Optimal Preventive Maintenance Schedule Research Articles

Decision support for optimal scheduling of highway pavement preventive maintenance within resurfacing cycle

This paper presents a case study for optimizing decisions on the best combination of preventive maintenance (PM) treatments and timings to be applied in the resurfacing life-cycle (interval between resurfacing events), for a given highway pavement section. In the optimization procedure, the paper incorporates key infrastructure management concepts of treatment-specific triggers, performance jump models, and performance trend models. Using a case study, the paper determines that optimization can be a viable tool to support scheduling decisions for highway preventive maintenance. Also, using sensitivity analysis, it is determined that changes in the resurfacing interval length and discount rate can influence the choice of optimal PM schedule.

Optimal Maintenance and Replacement of Extraction Machinery

This paper considers a problem of optimal preventive maintenance and replacement schedule of equipment devoted to extracting resources from known deposits. Typical examples are oil drills, mine shovels, etc. At most one replacement of the existing machinery by a new one is allowed. The problem is formulated as an optimal control problem subject to the state constraint that the remaining deposit at any given time is nonnegative. We show that the optimal preventive maintenance, production rates, and the replacement and salvage times of the existing machinery and the new one, if required, can be obtained by solving sequentially a series of free-end-point optimal control problems. Moreover, an algorithm based on this result is developed and used to solve two illustrative examples.

Optimal generator maintenance scheduling using a modified discrete PSO

A modified discrete particle swarm optimisation (MDPSO) algorithm to generate optimal preventive maintenance schedule of generating units for economical and reliable operation of a power system, while satisfying system load demand and crew constraints, is presented. Discrete particle swarm optimisation (DPSO) is known to effectively solve large-scale multi-objective optimisation problems and has been widely applied in power system. The MDPSO proposed for the generator maintenance scheduling optimisation problem generates optimal and feasible solutions and overcomes the limitations of the conventional methods, such as extensive computational effort, which increases exponentially as the size of the problem increases. The efficacy of the proposed algorithm is illustrated and compared with the genetic algorithm (GA) and DPSO in two case studies – a 21-unit test system and a 49-unit system feeding the Nigerian national grid. The MDPSO algorithm is found to generate schedules with comparatively higher system reliability indices than those obtained with GA and DPSO.

Preventive maintenance scheduling by variable dimension evolutionary algorithms

Black box optimization strategies have been proven to be useful tools for solving complex maintenance optimization problems. There has been a considerable amount of research on the right choice of optimization strategies for finding optimal preventive maintenance schedules. Much less attention is turned to the representation of the schedule to the algorithm. Either the search space is represented as a binary string leading to highly complex combinatorial problem or maintenance operations are defined by regular intervals which may restrict the search space to suboptimal solutions. An adequate representation however is vitally important for result quality. This work presents several nonstandard input representations and compares them to the standard binary representation. An evolutionary algorithm with extensions to handle variable length genomes is used for the comparison. The results demonstrate that two new representations perform better than the binary representation scheme. A second analysis shows that the performance may be even more increased using modified genetic operators. Thus, the choice of alternative representations leads to better results in the same amount of time and without any loss of accuracy.

Optimal Preventive Maintenance Scheduling in Semiconductor Manufacturing

Preventive maintenance (PM) scheduling is a very challenging task in semiconductor manufacturing due to the complexity of highly integrated fab tools and systems, the interdependence between PM tasks, and the balancing of work-in-process (WIP) with demand/throughput requirements. In this paper, we propose a two-level hierarchical modeling framework. At the higher level is a model for long-term planning, and at the lower level is a model for short-term PM scheduling. Solving the lower level problem is the focus of this paper. We develop mixed-integer programming (MIP) models for scheduling all due PM tasks for a group of tools, over a planning horizon. Interdependence among different PM tasks, production planning data such as projected WIP levels, manpower constraints, and associated PM time windows and costs, are incorporated in the model. Results of a simulation study comparing the performance of the model-based PM schedule with that of a baseline reference schedule are also presented.

Vulnerability based management of water resources systems

Must the water networks be fail-proof or must they remain safe during a failure? What must water system managers try to achieve? The present paper introduces a methodology for the hierarchical analysis (in time and space) of the preventive maintenance policy of water supply networks, using water supply system performance indices. This is being accomplished through a technical–economic analysis that takes into account all kinds of costs referring to the repair or replacement of trouble-causing parts of the water supply network. The optimal preventive maintenance schedule suggested by the methodology is compared with the empirically based maintenance policy applied to the Athens water supply system.

