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Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization

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Abstract

Equipment maintenance is momentous for improving production efficiency, how to integrate maintenance into production to address uncertain problems has attracted considerable attention. This paper addresses a novel approach for integrating preventive maintenance (PM) into production planning of a complex manufacturing system based on availability and cost. The proposed approach relies on two phases: firstly, this study predicts required capacity of each machine through extreme learning machine algorithm. Based on analyzing historical data, the opportunistic periods are calculated for implementing PM tasks to have less impact on production and obtain the PM interval and duration. Secondly, this study obtains the scheduling planning and the least number of maintenance personnel through an improved ant colony optimization algorithm. Finally, the feasibility and benefits of this approach are investigated based on empirical study by using historical data from real manufacturing execution system and equipment maintenance system. Experimental results demonstrate the effectiveness of proposed approach, reduce personnel number while guarantee the maintenance tasks. Therefore, the proposed approach is beneficial to improve the company’s production efficiency.

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Abbreviations

\(\beta \) :

weight matrix of ELM output layer

B :

bias matrix of ELM hidden layer

C :

the adjusted parameter of ELM

\(g(\cdot )\) :

activation function

I :

identity matrix

L :

number of ELM hidden layer nodes

\(L_{lop}\) :

last overlap list

\(L_{op}\) :

overlap list

m :

number of ELM input features

N :

number of ELM training samples

n :

number of ELM output caregories

\(N_p\) :

the number of PM tasks

\(N_{ant}\) :

the number of ants in ACO algorithm

\(S_d\) :

total distance

T :

output matrix of ELM

\(T_c\) :

sum of \(T_{prc_i}, 1\le i\le 12\) in the past for each machine

\(T_{cc}\) :

CM cycle time

\(T_{cds_i}\) :

hourly CM duration of day shift

\(T_{cds}\) :

CM duration of day shift

\(T_{cd}\) :

CM duration of machine

\(T_{maxc}\) :

maximum sum of \(T_{prc_i}, 1\le i\le 12\) in the past for each machine

\(T_{maxpd}\) :

maximum PM duration

\(T_{minc}\) :

minimum sum of \(T_{prc_i}, 1\le i\le 12\) in the past for each machine

\(T_{minpd}\) :

minimum PM duration

\(T_{op_i}\) :

hourly opportunity time

\(T_{p_i}\) :

PM duration of one PM type

\(T_{pc}\) :

PM cycle time

\(T_{prc_i}\) :

hourly predicted required capacity

\(Tb_d\) :

distance table

\(Tb_p\) :

path table

X :

input feature matrix of ELM training samples

W :

weight matrix of ELM hidden layer

ACO:

ant colony optimization

CM:

corrective maintenance

ELM:

extreme learning machine

EMS:

equipment maintenance system

MES:

manufacturing execution system

MSE:

mean square error

PCA:

principal component analysis

PdM:

predictive maintenance

PM:

preventative maintenance

TSP:

traveling salesman problem

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Acknowledgements

This research was supported by National Key R&D Program of China, No. 2018YFE0105000, the National Natural Science Foundation of China under Grant No. 51475334, the Shanghai Municipal Commission of science and technology No. 19511132100 and the Fundamental Research Funds for the Central Universities of China under Grant No. 22120170077.

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Authors and Affiliations

  1. College of Electronics and Information Engineering, Tongji University, Shanghai, 201804, China

    Li Li, Yong Wang & Kuo-Yi Lin

  2. Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai, 201804, China

    Li Li & Kuo-Yi Lin

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  1. Li Li
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Li, L., Wang, Y. & Lin, KY. Preventive maintenance scheduling optimization based on opportunistic production-maintenance synchronization. J Intell Manuf 32, 545–558 (2021). https://doi.org/10.1007/s10845-020-01588-9

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