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Bi-objective optimization of maintenance scheduling for power systems

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Abstract

Enhance the quality of energy production in power generating stations and reducing its cost have become of paramount importance. One of the methods to reach that goal is by minimizing the maintenance scheduling time. For this purpose, a new competitive mechanism, based on a modified genetic algorithm (MGA), has been proposed to perform the preventive maintenance (PM) scheduling. Firstly, a mono-objective optimization (makespan) has implemented, and the results were quite good. Secondly, and in order to benefit from the waste time, a bi-objective optimization was developed to find a trade-off between makespan and training time of operators. Finally, the MGA-based maintenance scheduling was tested on a hybrid renewable power system (HRPS), that uses photovoltaic modules and a fuel cell (PV/FC) as sources and the telecommunication platform as load, the obtained results have proved the high efficiency of the proposed MGA-based maintenance scheduling.

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Abbreviations

i :

Index for maintenance operators.

k :

Index for skill levels.

β :

Index for operations.

n :

Number of maintenance operators.

a :

Number of tasks.

S j :

Specialties of index j.

\(T_{Lk\_Lk+1}\) :

Time needed to upgrade from level k to level k + 1.

w :

A weight given to the training duration, w < 1.

t α β :

Processing time of operation α β.

t 2,0 :

Training duration made by a trainee operator of level L0 in a session (operation). The trainer operator is of L2.

t 3,0 :

Training duration made by a trainee operator of level L0 in a session (operation). The trainer operator is of L3.

t 3,1 :

Training duration made by a trainee operator of level L1 in a session (operation). The trainer operator is of L3.

t 2,2 :

Training duration made by a trainee operator of level L2 in a session (operation). At this level, the operator can progress by Self-training.

C m a x :

The makespan of a schedule S C H . It varies according to schedule S C H

t r(S C H ):

The total time of training of all operators. It varies according to schedule SCH

D :

Derringer desirability

Y r :

A variable which needs to be optimized

\( Y_{r\_tgt}\) :

Target value of Yr

T r :

The total time of training, which operators should make to upgrade all their skills to expert level L3, and it is independent of any schedule.

FJSP :

Flexible job shop problem

j :

Index for specialties

α :

Index for tasks

m :

Number of specialties

l :

Number of skill levels

b α :

Number of operations in task

M O i :

Maintenance operator of index i

T α :

Task of index α

O P α β :

Operation of index

L k :

Skill level of a maintenance operator

C α :

The time of end of the task T α .

\(C_{max\_opt}\) :

The optimum makespan. It is independent of any schedule.

S C H :

It is an abbreviation for Schedule

C m a x :

The makespan of a schedule S C H . It varies according to schedule S C H

W t i m e :

Waste time

d r :

The individual desirability function

\(Y_{r\_sup}\) :

Maximum acceptable value of Yr

W r :

A weight given to a function dr

SM :

Skills matrix

PM :

Preventive maintenance

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Authors and Affiliations

  1. LACoSERE Laboratory, Amar Telidji University, BP 37G, Ghardaia Road, Laghouat, 03000, Algeria

    B. Hadjaissa & K. Ameur

  2. LDCCP Laboratory, Ecole Nationale Polytechnique, 10 avenue H. Badi BP 182, Harrach Algiers, Algeria

    S. M. Ait cheikh

  3. CReSTIC Laboratory, Reims University, 10026, Troyes CEDEX, France

    N. Essounbouli

Authors
  1. B. Hadjaissa
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  2. K. Ameur
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  3. S. M. Ait cheikh
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  4. N. Essounbouli
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Correspondence to B. Hadjaissa.

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Hadjaissa, B., Ameur, K., Ait cheikh, S.M. et al. Bi-objective optimization of maintenance scheduling for power systems. Int J Adv Manuf Technol 85, 1361–1372 (2016). https://doi.org/10.1007/s00170-015-8053-7

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  • DOI: https://doi.org/10.1007/s00170-015-8053-7

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