Total Maintenance Cost Research Articles

Multi-objective optimisation of sewer maintenance scheduling

Abstract Effective functioning of sewer systems is critical for the everyday life of people in the urban environment. This is achieved, among other things, by the means of regular, planned maintenance of these systems. A large water utility would normally have several maintenance teams (or crews) at their disposal who can perform related jobs at different locations in the company area and with different levels of priority. This paper presents a new methodology for the optimisation of related maintenance schedules resulting in clear prioritisation of the ordering of maintenance tasks for crews. The scheduling problem is formulated as a multi-objective optimisation problem with the following three objectives, namely the minimisation of the total maintenance cost, the minimisation of travel times of maintenance teams and the maximisation of the job's priority score, all over a pre-defined scheduling horizon. The optimisation problem is solved using the Nondominated Sorting Genetic Algorithm-II (NSGA-II) optimisation method. The results obtained from a real-life UK case study demonstrate that the new methodology can determine optimal, low-cost maintenance schedules in a computationally efficient manner when compared to the corresponding existing company schedules. Daily productivity of maintenance teams in terms of number of jobs completed improved by 26% when the methodology was applied to scheduling in the case study. Given this, the method has the potential to be applied within water utilities and the water utility Welsh Water (Dŵr Cymru Welsh Water (DCWW)) is currently implementing it into their systems.

Optimal Task Abort and Maintenance Policies Considering Time Redundancy

For many practical systems that are required to perform critical tasks, it is commonly observed that tasks can be performed multiple times within a limited time to improve task success probability. Such property is referred to as time redundancy. This paper contributes by studying the optimal adaptive maintenance and the task abort strategies of continuously degraded systems considering two kinds of time redundancy to improve system safety and task reliability. The task abort decision is considered dynamically according to the degradation level and the number of task attempts. Task success probability and system survival probability under two kinds of time redundancy are evaluated using an event-based numerical algorithm. The optimal imperfect maintenance and task abort thresholds are investigated dynamically in each attempt to minimize the expected total cost of maintenance, task failure and system failure. The established model in this study is illustrated by numerical results.

A continuous location and maintenance routing problem for offshore wind farms: Mathematical models and hybrid methods

In this study, we examine a challenging green logistical problem encountered with offshore wind farms: the integrated continuous location and maintenance routing problem wherein a service operation vessel and a safe transfer boat are used to maintain offshore turbines. Our aim is to dynamically and simultaneously determine the best locations for the service operation vessel in the plane (i.e. the sea) and the best delivery and pick-up routes by which the safe transfer boat can access the turbines. An optimisation model of the problem is first developed based on mixed-integer nonlinear programming to minimise the total maintenance cost. Given the limitations of this method, a novel algorithm that integrates a genetic algorithm, variable neighbourhood search, and a Weiszfeld-based algorithm is presented. To assess the performance of the proposed technique, a guided multi-start approach and a hybrid technique based on particle swarm optimisation are introduced. Moreover, our proposed method is shown to be easily adaptable to produce results that compete with those of state-of-the-art methods when it comes to solving a related continuous location routing problem. The computational results demonstrate the effectiveness and robustness of the proposed hybrid method.

Estimates of the economic damage due to the soiling of residential buildings induced by air pollution in Italy.

The soiling of the external façades of buildings caused by air pollution has economic costs that are generally not borne entirely by the polluters but by society in general. The present paper attempts to estimate the maintenance costs attributable to the soiling of the façades of residential buildings exposed to ambient air pollution in Italy. In this study, dose-response functions were used to link the environmental concentrations of pollutants to the soiling rate of the opaque and transparent surfaces of the building façades. It was assumed that the spatial distribution of façade materials follows the distribution of population. The basic assumption was that maintenance is performed when critical levels, 35% loss of reflectance for opaque surfaces and 1% haze for glass surfaces, are reached. Several important elements seem to emerge from the analysis carried out. The cost of damage to opaque surfaces due to air pollution appears to be a non-negligible fraction of the total maintenance costs of building façades incurred in Italy. Moreover, the cost of soiling damage to residential buildings from particulate matter has the potential to significantly increase total external costs due to air pollution. The costs of cleaning window panes due to air pollution also appear significant. Finally, if the levels of atmospheric pollution in the most populated areas become similar to the concentrations currently present in the most remote areas in Italy, the overall cost at national level due to the soiling of the building façades could be reduced by about 50%.

