Maintenance Scheduling Model Research Articles

Sensor-Driven Condition-Based Generator Maintenance Scheduling—Part I: Maintenance Problem

Traditionally, generator maintenance scheduling has been implemented using highly conservative maintenance policies based on manufacturing specifications and engineering expertise on the type of generators. However, recent advances in sensor technology, signal processing, and embedded online diagnosis provide more unit-specific information on the degradation characteristics of the generators. In this two-paper study, we propose a new generation maintenance framework that integrates the sensor-driven predictive maintenance technologies with optimal maintenance scheduling models. In Part I, we propose a new mixed-integer optimization model for generation maintenance scheduling, which effectively incorporate the dynamic information of generators' health and maintenance cost provided by the Bayesian prognostic models. In Part II, we propose a framework that extends the maintenance model presented herein, and consider the effects of maintenance on network operation by coordinating generator maintenance schedules with the unit commitment and dispatch decisions. We introduce new reformulations and efficient algorithms for solving large-scale instances of the proposed maintenance scheduling model. Extensive computational studies using real-world degradation data demonstrates the effectiveness of the new framework.

System Hardening and Condition-Based Maintenance for Electric Power Infrastructure Under Hurricane Effects

The devastating impact of hurricanes on electric power systems calls for development of sophisticated asset management strategies for utility infrastructure. El Nino/La Nina are shown to have seasonal effects on hurricane arrivals in the long-term climatological horizon. The periodic effects of such natural phenomena need to be analyzed and incorporated into decision making processes for strategic asset management of power systems. In this paper, an integrated infrastructure hardening and condition-based maintenance scheduling model for critical components of the power systems is proposed. Partially observable Markov decision processes are used to formulate the problem. The survival function against hurricanes is derived as a dynamic stress–strength model, and is incorporated in the proposed framework. A generalized formulation for two hardening strategies, i.e., with temporary and permanent hardening effects, is proposed. Case studies are conducted to analyze the model and further illustrate the effectiveness of the proposed strategy.

Track geometry degradation and maintenance modelling: A review

Increased demand for railway transportation is creating a need for higher train speeds and axle loads. These, in turn, increase the likelihood of track degradation and failures. Modelling the degradation behaviour of track geometry and development of applicable and effective maintenance strategies has become a challenging concern for railway infrastructure managers. During the last three decades, a number of track geometry degradation and maintenance modelling approaches have been developed to predict and improve the railway track geometry condition. In this paper, existing track geometry measures are identified and discussed. Available models for track geometry degradation are reviewed and classified. Tamping recovery models are also reviewed and discussed to identify the issues and challenges of different available methodologies and models. Existing track geometry maintenance models are reviewed and critical observations on each contribution are provided. The most important track maintenance scheduling models are identified and discussed. Finally, the paper provides directions for further research.

Integrating Preventive Maintenance Scheduling As Probability Machine Failure And Batch Production Scheduling

This paper discusses integrated model of batch production scheduling and machine maintenance scheduling. Batch production scheduling uses minimize total actual flow time criteria and machine maintenance scheduling uses the probability of machine failure based on Weibull distribution. The model assumed no nonconforming parts in a planning horizon. The model shows an increase in the number of the batch (length of production run) up to a certain limit will minimize the total actual flow time. Meanwhile, an increase in the length of production run will implicate an increase in the number of PM. An example was given to show how the model and algorithm work.

Robust generation maintenance scheduling considering wind power and forced outages

Generation maintenance scheduling has system level and equipment level aspects. At the system level, large-scale integration of wind power brings about significant challenges for classical generator maintenance scheduling. At the equipment level, forced outages of power equipment caused by wear affect maintenance decisions. Here, the authors report a robust optimisation model for maintenance scheduling considering wind power integration and forced outages of equipment. An uncertainty set is used to describe fluctuations in the wind power, and a method to control the conservativeness of the robust model is developed. An outage model is used to describe the impact of the operating conditions on maintenance decisions. The effects and advantages of the model are demonstrated via numerical simulations.

An Approach for Integrated Generation and Transmission Maintenance Scheduling Considering N-1 Contingencies

This paper presents an approach for integrated generation and transmission maintenance scheduling model (IMS) that takes into consideration N-1 contingencies. The objective is to maximize the maintenance preference of facility owners while satisfying N-1 security and other constraints. To achieve this goal, Benders decomposition is employed to decompose the problem into a master problem and several sub-problems. A Relaxation Induced (RI) algorithm is proposed to efficiently solve the large mixed integer programming (MIP) master problem. This algorithm is based on the solution of the linear relaxed problem. It is demonstrated that the proposed algorithm can efficiently reach a near-optimal solution that is usually satisfactory. If this near-optimal solution is not acceptable, it is used as the initial solution to fast start the solution of the original IMS problem. The performance of the proposed method is demonstrated using a modified version of the IEEE 30-bus system and a model of the power system of a Chinese province. Case studies show that the proposed algorithm can improve the computational efficiency by more than an order of magnitude.

