A New Multiobjective Time-Cost Trade-Off for Scheduling Maintenance Problem in a Series-Parallel System

Leyla Sadat Tavassoli,Hong-Xia Chen,Guang-Jun Jiang,Arsalan Montazeri,Reza Massah,Mirpouya Mirmozaffari,Wenyu Zhang

doi:10.1155/2021/5583125

Abstract

In this paper, a modified model of Nondominated Sorting Genetic Algorithm 2 (NSGA-II), which is one of the Multiobjective Evolutionary Algorithms, is proposed. This algorithm is a new model designed to make a trade-off between minimizing the cost of preventive maintenance (PM) and minimizing the time taken to perform this maintenance for a series-parallel system. In this model, the limitations of labor and equipment of the maintenance team and the effects of maintenance issues on manufacturing problems are also considered. In the mathematical model, finding the appropriate objective functions for the maintenance scheduling problem requires all maintenance costs and failure rates to be integrated. Additionally, the effects of production interruption during preventive maintenance are added to objective functions. Furthermore, to make a better performance compared with a regular NSGA-II algorithm, we proposed a modified algorithm with a repository to keep more unacceptable solutions. These solutions can be modified and changed with the proposed mutation algorithm to acceptable solutions. In this algorithm, modified operators, such as simulated binary crossover and polynomial mutation, will improve the algorithm to generate convergence and uniformly distributed solutions with more diverse solutions. Finally, by comparing the experimental solutions with the solutions of two Strength Pareto Evolutionary Algorithm 2 (SPEA2) and regular NSGA-II, MNSGA-II generates more efficient and uniform solutions than the other two algorithms.

Highlights

In today’s industrial world, it is crucial for manufacturing companies to keep the production rates of machines constant
We initialized the algorithm by randomly generating a population of 100 solutions. e number of the initial population and the solutions provided by mutation and crossover operators, repository capacity, and the maximum number of unacceptable solutions sent from repository to operator mutation type 2 would be different based on their importance on the problem
We developed a model to solve the scheduling problem in preventive maintenance for seriesparallel systems

Summary

Introduction

In today’s industrial world, it is crucial for manufacturing companies to keep the production rates of machines constant. Since cost reduction and profit increase are the main goals of all manufacturing companies, the breakdown of production machines can cause a decrease in production or stop production in some cases, which will reduce the profits of companies eventually. In this situation, the need for a preventive maintenance (PM) system to keep the machines running is essential [1, 2]. Calculating the optimal time for PM actions will prevent the unexpected breakdown of machines and save cost as too many maintenance operations could potentially increase the cost of production [3]. Since the maintenance team is unable to perform more than one maintenance at a time, the maintenance schedule should be changed so that no more than one maintenance is scheduled at a time [5]

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Conclusion