Open Access

ARTICLE

A Mathematical Optimization Model for Maintenance Planning of School Buildings

Mehdi Zandiyehvakili¹, Babak Aminnejad^2,*, Alireza Lork³

1 Department of Civil Engineering, Kish International Branch, Islamic Azad University, Kish Island, Iran
2 Department of Civil Engineering, Roudehen Branch, Islamic Azad University, Roudehen, Iran
3 Department of Civil Engineering, Safadasht Branch, Islamic Azad University, Safadasht, Iran

* Corresponding Author: Babak Aminnejad. Email:

(This article belongs to the Special Issue: Soft Computing and Machine Learning in Industrial Systems)

Intelligent Automation & Soft Computing 2022, 32(1), 499-512. https://doi.org/10.32604/iasc.2022.021461

Received 03 July 2021; Accepted 07 August 2021; Issue published 26 October 2021

Abstract

This article presents a methodology to optimize the maintenance planning model and minimize the total maintenance costs of a typical school building. It makes an effort to provide a maintenance schedule, focusing on maintenance costs. In the allocation of operations to the school equipment, the parameter of its age was also taken into account. A mathematical optimization model to minimize the school maintenance cost in a three-year period was provided in the GAMS software with CPLEX solver. Finally, the optimum architecture of the Perceptron multi-layer neural network was used to predict the schedule of equipment operations and maintenance costs. The Multi-layer Perceptron (MLP) optimum neural network results, with minor Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), indicated that the proposed model was capable of predicting the schools’ maintenance costs with high accuracy. According to the results, the school's maintenance cost for the intended three-year period based on the Weibull distribution was equal to 15361 currency units per hour, in which the “heating and cooling system” has the highest contribution. Hence, accurate and definite planning can prevent damages to such equipment, while saving the school's maintenance costs.

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Keywords

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