A Pragmatic Optimization Method for Motor Train Set Assignment and Maintenance Scheduling Problem

With the rapid development of high-speed railway in China, the problem of motor train set assignment and maintenance scheduling is becoming more and more important for transportation organization. This paper focuses on considering the special maintenance items of motor train set and mainly meets the two maintenance cycle limits on aspects of mileage and time for each item. And then, a 0-1 integer programming model for motor train set assignment and maintenance scheduling is proposed, which aims at maximizing the accumulated mileage before each maintenance and minimizing the number of motor train sets. Restrictions of the model include the matching relation between motor train sets and routes as well as that between motor train sets and maintenance items and maintenance capacity of motor train set depot. A heuristic solution strategy based on particle swarm optimization is also proposed to solve the model. In the end, a case study is designed based on the background of Beijing south depot in China, and the result indicates that the model and algorithm proposed in this paper could solve the problem of motor train set assignment and maintenance scheduling effectively.

Integrated batch production and maintenance scheduling for multiple items processed on a deteriorating machine to minimize total production and maintenance costs with due date constraint

This paper discusses an integrated model of batch production and maintenance scheduling on a deteriorating machine producing multiple items to be delivered at a common due date. The model describes the trade-off between total inventory cost and maintenance cost as the increase of production run length. The production run length is a time bucket between two consecutive preventive maintenance activities. The objective function of the model is to minimize total cost consisting of in process and completed part inventory costs, setup cost, preventive and corrective maintenance costs and rework cost. The problem is to determine the optimal production run length and to schedule the batches obtained from determining the production run length in order to minimize total cost.

Availability and cost-centered preventive maintenance scheduling of continuous operating series systems using multi-objective genetic algorithm: A case study

This article presents a multi-objective (maximization of availability and minimization of maintenance cost) preventive maintenance (PM) scheduling model for a continuous operating series system (COSS) which do not provide an off-working period for PM. The objective functions are optimized by using a Multi-Objective Genetic Algorithm (MOGA). The effectiveness of the model is demonstrated through a coal-fired boiler-tube. The case study shows that the model can improve the availability along with profound reduction of the maintenance cost, i.e., increases the profit of the plant.

Selective maintenance scheduling over a finite planning horizon

A preventive maintenance scheduling model is proposed in this article. The proposed model includes finite planning horizon and limited available resources to perform maintenance scheduling. A subset of maintenance actions, that is, selective maintenance is needed during maintenance breaks due to limited resources such as time, cost, and repairman availability. Maintenance can not only improve the effective age of a component but also may alter the hazard rate. Therefore, a hybrid imperfect maintenance model is used in this article that considers the combined effect of age reduction and hazard adjustment on a component. For a multi-component system, selective maintenance is performed at periodic intervals. In addition to maintenance and failure costs, we have included the maintenance break duration and the shutdown cost in the proposed scheduling model. A periodic maintenance scheduling problem is solved in this article for a series–parallel system. The optimal number of periodic maintenance breaks in a finite planning horizon is determined. Also, maintenance actions required during each of the maintenance breaks are determined. The number of periodic maintenance breaks and maintenance actions during these breaks is selected in a way that the total maintenance, failure, and shutdown cost are minimum. An evolutionary algorithm is used to solve the problem.

Combined SA PSO method for transmission constrained maintenance scheduling using levelized risk method

This paper presents a model for maintenance scheduling (MS) of generators using combined simulated annealing and binary particle swarm optimization (CSABPSO) based stochastic approach. The objective function of this paper is to reduce the loss of load probability (LOLP) for a power system. The hybrid algorithm combines BPSO with simulated annealing behavior. The CSABPSO algorithm takes both of the advantages, good solution quality of SA and fast searching ability of BPSO. As stochastic optimization algorithms are sensitive to their parameters, proper weight update procedure for parameters selection is introduced in this paper to improve solution quality. Weight update operators are introduced in the BPSO in order to obtain the diversified solutions in the search space. In this paper the impacts of transmission constrained MS is considered. Moreover, the CSABPSO based economic dispatch (ED) and load flow have been decomposed as a sub-problem in the maintenance model that results to a more practical transmission constrained maintenance schedule. A case study for IEEE reliability test system (RTS) is used for detailed analysis and further results are presented for roy billinton test system (RBTS) which shows the proposed algorithm can accomplish a significant levelization in the reliability indices over the planning horizon and demonstrates the usefulness of the proposed approach. The simulation results obtained by proposed algorithm indicate transmission line constraint influence on establishment of optimal MS. The CSABPSO could have higher efficiency, better quality than the other compared algorithms.