Flexible Joint Planning of Sectionalizing Switches and Tie Lines Among Distribution Feeders

This paper proposes an optimization model for the joint planning of sectionalizing switches (SSs), including remote-controlled switches (RCSs) and manual switches (MSs), and tie lines among distribution feeders. Considering a flexible installation of SSs and tie lines (i.e., deployment of SSs on both sides of a line and construction of external and internal tie lines on of a feeder), this model is conducive to developing an economic investment scheme while preserving the expected energy not supplied (EENS) reliability criterion. First, the minimum total cost of investment, maintenance and penalty for EENS is chosen as the objective function. Next, a route analysis method is introduced to analyze particular performances of different deployment schemes for RCSs, MSs, external tie lines and internal tie lines. Considering the differences between overhead lines and underground cables, the lower bound of the restoration time of each load is estimated based on optimal restoration strategies in different fault scenarios. Then, the joint planning problem is formulated as a mixed-integer linear programming (MILP) model with the aid of algebraic operations to calculate the global optimal solution. The effectiveness of the proposed model is verified for several cases in a 54-node distribution system. Numerical results indicate that the proposed planning solution merits higher economy and lower EENS as compared to the schemes offered by other prevalent methods.

Task assignment to counter the effect of developer turnover in software maintenance: A knowledge diffusion model

Developer churn is the overall turnover in a software organization’s staff. Existing developers leave and new ones join the project. Retaining the knowledge of the software source code among the development team in such scenarios is an essential factor to keep the software maintenance cost as low as possible. Knowledge diffusion is an activity that could mitigate the negative impact of developer churn, while a task assignment strategy could pay an important role to attain good knowledge diffusion among the team members and effectively lower the likelihood of knowledge loss. In this work, a self-adaptive task assignment (SATA) approach is proposed that adaptively switches between cost-oriented and diffusion-oriented strategies over subsequent rounds of task assignments. An entropy-based model is applied to estimate the current conditions of the development team from the knowledge concentration perspective. This model is assisted by a learning automata and evolutionary algorithms to offer smart assignments. The experimental results show that, particularly in teams with medium churn rates, applying an entropy-aware task assignment model can reduce the total maintenance cost up to slightly over 50%, provided that the knowledge demands in the team over successive rounds of task assignment remain stationary. There are also improvements in terms of the projects’ bus factor which prevent the project to lose its key knowledge. Even for projects where there is no saving in maintenance costs, SATA results in knowledge being more distributed among the developers, resulting in a more resilient project. SATA improves the long-term sustainability of development teams with developer turnover. Projects and their managers can hence rely on it when there is the risk of knowledge loss due to developer turnover.

Maintenance Decision Support Model for Railway Track Geometry Maintenance Planning Using Cost, Reliability, and Availability Factors: A Case Study

Determining the strategy and planning for maintenance operations on railway tracks is important given the increasing demand from both freight and passengers. An important prerequisite of maintenance planning is the ability to predict the overall and geometric conditions of the railway in the near future. It is, therefore, necessary to develop a degradation model and consider the effect of various maintenance operations using a recovery model. According to EN 13848-5, the alert limit (AL) can be used as an indicator to plan maintenance operations. The current paper benefits from this index as well as the effect of its changes on important parameters such as maintenance costs, availability, and reliability to present a maintenance decision support model. This model allows infrastructure managers to plan maintenance interventions under the conditions of their intended usage strategy. The results of this paper indicate that choosing suitable AL could affect total maintenance cost and availability by up to 57% and 33% respectively.

Data-Driven Maintenance Priority and Resilience Evaluation of Performance Loss in a Main Coolant System

The main coolant system (MCS) plays a vital role in the stability and reliability of a nuclear power plant. However, human errors and natural disasters may cause some reactor coolant system components to fail, resulting in severe consequences such as nuclear leakage. Therefore, it is crucial to perform a resilience analysis of the MCS, to effectively reduce and prevent losses. In this paper, a resilience importance measure (RIM) for performance loss is proposed to evaluate the performance of the MCS. Specifically, a loss importance measure (LIM) is first proposed to indicate the component maintenance priority of the MCS under different failure conditions. Based on the LIM, RIMs for single component failure and multiple component failures were developed to measure the recovery efficiency of the system performance. Finally, a case study was conducted to demonstrate the proposed resilience measure for system reliability. Results provide a valuable reference for increasing the system security of the MCS and choosing the appropriate total maintenance cost.

