An approach to design of maintenance float systems

Abstract

The paper offers an intelligent approach to analyze and determine the design parameters minimizing the total cost and achieving the desired performance measures in the maintenance float systems. The expected total cost in a maintenance float system includes the cost of lost production, the cost of repair persons and the cost of standby machines. The developed design procedure integrates simulation, metamodel and genetic algorithms. Neural networks are able to approximate functions based on a set of sample data, i.e. construct metamodels from simulation results in this study. The objective of metamodels is to predict simulation responses in order to significantly reduce the amount of simulation runs. The predictive performance of neural metamodels comparably outperforms the traditional regression metamodels. The neural metamodels are further extended to formulate a decision model for optimizing the maintenance float systems by using genetic algorithms.

Anticipating Future Behavior of an Industrial Press Using LSTM Networks

“…This makes maintenance one of the strategic points for the development and growth of competitiveness vis-à-vis competitors. Chen and Tseng studied the total expected cost of maintaining a flotation system, including the cost of lost production, the cost of repairs, and the cost of standby machines [11]. Daniyan et al propose the integration of Artificial Intelligence (AI) systems, which will bring many benefits in diagnosing condition problems of industrial machines [12].…”

Maintenance Decision Optimization

“…Two key problems with the maintenance float system are (a) system design to determine the key parameters such as the number of standby items and the number of maintenance servers (e.g., see Ref. [1]); and (b) performance evaluation for a given system configuration (e.g., see Ref. [7]).…”

Availability of Large Scale Repairable Systems with Imperfect Repair

“…Knowledge management was utilized in setting up the system and to store component parameters during the design process. Chen & Tseng (2008) introduced an approach to determine the most cost effective parameters that increase the system's performance. The design procured an integrated genetic algorithm, simulation, and neural networks.…”