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https://ceur-ws.org/Vol-2540/FAIR2019_paper_16.pdf
Preventive maintenance scheduling and
replacement using parallel evolutionary
algorithms
Anthony O. Ikechukwu1 , Shawulu H. Nggada2 , and José Ghislain Quenum1
1
Namibia University of Science and Technology, Windhoek, Namibia
ikechukwu anthony@yahoo.com, jquenum@nust.na,
2
Higher Colleges of Technologies, Ras Al Khaimah, United Arab Emirates
shnggada@gmail
Abstract. The dependability of safety-critical systems prescribes the
replace policy when the reliability of any of its components drops below
a predefined unacceptable level. Various techniques have been used in the
past to draw up the perfect preventive maintenance schedule based on
the meantime to failure (MTTF). This paper presents a review of parallel
evolutionary algorithms to accomplish this task.
Keywords: Dependability · Maintenance Scheduling · Component re-
placement · Island Model · Strength Pareto Evolutionary Algorithm 2
(SPEA2) · Optimisation · Reliability and Availability · Safety Critical
Systems.
1 Introduction
With modern safety-critical systems displaying multiple and complex failure
modes, orthodox manual analysis of systems turn out to be more and more com-
plicated and error-prone. Even rule-based and traditional safety and reliability
analysis techniques are outdated. Preventive maintenance (PM), the introduc-
tion of scheduled maintenance for all or critical components of the system, is
introduced to address these limitations. Preventive maintenance is naturally ex-
pressed as a combinatorial optimisation problem [4, 2].
Typically, an appropriate preventive maintenance schedule is meant to elon-
gate the useful life of components [5] through repairs based on the meantime
between failures (MTBF). However, at a certain point, these components will
become unmaintainable. In this work, we extend the preventive maintenance
with a replacement policy according to the meantime to failure (MTTF). In
this paper, we present a review of parallel evolutionary algorithms for perfect
preventive maintenance based on a replacement policy.
2 Parallel Evolutionary Algorithms
Evolutionary algorithms (EA) represent a family of algorithms used in artificial
intelligence (AI) where the problem space is searched by focusing on the fittest
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0)
2 Ikechukwu et al
individuals over several generations. An EA starts with an initial population,
which it evaluates to select the fittest. After that, the algorithm iterates through
the selection of the fittest, crossover and mutation until the termination criteria
(an expected fitness or the maximum number of iterations has been reached) are
met.
Because EAs are generally computationally costly, previous research efforts
have developed parallel versions of EAs (parallel evolutionary algorithms, PEAs),
using a single population or multiple sub-populations. These parallelisation ef-
forts prove useful while executing EAs on massively parallel computers or super-
computers. Alba [1] discusses three variants of PEAs, including a master-slave
PEA, a fine-grained PEA and a coarse-grained PEA. A master-slave PEA uses
a single population but distributes the crossover, mutation and fitness evalua-
tion to other processors. A fine-grained PEA also uses a single population and
confines individuals within a spatial structure, where they can only interact with
their neighbours. Finally, a coarse-grained PEA or island model which divides
the population into several sub-populations. The latter then have an amount of
migration among them.
3 Evolutionary Selection Algorithm
3.1 The Non-dominated Sorting Genetic Algorithm II (N SGA II)
N SGA II [3] is a multi-objective, multi-directional optimisation algorithm. It is
an improved version of N SGA that mitigates some its drawbacks. For example,
N SGA does not have an elitist feature, which improves the performance of the
search process as it retains good solution from generation to generation. This
limitation is addressed in N SGA II. As well, parameter tuning is handled in
In N SGA II. Finally, N SGA II is a Pareto frontier algorithm; i.e., it produces
optimal solutions.
3.2 Strength Pareto Evolutionary Algorithm 2 (SP EA 2)
SP EA 2 [6] is an enhanced elitist multi-objective EA which uses an improved
fitness allotment approach different from what is available in its predecessor
SP EA. The fitness assignment approach integrates density information and the
archive. Additionally, a clustering technique, which is introduced as soon as the
non-dominated front surpasses the archive border, is substituted with another
truncation method with similar characteristics and nevertheless keeps the bound-
ary details. Furthermore, in SP EA 2 the archive members are involved in the
reproduction process.
References
1. Alba, E.: Parallel Metaheuristics: A New Class of Algorithms. Wiley-Interscience,
New York, NY, USA (2005)
Preventive Maintenance and PEAs 3
2. Budai, G., Huisman, D., Dekker, R.: Scheduling preventive railway mainte-
nance activities. Journal of the Operational Research Society 57(9), 1035–1044
(2006). https://doi.org/10.1057/palgrave.jors.2602085, https://doi.org/10.1057/
palgrave.jors.2602085
3. Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjec-
tive genetic algorithm: Nsga-ii. Trans. Evol. Comp 6(2), 182–197 (Apr 2002).
https://doi.org/10.1109/4235.996017, http://dx.doi.org/10.1109/4235.996017
4. Mahadevan, M., Poorana, K., Vinodh, R., Paul, R.: Preventive maintenance optimi-
sation of critical equipment in process plant using heuristic algorithms. In: Proceed-
ings of the 1st International Conference on Industrial Engineering and Operations
Management. pp. 647–653. Dhaka, Bangladesh (2010)
5. Nggada, S.H.: Reducing component time to dispose through gained life. Interna-
tional Journal of Advanced Science and Technology 35(4), 103–118 (2011)
6. Zitzler, E., Laumanns, M., Thiele, L.: Spea2: Improving the strength pareto evolu-
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