BIM–LCCA Integration for Road Pavement Maintenance

The use of building information modeling (BIM) for infrastructures is on the rise but the use of the life cycle cost analysis (LCCA) integrated within the BIM environment itself to analyze multiple design alternatives and set up a proper maintenance plan has not yet been developed. It is possible to integrate evaluation of maintenance and waste recovery strategies, minimizing costs and the use of raw materials. A visual programming tool was exploited to set up an application that applies the LCCA methodology to a road pavement project in the early design phase, returning the total maintenance cost updated to the year of construction, the number of maintenance interventions, and the residual value of accumulated fatigue damage. The execution of the LCCA tool dynamically updates the properties of the BIM model with the resulting pavement management plan and returns multiple outputs, giving to the user the possibility of making further assessments and store the data as needed. The developed application shows that BIM is a promising tool to implement more complex and useful analyses that can reduce the maintenance planning time and allow the designer to choose between multiple alternative solutions already in the early design phase, bringing consistent cost savings through the possibility of finding the minimum cost solution based on the specific project needs in compliance with the quality and safety criteria to be guaranteed for the road category.

Research on offshore wind power system based on Internet of Things technology

Abstract Wind power generation is considered as one of the very promising new energy power generation methods. Offshore wind farms are usually located in open spaces far from the coast, where the wind is strong enough to generate electricity efficiently and reliably. The operation and maintenance of offshore wind power generation is more important; it will greatly affect the life cycle cost, although it is more difficult. Different from the methods used in other papers, this paper uses the Internet of Things (IOT) technology to collect and analyze wind power generation data to accurately and efficiently realize the operation and maintenance of offshore wind power generation. This paper also establishes an economic model for further analysis. Estimated electricity production under real weather is integrated into the model. According to the estimated model, with IOT technology can reduce maintenance costs by ~75% compared to without IOT technology, and according to our operation and maintenance data, we found that the downtime caused by blades, gearboxes and generators accounted for more than 87% of the total unplanned downtime, and maintenance costs accounted for more than three-fourths of the total maintenance costs. These data have reference significance for the operation and maintenance of offshore wind power generation.

Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics

The increasing availability of condition monitoring data for aircraft components has incentivized the development of Remaining Useful Life (RUL) prognostics in the past years. However, only few studies consider the integration of such prognostics into maintenance planning. In this paper we propose a dynamic, predictive maintenance scheduling framework for a fleet of aircraft taking into account imperfect RUL prognostics. These prognostics are periodically updated. Based on the evolution of the prognostics over time, alarms are triggered. The scheduling of maintenance tasks is initiated only after these alarms are triggered. Alarms ensure that maintenance tasks are not rescheduled multiple times. A maintenance task is scheduled using a safety factor, to account for potential errors in the RUL prognostics and thus avoid component failures. We illustrate our approach for a fleet of 20 aircraft, each equipped with 2 turbofan engines. A Convolution Neural Network is proposed to obtain RUL prognostics. An integer linear program is used to schedule aircraft for maintenance. With our alarm-based maintenance framework, the costs with engine failures account for only 7.4% of the total maintenance costs. In general, we provide a roadmap to integrate imperfect RUL prognostics into the maintenance planning of a fleet of vehicles.

A Study on Improving Economy Efficiency of Pumping Stations Based on Tariff Changes