Preventive Maintenance Scheduling Based on Short Circuit and Overload Currents

One of the important aspects of smart grid is to make the best use of existing assets. One way (among others) to achieve this objective is by scheduling effective maintenance routines. Abnormal conditions, including short circuits or overloads, would affect not only the exposed components, but also those farther away from the fault locations. This exposure may cause some of the components fail right away or at a later time. Therefore, the severity, frequency, and timings of system faults are important factors in planning maintenance programs. In this paper, the impact of increased current flows, in the form of short circuits and overloads, on component failure rates are studied and a maintenance scheduling model taking into account such impacts is developed. Using the developed model, the optimum mean time to preventive maintenance is derived and used to maximize the components availability. Results of this paper would help in preventing damage to the equipment by optimally scheduling maintenance programs based on short circuit current levels of the system.

An integrated three-stage maintenance scheduling model for unrelated parallel machines with aging effect and multi-maintenance activities

We propose an integrated three-stage model for maintenance scheduling of unrelated parallel machines (UPMs) with aging effect and multi-maintenance activities (AEMMAs) using a variety of MODM techniques such as the fuzzy analytic hierarchy process (AHP), the technique for order of preference by similarity to ideal solution (TOPSIS), and goal programming (GP). We use fuzzy AHP in the first stage of the proposed model to account for the inherent ambiguity and vagueness in real-life maintenance scheduling problems. In the second stage, we use TOPSIS to reduce the multi-objective problem into an efficient bi-objective problem. Finally, we use GP to solve the resulting bi-objective problem and develop an optimal maintenance schedule in the third stage of the model. We use a numerical example to demonstrate the applicability of the proposed approach and exhibit the efficacy of the procedures and algorithms.

Transmission maintenance scheduling strategy considering potential fault risk balance

SUMMARY In the period of transmission maintenance, the phenomenon of power interruption or even progressing to cascading failure occurs commonly just originating from one or some elements out of service. Therefore, reasonable transmission maintenance scheduling is important to ensure the safe and stable operation of power supply system. In this paper, a transmission maintenance scheduling strategy considering potential fault risk balance is proposed. Combined with the theory of value at risk and the character of potential fault, the potential fault risk index is defined to represent the possibility and risk level of the whole system power failure under the condition of the certain element failing. Meanwhile, compared with traditional transmission maintenance scheduling model, the balance index and normalized weight factor shown as the system's risk difference of each maintenance period is considered into the objective function to achieve risk sharing and real operation reliability in the whole schedule. In addition, the random potential fault simulation is applied to solve calculation problem when making the schedule. Numerical results of IEEE-118 bus system show the validity of the methodology proposed. Copyright © 2015 John Wiley & Sons, Ltd.

Developing an Enhanced Short-Range Railroad Track Condition Prediction Model for Optimal Maintenance Scheduling

As railroad infrastructure becomes older and older and rail transportation is developing towards higher speed and heavier axle, the risk to safe rail transport and the expenses for railroad maintenance are increasing. The railroad infrastructure deterioration (prediction) model is vital to reducing the risk and the expenses. A short-range track condition prediction method was developed in our previous research on railroad track deterioration analysis. It is intended to provide track maintenance managers with two or three months of track condition in advance to schedule track maintenance activities more smartly. Recent comparison analyses on track geometrical exceptions calculated from track condition measured with track geometry cars and those predicted by the method showed that the method fails to provide reliable condition for some analysis sections. This paper presented the enhancement to the method. One year of track geometry data for the Jiulong-Beijing railroad from track geometry cars was used to conduct error analyses and comparison analyses. Analysis results imply that the enhanced model is robust to make reliable predictions. Our in-process work on applying those predicted conditions for optimal track maintenance scheduling is discussed in brief as well.

Model Integrasi Penjadwalan Produksi Batch dan Penjadwalan Perawatan dengan Kendala Due Date

This paper discusses the integration model of batch production and preventive maintenance scheduling on a single machine producing an item to be delivered at a common due date. The machine is a deteriorating machine that requires preventive maintenance to ensure the availability of the machine at a desired service level. Decision variables of the model are the number of preventive maintenances, the schedule, length of production runs, as well as the number of batches, batch sizes and the production schedule of the resulting batches for each production run. The objective function of the model is to minimize the total cost consisting of inventory costs during parts processing, setup cost and cost of preventive maintenance. The results show three important points: First, the sequence of optimal batches always follows the SPT (short processing time). Second, variation of preventive maintenance unit cost does not influence the sequence of batches. Third, the first production run length from production starting time is smaller than the next production run length and this pattern continues until the due date. When in process inventory unit cost is increased, the pattern will continue until a specified cost limit, and beyond the limit the pattern will change to be the opposite pattern.