Optimal periodic maintenance strategy under an intermittently used environment

In this paper, the optimal periodic replacement-like strategy with minimal repairs is considered under an intermittently used environment. Suppose that transactions or jobs with exponential service times arrive at a system according to a homogeneous Poisson process and the system is alternatively in either a busy or an idle period subject to demand for use. We derive approximately the optimal preventive maintenance schedule which minimizes the relevant expected cost criterion for the system which behaves intermittently on an M/M/l queueing process, and compare it with the usual maintenance schedule for the continuously used system.

Optimal preventive maintenance planning for multiple elevators

The purpose of this paper is to properly plan the preventive maintenance schedule for multiple elevators and optimize the number of maintenance workers.The total amount of the maintenance cost consisting of the labor cost, the part cost, and the quality cost (the loss evaluated in terms of cost, to be incurred when an elevator breaks down) is to be minimized.The method is presented of setting up the optimal preventive maintenance schedule on a long‐term basis by rescheduling the contents of schedule dynamically and flexibly in accordance with the ever‐changing maintenance conditions, taking the possibility of the future occurrence of failure into consideration. From numerical experiments, the validity of the proposal procedure for planning the preventive maintenance schedule and the effectiveness of considering the possibility of the future occurrence of failure in planning the schedule are shown, and the optimal number of maintenance workers can be decided.

Sequential imperfect preventive maintenance models with two categories of failure modes

In this paper, we introduce the concept of two categories of failure modes: maintainable failure modes and nonmaintainable failure modes into the modeling of preventive maintenance (PM) activities. The hybrid model proposed by Lin et al. is used to model the effect of a PM activity on the failure rate function of maintainable failure modes. However, PM does not change the hazard rate function of nonmaintainable failure modes. PM is performed at a sequence of intervals. The objective is to determine the optimal PM schedules to minimize the mean cost rate. Numerical examples for Weibull hazard rates are given. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 172–183, 2001

インフラ設備の最適アフターサービス戦略に関する研究 ２ 複数エレベータを対象とした最適保全スケジューリング

The purpose of this paper is to properly plan the preventive maintenance schedules of multi elevators and optimize the number of maintenance personnel so as to minimize the total cost of the maintenance cost composed of labor cost and parts cost, and the quality cost, namely the loss to be incurred when an elevator breaks down. The method is presented to compose the optimal preventive maintenance schedules on a long-term basis by rescheduling the contents of schedules dynamically and flexibly in accordance with ever-changing maintenance conditions, with taking the possibility of the future failure occurrence into consideration. From numerical experiments, the validity of the presented procedure for planning the preventive maintenance schedules and the effectiveness of considering the possibility of the future failure occurrence in planning the schedules are shown, and the optimal number of maintenance personnel can be determined.

Optimal periodic maintenance strategy under an intermittently used environment

In this paper, the optimal periodic replacement-like strategy with minimal repairs is considered under an intermittently used environment. Suppose that transactions or jobs with exponential service times arrive at a system according to a homogeneous Poisson process and the system is alternatively in either a busy or an idle period subject to demand for use. We derive approximately the optimal preventive maintenance schedule which minimizes the relevant expected cost criterion for the system which behaves intermittently on an M/M/1 queueing process, and compare it with the usual maintenance schedule for the continuously used system.

GENERAL SEQUENTIAL IMPERFECT PREVENTIVE MAINTENANCE MODELS

This paper presents new sequential imperfect preventive maintenance (PM) models incorporating adjustment/improvement factors in hazard rate and effective age. The models are hybrid in the sense that they are combinations of the age reduction PM model and the hazard rate adjustment PM model. It is assumed that PM is imperfect: It not only reduces the effective age but also changes the hazard rate, while the hazard rate increases with the number of PMs. PM is performed in a sequence of intervals. The objective is to determine the optimal PM schedule to minimize the mean cost rate. Numerical examples for a Weibull distribution are given.

Solving Semi-Markov Decision Problems Using Average Reward Reinforcement Learning

A large class of problems of sequential decision making under uncertainty, of which the underlying probability structure is a Markov process, can be modeled as stochastic dynamic programs (referred to, in general, as Markov decision problems or MDPs). However, the computational complexity of the classical MDP algorithms, such as value iteration and policy iteration, is prohibitive and can grow intractably with the size of the problem and its related data. Furthermore, these techniques require for each action the one step transition probability and reward matrices, and obtaining these is often unrealistic for large and complex systems. Recently, there has been much interest in a simulation-based stochastic approximation framework called reinforcement learning (RL), for computing near optimal policies for MDPs. RL has been successfully applied to very large problems, such as elevator scheduling, and dynamic channel allocation of cellular telephone systems. In this paper, we extend RL to a more general class of decision tasks that are referred to as semi-Markov decision problems (SMDPs). In particular, we focus on SMDPs under the average-reward criterion. We present a new model-free RL algorithm called SMART (Semi-Markov Average Reward Technique). We present a detailed study of this algorithm on a combinatorially large problem of determining the optimal preventive maintenance schedule of a production inventory system. Numerical results from both the theoretical model and the RL algorithm are presented and compared.

