A PSO approach for preventive maintenance scheduling optimization

This work presents a Particle Swarm Optimization (PSO) approach for preventive maintenance policy optimization, focused in reliability and cost. The probabilistic model for reliability and cost evaluation is developed in such a way that flexible intervals between maintenance are allowed. As PSO is skilled for realcoded continuous spaces, a non-conventional codification has been developed in order to allow PSO to solve scheduling problems (which is discrete) with variable number of maintenance interventions. In order to evaluate the proposed methodology, the High Pressure Injection System (HPIS) of a typical 4-loop PWR has been considered. Results demonstrate ability in finding optimal solutions, for which expert knowledge had to be automatically discovered by PSO

Quantifying Preventive Maintenance Efficacy: A Baltimore City Use Case

Existing preventive maintenance efficacy research heavily focuses on quantifying system degradation in deterministic, probabilistic, and policy-based models, yet in a data centric age they inadequately address data requirements, the linchpin for improving preventive maintenance and graduating to predictive maintenance. Using Baltimore City’s public facility maintenance work orders, this study demonstrates the impact of data requirements on frequency, time and cost key performance indicators (KPI) and addresses omitted variable bias introduced by lack of condition-based data. Overall results show that maintenance cost has annually increased by $6,520 despite a sharp drop in 2018. Facilities in poor condition with persistently high repair needs present an opportunity for Baltimore to tailor its preventive maintenance strategy using condition-based data and separating money for a narrower definition of functional maintenance, potentially making better use of up to$14 million. Data requirements such as tracking corrective and preventive maintenance work for the same system and parts, combining facilities condition index (FCI) scores with work order frequency, prioritizing which facilities get preventive maintenance using return on investment thresholds, and enforcing data quality discipline compose the road map to these insights

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), using some randomly generated instances.genetic algorithm;heuristics;opportunities;maintenance optimization;memetic algorithm

Maintenance grouping for multi-component systems with availability constraints and limited maintenance teams

International audienceThe paper deals with a maintenance grouping approach for multi-component systems whose components are connected in series. The considered systems are required to serve a sequence of missions with limited breaks/stoppage durations while maintenance teams (repairmen) are limited and may vary over time. The optimization of the maintenance grouping decision for such multi-component systems leads to a NP-complete problem. The aim of the paper is to propose and to optimize a dynamic maintenance decision rule on a rolling horizon. The heuristic optimization scheme for the maintenance decision is developed by implementing two optimization algorithms (genetic algorithm and MULTIFIT) to find an optimal maintenance planning under both availability and limited repairmen constraints. Thanks to the proposed maintenance approach, impacts of availability constraints or/and limited maintenance teams on the maintenance planning and grouping are highlighted. In addition, the proposed grouping approach allows also updating online the maintenance planning in dynamic contexts such as the change of required availability level and/or the change of repairmen over time. A numerical example of a 20-component system is introduced to illustrate the use and the advantages of the proposed approach in the maintenance optimization framework

Determinação de cronograma de manutenção preventiva utilizando algoritmo genético

Este artigo propõe um método para otimização do cronograma de manutenção preventiva com vistas à minimização dos custos associados. Dados de falhas são coletados e modelados através de distribuições de probabilidades paramétricas. Na sequência, geram-se índices de melhoria associados aos incrementos de confiabilidade decorrentes das manutenções, valendo-se do conhecimento de especialistas de processo, conforme Tsai et al. (2001). Tais índices são integrados a formulações quantificadoras do custo incorrido pelos procedimentos de manutenção. A formulação é otimizada através de Algoritmo Genético (AG), determinando o melhor tipo de manutenção (por exemplo, manutenção total ou parcial) a ser realizada em intervalos pré-definidos. Ao ser aplicado em uma máquina de transformação de bobinas de aço plano em tubos, o método gerou um cronograma coerente de manutenções com base na avaliação de especialistas de processo, além de reduzir em 20% os custos comparados à programação empírica de manutenção. Os resultados ainda comprovam a eficiência de AGs na resolução de problemas de manutenção por conta de sua rápida convergência e fácil implementação