Coordinative production and maintenance scheduling problem with flexible maintenance time intervals

This study investigates the simultaneous scheduling of production and planning of maintenance activities in the flow shop scheduling environment. The problem is considered in a bi-objective form, minimizing the makespan as the production scheduling criterion and minimizing the system unavailability as the maintenance planning criterion. We propose the coordinative production and maintenance scheduling model in which the time interval between consecutive maintenance activities as well as the number of maintenance activities on each machine are assumed to be non-fixed. The coordinative model aims to find the best permutation of jobs as the production problem and to assign the maintenance activities into the schedule as the maintenance problem, simultaneously. Moreover, a special setting called single server maintenance is introduced and discussed. A bi-objective ant colony system algorithm is presented to solve the problem in focus, introducing some novel ideas. CDS and NEH heuristics are applied to define the heuristic information part of the proposed algorithm. Some experiments are carried out to select the appropriate heuristic method between CDS and NEH. Moreover, some experiments are performed using the well-known Taillard benchmark, comparing the performance of the proposed algorithm with another ant colony optimization algorithm. Computational experiments indicate the effectiveness of the proposed algorithm.

Optimal maintenance scheduling for local public purpose buildings

Purpose – The purpose of this paper is to set up and analyze a formal model for maintenance scheduling for local government purpose buildings. Design/methodology/approach – The authors formulate the maintenance scheduling decision as a dynamic optimization problem, subject to an accelerating decay. This approach offers a formal, yet intuitive, weighting of an important trade-off when deciding a maintenance schedule. Findings – The optimal maintenance schedule reflects a trade-off between the interest rate and the rate at which the decay accelerates. The prior reflects the alternative cost, since the money spent on maintenance could be saved and earn interests, while the latter reflects the cost of postponing maintenance. Importantly, it turns out that it is sub-optimal to have a cyclical maintenance schedule where the building is allowed to decay and then be intensively maintained before decaying again. Rather, local governments should focus the maintenance either early in the building’s life-span and eventually let it decay toward replacement/abandonment or first let it decay to a target level and then keep it there until replacement/abandonment. Which of the two is optimal depends on the trade-off between the alternative cost and the cost of postponing maintenance. Originality/value – The paper provides a first formal inquiry into important trade-offs that are important for maintenance scheduling of local public purpose buildings.

Optimal maintenance scheduling for a complex manufacturing system subject to deterioration

We address the problem of determining inspection strategy and replacement policy for a deteriorating complex multi-component manufacturing system whose state is partially observable. We develop inspection and replacement scheduling models and other simple maintenance scheduling models via employing an imperfect repair model coupled with a damage process induced by operational conditions. The system state in performance of the imperfectly repaired system is modelled using a proportional intensity model incorporating a damage process and a virtual age process caused by repair. The system is monitored at periodic times and maintenance actions are carried out in response to the observed system state. Decisions to perform imperfect repair and replacement are based on the system state and crossing of a replacement threshold. The model proposed here aims at joint determination of a cost-optimal inspection and replacement policy along with an optimal level of maintenance which result in low maintenance cost and high operational performance and reliability of the system. To demonstrate the use of the model in practical applications a numerical example is provided. Solutions to optimal system parameters are obtained and the response of the model to these parameters is examined. Finally some features of the model are demonstrated. The approach presented provides a framework so that different scenario can be explored.

Short-term transmission maintenance scheduling based on the Benders decomposition

Summary This paper proposes a modified Benders decomposition method to solve a new short-term transmission maintenance scheduling model in vertically integrated power systems. The model aims to minimize the maintenance cost and the expected cost of lost load. The random failures of transmission components and system reliability constraints are also considered. Since traditional heuristic algorithms cannot guarantee the global optimal solution for this mixed-integer nonlinear programming problem, the Benders decomposition is adopted despite some difficulties due to the problem features. Compared with that of generation maintenance scheduling, the specific difficulties of transmission maintenance scheduling are analyzed and several modifications are proposed to improve the convergence of the Benders decomposition. In addition, the influenced energy index is introduced to select the critical branches and reduce computational complexity in reliability evaluation as well. Numerical results of a test system demonstrate that the modified Benders decomposition method is highly efficient in arranging short-term transmission maintenance. Copyright © 2014 John Wiley & Sons, Ltd.