A new multi-objective optimization model for preventive maintenance and replacement scheduling of multi-component systems

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.     

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.     

Combining preventive replacement and burn‐in procedures

This paper considers a non‐homogeneous population of items. Most of the items fail due to a wear‐out process. Therefore it is reasonable to employ a procedure of preventive maintenance. Some items, however, are of short life due to intrinsic defects. When used as replacement parts they may spoil results of planned prevention. The paper seeks for equilibrium of burn‐in and preventive replacement periods. Copyright © 2002 John Wiley & Sons, Ltd.     

Multi-objective group scheduling optimization integrated with preventive maintenance

This article proposes a single-machine-based integration model to meet the requirements of production scheduling and preventive maintenance in group production. To describe the production for identical/similar and different jobs, this integrated model considers the learning and forgetting effects. Based on machine degradation, the deterioration effect is also considered. Moreover, perfect maintenance and minimal repair are adopted in this integrated model. The multi-objective of minimizing total completion time and maintenance cost is taken to meet the dual requirements of delivery date and cost. Finally, a genetic algorithm is developed to solve this optimization model, and the computation results demonstrate that this integrated model is effective and reliable.     

A knowledge-based prototype for optimization of preventive maintenance scheduling

A methodology has been developed, and a prototype tool, the Maintenance Advisor, has been designed and implemented based on this methodology which would assist the scheduling and decision-making for performance of preventive maintenance activities in a plant, based on probabilistic judgedment and probabilistic inference rules. Using data on failure rates, repair times, repair costs and indirect economic costs (e.g. power replacement and accident risk), and within the imposed deterministic constrainst, the program develops an optimum (minimum expected cost) maintenance schedule for the various pieces of equipment described by the model.

The Maintenance Advisor is a frame-based object-oriented tool, programed in KEE and Lisp. Equipment and other objects are represented as complex units, containing a complete set of characteristics, data and functional capabilities. Functional relations between the units are described in terms of two relations: TYPE-OF and PART-OF. The hierarchies formed by these relations serve as the basis for probabilistic and other inferences.     

Nonparametric preventive maintenance optimization models under earning rate criteria

The expected profit is frequently used as an optimality criterion in manufacturing and maintenance operations. In this paper, we consider the optimal preventive maintenance policy and the optimal repair limit policy which maximize the expected earning rates for one-unit systems subject to failure and repair, respectively. In particular, the total time on test concept is used to directly estimate the optimal policies from empirical sample data. The proposed methods provide geometrical interpretations of the underlying probabilistic problems to practitioners as well as nonparametric estimators of the optimal maintenance strategies.     

On the effect of downtime costs and budget constraint on preventive and replacement policies

This work proposes a general approach to study and improve the effectiveness of the system with respect to its expected life-cycle cost rate. The model we propose considers a production system which is protected against demand fluctuations and failure occurrences with elements like stock piles, line and equipment redundancy, and the use of alternative production methods. These design policies allow to keep or minimize the effect on the nominal throughput, while corrective measures are taken. The system is also subject to an aging process which depends on the frequency and quality of preventive actions. Making decisions is difficult because of discontinuities in intervention and downtime costs and the limited budget. We present a non-linear mixed integer formulation that minimizes the expected overall cost rate with respect to repair, overhaul and replacement times and the overhaul improvement factor proposed in the literature. The model is deterministic and considers minimal repairs and imperfect overhauls. We illustrate its application with a case based on a known benchmark example.     

New methods to minimize the preventive maintenance cost of series–parallel systems using ant colony optimization

This article is based on a previous study made by Bris, Châtelet and Yalaoui [Bris R, Chatelet E, Yalaoui F. New method to minimise the preventive maintenance cost of series–parallel systems. Reliab Eng Syst Saf 2003;82:247–55]. They use genetic algorithm to minimize preventive maintenance cost problem for the series–parallel systems. We propose to improve their results developing a new method based on another technique, the Ant Colony Optimization (ACO). The resolution consists in determining the solution vector of system component inspection periods, T_P. Those calculations were applied within the programming tool Matlab. Thus, highly interesting results and improvements of previous studies were obtained.     

