Joint Scheduling of a Multi-Objective Stochastic Complex Production Problem Via an Intelligent Real-Time Predictive Maintenance Model

Condition-based maintenance (CBM) is a new generation of maintenance methodologies that help production system managers follow the real-time state of their system and enhance its reliability.CBM determines the right time and type of maintenance activities.However, it has less portion of the production research area.This research tries to fill a part of this gap by developing a joint production and maintenance model.This model introduces a multi-objective real-time version of a flexible job shop scheduling problem (FJSP) that manages maintenance affairs through the CBM mechanism.The proposed integrated model includes various stochastic items, such as maintenance activation times, maintenance durations, number of different types of maintenance, and real-time shocking process to get close to a real-world environment.The proposed stochastic objective functions are complementation time (Cmax), maintenance costs (MC), and the average system degradation level (ASDL). To solve the developed NP-Hard model, two multi-objective simulation-based optimizations (MSBO) algorithms, called nondominated sorting genetic algorithm (NSGAII) and multi-objective evolutionary algorithm based on decomposition (MOEAD) are developed. The visualized shocking process alongside other graphs completes the introduction of the behavior of the real-time model and the proposing solving algorithms to the considered research area.