In this paper, research on improving the economic efficiency of 38 drainage pumping stations was undertaken. Particular attention was paid to the effectiveness of activities without considering any expenditures. Energy costs for this type of machine are usually high, approximately 45% of the total maintenance cost. The main assumption of this work was the selection of appropriate energy tariffs to reduce operating costs. Liquid transport in any economy consumes significant amounts of electricity, estimated at 20–30% of the total electricity production. The optimization of the energy consumption of pumping processes is, therefore, very important. While analyzing the choice of energy tariffs, we designed profitability ratios (PR) specifically for different daily time zones. With the forecasted distribution of energy demands for different daily time zones (usually 2 or 3 zones), it was possible to compare multi-zone tariffs with 24 h tariffs. The profitability of the tariffs was decided by the value of the PR indicator. The lower, the better. In practice, this meant that the analyzed multi-zone tariffs, in most cases, are more profitable compared to single-zone tariffs. In the Polish energy system, each entrepreneur, depending on the connection power, has a right to choose a particular energy tariff from three tariff groups, i.e., high (A), medium (B), and low (C) voltage. In the case of land reclamation pumping stations, energy tariffs are usually from groups B and C. The choice of tariffs largely depends on the contracted capacity and is determined by tariff regulation. Nowadays, the current energy system forces entrepreneurs to declare a connected power load at the level corresponding to the maximum use of the production potential. Lack of knowledge of the hydrological regime, quite common for land reclamation pumping stations, usually results in overestimating the contracted capacity. When comparing the effect of changing tariffs, it was found that the profitability of this method is significant. The four-year study period (2010–2013) showed that active energy in the multi-part tariffs of groups B and C is on average 10.2% cheaper than in the single-part tariffs and varies in a wide range from 2% to 20.4%. The analysis carried out on 38 drainage pumping stations shows that for only five pumping stations could changing the current tariff be unjustified. In the other cases, the four years of analysis demonstrates that changing the current energy tariff could reduce electricity costs by approximately 5%., i.e., approximately PLN 124,000 per year (approximately EUR 27,000).

Multi-objective imperfect selective maintenance optimization for series-parallel systems with stochastic mission duration

To improve the probability that an engineering system successfully completes its next mission, it is crucial to implement timely maintenance activities, especially when maintenance time or maintenance resources are limited. Taking series-parallel system as the object of study, this paper develops a multi-objective imperfect selective maintenance optimization model. Among it, during the scheduled breaks, potential maintenance actions are implemented for the components, ranging from minimal repair to replacement. Considering that the level of maintenance actions is closely related to the maintenance cost, age reduction coefficient and hazard rate adjustment coefficient are taken into account. Moreover, improved hybrid hazard rate approach is adopted to describe the reliability improvement of the components, and the mission duration is regarded as a random variable. On this basis, a nonlinear stochastic optimization model is established with dual objectives to minimize the total maintenance cost and maximize the system reliability concurrently. The fast elitist non-dominated sorting genetic algorithm (NSGA-II) is adopted to solve the model. Numerical experiments are conducted to verify the effectiveness of the proposed approach. The results indicate that the proposed model can obtain better scheduling schemes for the maintenance resources, and more flexible maintenance plans are gained.

An optimal production, maintenance and quality problem, with improved statistical process chart of a supply chain under service and quality requirements

The production system is unreliable, therefore, prone to random failures directly affecting the products quality. The machine status is affected by the production variation and its use over time. In this work a new maintenance strategy with corrective, imperfect and perfect maintenance is employed when the process is respectively in control, surveillance, and critical stages. We increase the reliability of the process and subsequent reduction in the production of the non-conformal items. Several retail houses are to satisfy random customer demands during the finite production horizon with service, quality and production bounds constraints. The work established a collaborative production planning with inventory management considering the production, holding, delivery, delay, and quality costs. Conclusively, the total maintenance costs (corrective, imperfect, and perfect) are minimized, according to the optimal production plans and control chart parameters as decision variables.

Optimizing the Maintenance Schedule of a Combined Cycle Gas Turbine Considering Different Maintenance Types and Operating Hours

An efficient maintenance schedule for gas turbines of power plants ensures a reliable electricity supply. This study addresses a generator maintenance scheduling problem arising from Taiwan’s combined cycle power plant with two notable characteristics, i.e., a specific sequence of various maintenance types and the concept of performing maintenance according to operational hours spent. The objective is to minimize the total maintenance cost. The problem is formulated as a Mixed Integer Linear Program which is solvable by an off-the-shelf exact solver, i.e., CPLEX. Moreover, a set of newly generated instances is proposed as benchmark instances for the problem. The instances generated are based on the realistic conditions obtained from the historical record of Taiwan’s combined cycle power plant. Computational studies are presented as interesting insights regarding the complexity of the problem and the factors driving the total maintenance costs.

