventive Maintenance ◽  
Decision Support Tool ◽  
Planning Horizon ◽  
Good Decision ◽  
Support Tool ◽  
Semi Markov Process ◽  
Maintenance Policies ◽  
Time Planning ◽  
Finite Planning Horizon

This chapter uses a semi-Markov process and the z transform to find the optimal preventive maintenance interval when dealing with maintenance decision making for a finite time planning horizon. The result is a method that can be easily implemented to assets for which a Weibull reliability analysis exists. The suggested preventive interval formulation is simple and practical. The requirements to apply this simple formula are related to the existence of asset´s reliability data as well as cost/rewards that the assets have when remaining or transitioning to a given state. The application of this method can be very straightforward, and the tool can become a good decision support tool allowing “what if” analysis for different time horizon and maintenance policies.

Optimizing preventive maintenance over a finite planning horizon in a semi-Markov framework

2020 ◽  
Author(s):  
Antonio Sánchez Herguedas ◽  
Adolfo Crespo Márquez ◽  
Francisco Rodrigo Muñoz
Keyword(s):  
Preventive Maintenance ◽  
Recurrence Relations ◽  
Direct Calculation ◽  
Planning Horizon ◽  
Practical Implementation ◽  
Practical Application ◽  
Mathematical Solution ◽  
Accumulated Reward ◽  
Finite Planning Horizon

Abstract This paper describes the optimization of preventive maintenance (PM) over a finite planning horizon in a semi-Markov framework. In this framework, the asset may be operating, and providing income for the asset owner, or not operating and undergoing PM, or not operating and undergoing corrective maintenance following failure. PM is triggered when the asset has been operating for τ time units. A number m of transitions specifies the finite horizon. This system is described with a set of recurrence relations, and their z-transform is used to determine the value of τ that maximizes the average accumulated reward over the horizon. We study under what conditions a solution can be found, and for those specific cases the solution τ* is calculated. Despite the complexity of the mathematical solution, the result obtained allows the analyst to provide a quick and easy-to-use tool for practical application in many real-world cases. To demonstrate this, the method has been implemented for a case study, and its accuracy and practical implementation were tested using Monte Carlo simulation and direct calculation.

scholarly journals The Optimal Warranty and Preventive Maintenance Policy for the Four-State System

2011 ◽  
Vol 2011 ◽  
pp. 1-12 ◽  
Author(s):  
Ahmed F. Attia ◽  
Eman D. Abou Elela ◽  
Hany A. Hosham
Keyword(s):  
Steady State ◽  
Markov Process ◽  
Preventive Maintenance ◽  
State System ◽  
Maintenance Policy ◽  
Failure Times ◽  
Multistate System ◽  
Maintenance Policies ◽  
The Cost ◽  
Preventive Maintenance Policy

A complete view for the multistate system considering the four-state system is here introduced. The exponential distribution for failure times and repair times is considered. The steady state availability is established via the Markov process. Different warranty and preventive maintenance policies are introduced, and also the cost of these policies for the manufacturer and the buyer is evaluated.

scholarly journals Intelligent system for recruitment decision making using an alternative parallel-sequential genetic algorithm

2021 ◽  
Vol 22 (1) ◽  
pp. 385
Author(s):  
Said Tkatek ◽  
Saadia Bahti ◽  
Otman Abdoun ◽  
Jaafar Abouchabaka
Keyword(s):  
Genetic Algorithm ◽  
Decision Making ◽  
Decision Support ◽  
Intelligent System ◽  
Decision Support Tool ◽  
Good Decision ◽  
Support Tool ◽  
Hr Managers ◽  
The Right ◽  
Recruitment Model

<p>The human resources (HR) manager needs effective tools to be able to move away from traditional recruitment processes to make the good decision to select the good candidates for the good posts. To do this, we deliver an intelligent recruitment decision-making method for HR, incorporating a recruitment model based on the multipack model known as the NP-hard model. The system, which is a decision support tool, often integrates a genetic approach that operates alternately in parallel and sequentially. This approach will provide the best recruiting solution to allow HR managers to make the right decision to ensure the best possible compatibility with the desired objectives. Operationally, this system can also predict the altered choice of parallel genetic algorithm (PGA) or sequential genetic algorithm (SeqGA) depending on the size of the instance and constraints of the recruiting posts to produce the quality solution in a reduced CPU time for recruiting decision-making. The results obtained in various tests confirm the performance of this intelligent system which can be used as a decision support tool for intelligently optimized recruitment.</p>

scholarly journals A study of professional mobility in a large corporation through cognitive mapping

