Preventive Maintenance Optimization and Comparison of Genetic Algorithm Models in a Series–Parallel Multi-State System

Abstract

In this research, different optimization models are developed to solve the preventive maintenance (PM) optimization problem in a maintainable multi-state series–parallel system. The objective is to determine for each component in the system the maintenance period minimizing a cost function under the constraint of required availability and for a specified horizon of time. Four genetic models based on the cost associated with maintenance schedule and availability characteristic parameters are constructed and analyzed. They are genetic algorithm (GA), hybridization GA and local search (GA-LS), fuzzy logic controlled GA (FLC-GA), and hybridization FLC-GA and LS. The experiment analyzes and compares the efficiency between them. These experiments investigate the effect of the parameters of the GA on the structure of optimal PM schedules in multi-state multi-component series–parallel systems. Results show that the hybridization FLC-GA and LS outperform the other algorithms.

Improved Genetic Algorithm to Solve the Scheduling Problem of College English Courses

“…For classes, two concepts need to be clear, namely, administrative classes and teaching classes [18][19][20]. Administrative classes, as the basic unit of the school, will be managed by the counsellors of each administrative class for the daily management of students; teaching classes are administrative classes in which the same curriculum needs are formed into a collective class, and the teachers of the classes are mainly responsible for the teaching and management of students.…”

Artificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications

“…defect prediction [80] failure prediction [81] defect detection [82] Y: defect prediction [83] defect detection [84], [85] Safety and security Y: train protection [86], speed error reduction [87] Y: accidents [53] disruptions [88] Autonomous driving and control Y: energy optimization [89] intelligent train control [90] Y: intelligent train control [55] Traffic planning and management Y: train timetabling [91], [92] Y: delay analysis [40], train rescheduling [93] train timetabling [63], [94], train shunting [95] Revenue management P: revenue simulation [96] P: overall revenue management [97] inventory control and prediction [98] Transport policy P: energy network policy making [99] U Passenger mobility P: demand forecasting [100] Y: flow prediction [101], [102] and reinforcement learning for optimal train control. Reference [89] proposed a method for energy optimisation of the train movement applying control based on genetic algorithms.…”

“…For example, [82] proposed a model for texture segmentation in wood manufacturing using Gabor filters to the analysis of texture and defect regions found on wooden boards. Also, possible applications are seen for using GA for preventive maintenance [80], [81]. These would lead to providing to focus on the most important characteristics while disregarding the others, and thus lead to smaller required datasets and hopefully simpler and more efficient AI models.…”

Preventive Maintenance Interval Optimization for Continuous Multistate Systems

“…i reflects the importance of each objective, k 1 i � 1, and k 2 i is used to adjust the effect of the difference in magnitude among objectives (e.g., k 2 i � 1/‖∇f i ‖ 2 ). To solve the PM interval optimization problem, three algorithms were usually applied, including genetic algorithms (GAs) [8,9,21], particle swarm optimization (PSO) [22], and simulated annealing (SA) [23]. Generally, the appropriate optimization algorithm is selected according to the type of the optimization model.…”