Integrated Production and Maintenance Scheduling with Environmental and Operational Considerations

In this paper, we present an integrated production-maintenance scheduling problem with economic and ecological considerations. The contribution of our study consists in developing a new analytical model which determines, an optimal sequence of production and maintenance minimizing earliness and tardiness penalty costs as well as harmful emissions (CO2) cost due to degradation of production system. This study addresses the problem of the single machine where the machine follows a failure law. Each time the machine's degradation rate reaches a predefined threshold (λthreshold), preventive maintenance (PM) is carried out throughout optimal periods. The maintenance strategy adopted makes it possible to define the age of preventive maintenance T* corresponding to a minimum total cost of preventive and corrective maintenance. We consider the result of this strategy as a strong constraint in order to determine an optimal integrated scheduling of production and maintenance. We provide illustrative numerical examples as well as sensitivity analysis to show the applicability of our approaches and provide.

Machine learning approach for integrated maintenance and spare parts management strategies

We targeted a data mining and machine learning approach for integrated maintenance/production and spare parts management problems for components of a wind farm where the level of degradation is noticeable. The degradation is established as a function of the actual functioning mode. Spare parts are stored in a local inventory. The costs associated with the supply of spare parts and their renewal are related to the functioning mode. Our goal is to use a data mining and a machine learning approach in order to optimize the total current cost of maintenance, production and spare parts related costs over a fixed planning horizon. We formulate the problem of the optimal policy structure, which turns out to be a three-threshold policy in all operating modes. Our numerical results show that the cost reductions achieved by the integrated maintenance, production and spare parts optimization are significant.

Izbor politike održavanja n-komponentnog sistema koji se može popraviti korišćenjem genetskog algoritma

A typical manufacturing system consists of a large number of repairable components/ machines which age with time and require maintenance. This paper proposes a novel maintenance policy selection method using genetic algorithm. Where, maintenance problem is formulated for n-component repairable system to minimize the total maintenance cost. The various maintenance policies and repairable components are represented in the form of chromosomes, initially various chromosomes are randomly generated which are then assessed and selected using fitness value and then crossover and mutation function is performed to obtain a better chromosome. Several iterations are performed till the desired results is achieved. The proposed algorithm is further explained and validated through an illustrative example.

Improving Prescriptive Maintenance by Incorporating Post-Prognostic Information Through Chance Constraints

Maintenance is one of the critical areas in operations in which a careful balance between preventive costs and the effect of failures is required. Thanks to the increasing data availability, decision-makers can now use models to better estimate, evaluate, and achieve this balance. This work presents a maintenance scheduling model which considers prognostic information provided by a predictive system. In particular, we developed a prescriptive maintenance system based on run-to-failure signal segmentation and a Long Short Term Memory (LSTM) neural network. The LSTM network returns the prediction of the remaining useful life when a fault is present in a component. We incorporate such predictions and their inherent errors in a decision support system based on a stochastic optimization model, incorporating them via chance constraints. These constraints control the number of failed components and consider the physical distance between them to reduce sparsity and minimize the total maintenance cost. We show that this approach can compute solutions for relatively large instances in reasonable computational time through experimental results. Furthermore, the decision-maker can identify the correct operating point depending on the balance between costs and failure probability.

A Novel Approach to Multi-Compartmental Model Implementation to Achieve Metabolic Model Identifiability on Patient's CGM Data

Diabetes is a widespread civilization disease. When developing a new treatment method, in-silico benefits the development process by reducing the need for in-vivo subjects. In-silico evaluation requires a reliable metabolic model, often created as a multi-compartmental model. A common approach to implementing a multi-compartmental model is to use a system of ordinary differential equations. This approach utilises exponential transfer functions to transfer substances among the compartments. Using other than an exponential function is complex. Therefore, we propose a novel approach based on a direct, numeric integration of separated compartments, which can be further divided into individual depots. This enables to model substance transfer as a separate process with non-exponential characteristics, e.g.; when modelling carbohydrate absorption from the gut. As another benefit, the approach obeys the law of mass conservation on both the computational and architectural levels. This is a key feature when identifying a model on data, that are not measured within a controlled, isolated environment. Moreover, we actually transform the set of equations, i.e.; computer-code functions, into a component model to reduce the total maintenance costs – readability, testing, verification and deployment. We demonstrate the proposed approach by converting the Samadi model to it and enhancing it with a non-exponential transfer function.