2010 ◽  
Vol 30 (2) ◽  
pp. 331-344 ◽  
Author(s):  
Luiz Flávio Autran Monteiro Gomes ◽  
Luís Alberto Duncan Rangel ◽  
Rogério Lúcio Jerônimo
Keyword(s):  
Decision Making ◽  
Cognitive Mapping ◽  
Decision Support Tool ◽  
Professional Lives ◽  
Problem Structuring ◽  
Good Decision ◽  
Support Tool ◽  
Professional Mobility ◽  
Mobility Problem ◽  
Principal Factors

Decisions which are made by executives in large corporations regarding professional mobility cause changes to both their personal and professional lives. This research was carried out with the aim of creating the structuring of a professional mobility problem through the use of a decision support tool, the cognitive mapping. Through the use of this tool, a decision making structure for professional mobility was developed, taking into consideration some important aspects of this process. The cognitive mapping proposed here was a problem structuring tool which leads decision makers to a greater understanding of the problem, giving them support towards good decision making in professional mobility. Through the research carried out it was possible to identify the principal factors which lead these professionals to a professional mobility decision which is as coherent and consistent as possible with the subjective aspects of their professional reality.

scholarly journals Application of system analysis and preventive maintenance on cigarette packer line

2021 ◽  
Vol 12 (2) ◽  
pp. 439-449
Author(s):  
Mohamed fathi Karoui ◽  
Mohamed Najeh Lakhoua
Keyword(s):  
Preventive Maintenance ◽  
System Analysis ◽  
Decision Support Tool ◽  
Support Tool ◽  
Industrial Manufacturing ◽  
Repair Costs ◽  
Equipment Availability ◽  
Optimal Maintenance ◽  
Production Group

The use of system analysis and preventive maintenance in today’s industry becomes a necessity as it increases equipment availability. These methods reduce the frequency of failures. The objective of this article is to present a case study for improving the reliability and availability of a production group in an industrial enterprise. Next, we present an improvement in maintenance. Finally, we present and discuss the application of preventive maintenance. Through an industrial manufacturing model, we will combine three tools; SADT modelling, the FMECA analysis and the Pareto diagram to arrive at an optimal maintenance approach that will be a decision support tool in order to minimize the repair costs and the downtime of the system. 

scholarly journals Searching for Gas Turbine Maintenance Schedules

AI Magazine ◽  
2010 ◽  
Vol 31 (1) ◽  
pp. 21 ◽  
Author(s):  
Markus Bohlin ◽  
Kivanc Doganay ◽  
Per Kreuger ◽  
Rebecca Steinert ◽  
Mathias Warja
Keyword(s):  
Integer Programming ◽  
Preventive Maintenance ◽  
Gas Turbines ◽  
Software Tool ◽  
Decision Support Tool ◽  
Production Costs ◽  
Programming Approach ◽  
Test Results ◽  
Support Tool ◽  
Maintenance Schedules

Preventive maintenance schedules occurring in industry are often suboptimal with regard to maintenance coal-location, loss-of-production costs and availability. We describe the implementation and deployment of a software decision support tool for the maintenance planning of gas turbines, with the goal of reducing the direct maintenance costs and the often costly production losses during maintenance downtime. The optimization problem is formally defined, and we argue that the feasibility version is NP-complete. We outline a heuristic algorithm that can quickly solve the problem for practical purposes and validate the approach on a real-world scenario based on an oil production facility. We also compare the performance of our algorithm with results from using integer programming, and discuss the deployment of the application. The experimental results indicate that downtime reductions up to 65% can be achieved, compared to traditional preventive maintenance. In addition, the use of our tool is expected to improve availability with up to 1% and reduce the number of planned maintenance days by 12%. Compared to a integer programming approach, our algorithm is not optimal, but is much faster and produces results which are useful in practice. Our test results and SIT AB’s estimates based< on operational use both indicate that significant savings can be achieved by using our software tool, compared to maintenance plans with fixed intervals.

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