Cost optimal preventive maintenance and replacement scheduling

In this paper, we present a method for predicting a cost-optimal preventive maintenance policy for a repairable system with an increasing Rate of Occurrence Of Failure (ROCOF). We segment the maintenance planning horizon into n discrete and equally-sized periods. For each period, we predict which of three possible actions, (maintain the system, replace the system, or do nothing to the system) should be taken, such that the total net present worth of all future costs is minimized. Our model expands upon previous work by utilizing the concept that maintenance reduces the “effective age” of the system and hence the system ROCOF. We develop the model and evaluate three solution procedures, namely, a random search, a genetic algorithm, and a branch-and-bound approach. The entire approach is illustrated through the use of a numerical example.

Cost optimal preventive maintenance and replacement scheduling

In this paper, we present a method for predicting a cost-optimal preventive maintenance policy for a repairable system with an increasing Rate of Occurrence Of Failure (ROCOF). We segment the maintenance planning horizon into n discrete and equally-sized periods. For each period, we predict which of three possible actions, (maintain the system, replace the system, or do nothing to the system) should be taken, such that the total net present worth of all future costs is minimized. Our model expands upon previous work by utilizing the concept that maintenance reduces the “effective age” of the system and hence the system ROCOF. We develop the model and evaluate three solution procedures, namely, a random search, a genetic algorithm, and a branch-and-bound approach. The entire approach is illustrated through the use of a numerical example.

A full history proportional hazards model for preventive maintenance scheduling

This paper is concerned with the development of a realistic preventive maintenance (PM) scheduling model. A heuristic approach for implementing the semi-parametric proportional-hazards model (PHM) to schedule the next preventive maintenance interval on the basis of the equipment's full condition history is introduced. This heuristic can be used with repairable systems and does not require the unrealistic assumption of renewal during repair, or even during PM. Two PHMs are fitted, for the life of equipment following corrective work and the life of equipment following PM, using appropriate explanatory variables. These models are then used within a simulation framework to schedule the next preventive maintenance interval. Optimal PM schedules are estimated using two different criteria, namely maximizing availability over a single PM interval and over a fixed horizon. History data from a set of four pumps operating in a continuous process industry is also used to demonstrate the proposed approach. The results indicate a higher availability for the recommended schedule than the availability resulting from applying the optimal PM intervals as suggested by using the conventional stationary models. © 1997 John Wiley & Sons, Ltd.

Optimal preventive maintenance scheduling of thermal generating units in power systems —A survey of problem formulations and solution methods

Preventive maintenance scheduling of thermal generating units occupies a significant place in power system operation and expansion planning; at the same time it is a challenging optimization problem. The purpose of this paper is to analyse essential features of the maintenance scheduling problem, including imposed constraints and various objectives, as well as to present the results of the research work done in this field using operational research methods. Papers published during last fifteen years are discussed, with special regard for the applied optimization techniques.

Optimal Reliability Allocation Under Preventive Maintenance Schedule

This paper considers a series system of components with time-dependent reliability and gives a new formulation of an optimal reliability allocation problem where an optimal preventive maintenance (PM) schedule is determined simultaneously. The importance of this formulation is shown in comparison with a conventional formulation where PM schedule is not taken into account. The optimization problem becomes a nonlinear mixed-integer programming problem. A simple approximate solution algorithm is given on the basis of a nonlinear programming (NLP) algorithm. The procedure is illustrated by use of a numerical example. Though we restrict our attention to the case where a preventive replacement is adopted as a PM policy, a similar discussion is possible for the as-good-as-new repair.

Several objective criteria for optimal generator preventive maintenance scheduling

A number of objective criteria for optimal maintenance scheduling of thermal generators is discussed. The criteria are based on generation operating cost, reliability indices, deviations from a desired schedule and/or constraints violation penalties. A comparison of the performance of all these criteria is presented by maintenance scheduling a realistic 30 thermal-unit system. Also, the relationships between the several realibility indices are dicussed.