An Optimization Approach to the Preventive Maintenance Planning Process

Manar Altehmazi, Saad Suliman and Yaser Alalawi

Modern Applied Science, 2017, vol. 11, issue 9, 20

Abstract: 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.

Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://ccsenet.org/journal/index.php/mas/article/download/69421/38052 (application/pdf)
https://ccsenet.org/journal/index.php/mas/article/view/69421 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ibn:masjnl:v:11:y:2017:i:9:p:20

Access Statistics for this article

More articles in Modern Applied Science from Canadian Center of Science and Education Contact information at EDIRC.
Bibliographic data for series maintained by Canadian Center of Science and Education ().