Extended great deluge algorithm for the imperfect preventive maintenance optimization of multi-state systems

This paper deals with preventive maintenance optimization problem for multi-state systems (MSS). This problem was initially addressed and solved by Levitin and Lisnianski [Optimization of imperfect preventive maintenance for multi-state systems. Reliab Eng Syst Saf 2000;67:193–203]. It consists on finding an optimal sequence of maintenance actions which minimizes maintenance cost while providing the desired system reliability level. This paper proposes an approach which improves the results obtained by genetic algorithm (GENITOR) in Levitin and Lisnianski [Optimization of imperfect preventive maintenance for multi-state systems. Reliab Eng Syst Saf 2000;67:193–203]. The considered MSS have a range of performance levels and their reliability is defined to be the ability to meet a given demand. This reliability is evaluated by using the universal generating function technique. An optimization method based on the extended great deluge algorithm is proposed. This method has the advantage over other methods to be simple and requires less effort for its implementation. The developed algorithm is compared to than in Levitin and Lisnianski [Optimization of imperfect preventive maintenance for multi-state systems. Reliab Eng Syst Saf 2000;67:193–203] by using a reference example and two newly generated examples. This comparison shows that the extended great deluge gives the best solutions (i.e. those with minimal costs) for 8 instances among 10.     

Joint optimization of preventive maintenance and flexible flowshop sequence-dependent group scheduling considering multiple setups

This study focuses on a joint optimization problem regarding preventive maintenance (PM) and non-permutation group scheduling for a flexible flowshop manufacturing cell in order to minimize makespan. A mixed-integer linear programming model for the investigated problem is developed, which features the consideration of multiple setups, the relaxation of group technology assumptions, and the integration of group scheduling and PM. Based on the model, a lower bounding technique is presented to evaluate the quality of solutions. Furthermore, a genetic algorithm (GA) is proposed to improve computational efficiency. In the GA, a threshold-oriented PM policy, a hybrid crossover and a group swap mutation operator are applied. Numerical experiments are conducted on 45 test problems with various scales. The results show that the proposed model can remarkably reduce makespan. Comparative experiments reveal that the GA outperforms CPLEX, particle swarm optimization and cuckoo search with respect to effectiveness and efficiency.     

Joint optimization of preventive maintenance and inventory control in a production line using simulation

This paper proposes an integrated method for preventive maintenance and inventory control of a production line, composed of n machines (n?≥?1) without intermediate buffers. The machines are subject to failures and an age-dependent preventive maintenance policy is used. Approximate analytical results are proposed for the one machine case. Simulation software is used to model and simulate the behaviour of the production line of n machines under various maintenance and inventory control strategies. A methodology combining the simulation and genetic algorithms is proposed jointly to optimize maintenance and inventory control policies. Results are compared with the analytical solutions.     

Simultaneous determination of preventive maintenance and replacement policies in a queue-like production system with minimal repair

This paper addresses the joint effects of preventive maintenance and replacement policies on a queue-like production system with minimal repair at failures. We consider a policy which calls for a preventive maintenance operation whenever N parts have been processed. If a failure occurs and at least K preventive maintenance operations have been carried out, the system is replaced by a new one. Otherwise, a failure is handled by minimal repair. An analytical model is developed and the argument of renewal–reward theory is used to provide long-run expected profit per unit time for a given maintenance and replacement policy. Numerical examples are given to provide some managerial insights.     

Gradient-based multiobjective optimization using a distance constraint technique and point replacement

This paper proposes techniques to improve the diversity of the searching points during the optimization process in an Aggregative Gradient-based Multiobjective Optimization (AGMO) method, so that well-distributed Pareto solutions are obtained. First to be discussed is a distance constraint technique, applied among searching points in the objective space when updating design variables, that maintains a minimum distance between the points. Next, a scheme is introduced that deals with updated points that violate the distance constraint, by deleting the offending points and introducing new points in areas of the objective space where searching points are sparsely distributed. Finally, the proposed method is applied to example problems to illustrate its effectiveness.     

A Contribution to Hologram Imagery

A very simple method for evaluating hologram imagery in a general holographic arrangement with spherical or plane reference and reconstruction waves is proposed. For the hologram imagery approaching aberration-free case the relations providing new possibilities are introduced. The study is not restricted to plane holograms only but one can also study the imaging of objects reconstructed from volume holograms.     