Developing Predictive Models for Fuel Consumption and Maintenance Cost Using Equipment Fleet Data

The state departments of transportation (DOTs) possess a big construction equipment fleet that is engaged in various highway maintenance and repair activities all over the state. Fleet managers are called upon to give the budget estimates required to keep the equipment functioning throughout the year. These decisions are not easy to make as DOTs manage a big equipment fleet from pickup cabs to big size motor graders etc. This decision-making process could be improved by employing DATA MINING techniques on the equipment management data available with the DOTs. This study utilized the construction equipment data provided by the Oklahoma Department of Transportation (ODOT) and applied Multiple Linear Regression (MLR) to develop predictive models for fuel consumption and maintenance cost. The dataset was divided into two parts based on the operational charge type, i.e. equipment charged for operation by dollar/hour and equipment charged for operation by dollar/mile. In a total of the data from 2000 pieces of equipment was analyzed in this research. Four best models were selected based on the smallest average squared error (ASE) value. Apart from operational data, the model development utilized information such as equipment purchase price, age, and specified useful life of the equipment. Fuel consumption could be predicted based on yearly hours worked by the equipment or yearly miles driven. Other input variables used are current odometer value of the equipment, fuel consumption in gallons, age of the equipment, purchase price of the equipment and class code id. Maintenance cost model development used cumulative work hours and cumulative miles driven recorded in the span of the year 2011 to 2017 by Oklahoma DOT. Other input variables used are the age of the equipment, the purchase price of the equipment, current odometer value, the useful life of the equipment assigned by the manufacturer and class code ID. The coefficients of variables obtained from the MLR test are explained to see the impact on fuel consumption and maintenance cost. Finally, the model was validated by utilizing the 30% validation data and yielded good prediction results. The predictive models will help state DOTs better budget equipment operational budget as well as facilitate the equipment rental rate update process. Furthermore, future recommendations are stated at the end of the last chapter so that this study could be taken forward

Genetic and memetic algorithms for scheduling railway maintenance activities

Nowadays railway companies are confronted with high infrastructure maintenance costs. Therefore good strategies are needed to carry out these maintenance activities in a most cost effective way. In this paper we solve the preventive maintenance scheduling problem (PMSP) using genetic algorithms, memetic algorithms and a two-phase heuristic based on opportunities. The aim of the PMSP is to schedule the (short) routine activities and (long) unique projects for one link in the rail network for a certain planning period such that the overall cost is minimized. To reduce costs and inconvenience for the travellers and operators, these maintenance works are clustered as much as possible in the same time period. The performance of the algorithms presented in this paper are compared with the performance of the methods from an earlier work, Budai et al. (2006), usin

A survey of AI in operations management from 2005 to 2009

Purpose: the use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence this paper presents a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the ten-year period 1995-2004. Like the previous survey, it uses Elsevier’s Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case-based reasoning (CBR), fuzzy logic (FL), knowledge-Based systems (KBS), data mining, and hybrid AI in the four application areas are identified. Findings: the survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Design/methodology/approach: the paper builds upon our previous survey of this field which was carried out for the 10 year period 1995 to 2004 (Kobbacy et al. 2007). Like the previous survey, it uses the Elsevier’s ScienceDirect database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus the application categories adopted are: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Research on utilising neural networks, case based reasoning, fuzzy logic, knowledge based systems, data mining, and hybrid AI in the four application areas are identified. Findings: The survey categorises over 1400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: (a) The trends for Design and Scheduling show a dramatic increase in the use of GAs since 2003-04 that reflect recognition of their success in these areas, (b) A significant decline in research on use of KBS, reflecting their transition into practice, (c) an increasing trend in the use of fuzzy logic in Quality, Maintenance and Fault Diagnosis, (d) surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research. Originality/value: This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research