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Analysis of maintenance cost for an asset using the genetic algorithm

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Abstract

Nowadays, almost every firm focuses to beat the global competition across the worldwide. In order to deal with such situation, companies are undertaking efforts to improve the productivity of their products but at the minimum possible cost. Asset management is one of the ways to enhance the productivity under cost constraint which may also be seen as the management strategy for different the phases of asset life cycle. Operations and maintenance is one of the important phases of asset life cycle that can be focussed to improve the productivity. This phase may extend the equipment life, improves availability and retains them in healthy positions. But at the same time, frequent maintenance actions may increase the maintenance cost thereby increase the life cycle cost of a product. The maintenance cost only includes the preventive and corrective maintenance cost and which may in-turn depend upon the scheduled maintenance interval. Thus, a trade-off between maintenance actions and operational objectives (i.e. availability, etc.) is required to minimize the maintenance cost. In this paper, the genetic algorithm is applied to optimize the maintenance cost for higher performance (i.e. availability). A case study is taken into consideration for implementing the GA to optimize the objective function. The three different cases are presented, in the first case, subassemblies are repaired during maintenance action(s); in the second case subassemblies are repaired in preventive maintenance action and while replaced in corrective maintenance action; in the last case, the subassemblies are replaced in both kind of maintenance. In order to check the robustness of the solution, the sensitivity analysis is also performs and that validates the strength of the solution methodology.

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Abbreviations

A:

Operational availability

AM:

Asset management

CCi :

Cost of ith subassembly within the system

Cfix :

Fixed cost to perform each corrective or preventive action, which includes the cost of material required like lubricating oil, etc.

E[CCA]:

Expected cost of corrective action during any investigation period for a system

E[CPA]:

Expected cost of preventive action during any investigation period for a system

E[NCAi]:

Expected number of corrective action of ith subassembly in the system

E[NPAi]:

Expected number of preventive action of ith subassembly in the system

E[M cost ]:

Expected total maintenance cost

E[TDT]:

Expected downtime for which the system is unavailable

GA:

Genetic algorithm

INR:

Indian national rupees

L:

Operating life of the system in years

Lc :

Labour cost per unit time

LPCA :

Lost of average sales of product per unit time, during the uneven breakdown or failures

LPPA :

Lost of average sales of product per unit time due to preventive actions

MTTCAi :

Mean time to perform the corrective action on ith subassembly of the system

MTTPAi :

Mean time to perform the preventive action on ith subassembly of the system

N(t/V):

Conditional number of failure

P:

Profit generated per unit of product sold

r:

Restoration factor

RL :

Revenue lost due to maintenance actions

T:

Total time

Topr :

Actual operating time

tpmi :

Preventive maintenance interval for ith subassembly of the system

Vn :

Virtual age of the unit at the time of the n-th repair completion

V(n − 1)i :

Virtual age of the component before carrying out a particular maintenance action

β:

Shape factor, defines the shape of distribution in Weibull

η:

Scale parameter, defines where the bulk of the distribution lies in Weibull

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Authors and Affiliations

  1. Department of Mechanical Engineering, Jamia Millia Islamia, New Delhi, 110025, India

    Mohammad Asjad & Shahbaz Khan

Authors
  1. Mohammad Asjad
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  2. Shahbaz Khan
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Correspondence to Mohammad Asjad.

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Asjad, M., Khan, S. Analysis of maintenance cost for an asset using the genetic algorithm. Int J Syst Assur Eng Manag 8, 445–457 (2017). https://doi.org/10.1007/s13198-016-0448-9

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  • DOI: https://doi.org/10.1007/s13198-016-0448-9

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