Control-limit preventive maintenance policies for components subject to imperfect preventive maintenance and variable operational conditions

This paper develops two component-level control-limit preventive maintenance (PM) policies for systems subject to the joint effect of partial recovery PM acts (imperfect PM acts) and variable operational conditions, and investigates the properties of the proposed policies. The extended proportional hazards model (EPHM) is used to model the system failure likelihood influenced by both factors. Several numerical experiments are conducted for policy property analysis, using real lifetime and operational condition data and typical characterization of imperfect PM acts and maintenance durations. The experimental results demonstrate the necessity of considering both factors when they do exist, characterize the joint effect of the two factors on the performance of an optimized PM policy, and explore the influence of the loading sequence of time-varying operational conditions on the performance of an optimized PM policy. The proposed policies extend the applicability of PM optimization techniques.     

Contribution to quantification of highly inhomogeneous microstructures

In highly inhomogeneous microstructures with a wide range of grain sizes, the problem arises of defining an effective average grain size. An original method for the estimation of effective average grain size and a parameter describing the homogeneity of microstructure are presented. The coefficients of homogeneity for equiaxed and columnar microstructures are calculated. The effective average grain size is calculated as a weighted sum of the average grain sizes of homogeneous grain groups.     

Multi-objective design optimization of functionally graded material for the femoral component of a total knee replacement

The optimal design of complex systems in engineering requires pursuing rigorous mathematical modeling of the system’s behavior as a function of a set of design variables to achieve goal-oriented design. Despite the success of current knee implants, the limited life span remains the main concern of this complex system. The mismatch between the properties of engineered biomaterials and those of biological materials leads to insufficient bonding with bone, stress shielding effects and wear problems (i.e. aseptic loosening). The use of a functionally graded material (FGM) for the femoral component of knee implants is attractive because the properties can be designed to vary in a certain pattern to meet the desired requirements at different regions in the knee joint system, thereby decreasing loosening problem. However, matching the properties does not necessarily guarantee the best functionality of the knee implant and there is a need for developing the optimal design of an FGM femoral component that is longer lasting. In this study, therefore, a multi-objective design optimization of a FGM femoral component is carried out using finite element analysis (FEA) and response surface methodology (RSM). The results of using optimized FGM are then compared with the use of standard Co–Cr alloy in a femoral component knee implant to demonstrate relative performance.     

车身板件对车内噪声的贡献量分析

讨论车身板件对车内空腔辐射噪声的贡献量分析.通过对声源强度和声学传递函数的乘积求和来进行某块板在目标位置声压贡献量的合成.利用互异法间接测量声学传递函数,通过截面面积和其法向加速度的乘积得到声源强度值.模拟计算前面试验边界条件建立的有限元模型,有限元计算结果和实测数据进行对比.     

Contribution to the minimax evaluation of circularity error

The measurement of form and profile errors of mechanical parts involves the fitting of continuous surfaces, curves or lines to a set of coordinate points returned by inspection instrumentation. For circularity, both ASME (Y14.5M - 1994 [1995]) and ISO (1R 1001, 1983) prescribe the fitting of a pair of concentric circles with minimum radial separation to a set of discrete coordinate points sampled around the periphery of a surface of revolution at a plane perpendicular to the axis of revolution. This criterion is often referred to as the minimax or minimum zone criterion. The contribution of this paper is to provide a refinement to the basic results of both Ventura and Yeralan and Etesami and Qiao as an improved path to the optimum solution to the minimax roundness problem. The proposed solution applies the Voronoi diagram approach to the P2( k ) problem to show that the optimum solution can only reside at a particular type of vertex of the MAX region. This particular type of vertex corresponds to one of the possible type (ii) solutions of Ventura and Yeralan's P2( k ) problem. In other words, Etesami and Qiao's approach need not examine all vertices of the MAX region and Ventura and Yeralan's P2( k ) solution need not examine any type (i) solutions. This results in a substantial saving in time required to identify the optimum solution and would be useful for applications that return a large number of points such as dedicated roundness testers, axis of rotation analysers or telescopic ball bars.