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In view of the high complexity of the objective world, an economic dependence between subsystems (paired and unpaired) is proposed, and then the maintenance cost and time under different economic dependences are formulated in a simple and consistent manner. Selective maintenance problem under economic dependence (EDSMP) is presented based on a series–parallel system in this paper. A case study shows that the system reliability is promoted to a certain extent, which can validate the validity of the EDSMP model. The influence of the ratio of set-up cost on system performance is mainly discussed under different economic dependences. Several existing improvements of classical exhaust algorithm are further modified to solve a large sized EDSMP rapidly. Experimental results illustrate that these improvements can reduce CPU time significantly. Furthermore the contribution of each improvement is defined here, and then their contributions are compared thoroughly.

Purpose – The traditional practice for maintenance, quality control and production scheduling is to plan independently irrespective of an interrelationship exist between them. The purpose of this paper is to develop an approach for integrating maintenance, quality control and production scheduling. The objective is to investigate the benefits of the integrated effect in terms of the expected total cost of system operation of the three functions. Design/methodology/approach – The proposed approach is based on the conditional reliability of the components. Cost model for integrating selective maintenance, quality control using sampling-based procedure and production scheduling is developed using the conditional reliability. An integrated approach is such that, first an optimal schedule for the batches to be processed is obtained independently while the maintenance and quality control decisions are optimized considering the optimal schedule on the machine. The expected total cost of conventional approach, i.e. “No integration” is calculated to compare the effectiveness of integrated approach. Findings – The integrated approach have shown a higher cost saving as compared to the independent planning approach. The approach is practical to implement as the results are obtained in a reasonable computational time. Practical implications – The approach presented in this paper is generic and can be applied at planned as well as unplanned opportunities. The proposed integrated approach is dynamic in nature, as during maintenance opportunities, it is possible to optimize the decision on maintenance, quality control and production scheduling considering the current age of components and production requirement. Originality/value – The originality of the paper is in the approach for integration of the three elements of shop floor operations that are usually treated separately and rarely touched upon by researchers in the literature.

Decline of motor performance in older individuals affects their quality of life. Understanding the contribution of sport-related training in advanced ages might help to attenuate motor performance decay as one gets older. The purpose of our study was to evaluate the extent to which long-lasting training in running or sport-specific skills during old age preserves motor performance in different motor tasks.Older runners and tennis players with at least 10 years of training were assessed as were age-matched and young exercisers. Performance was evaluated for 6 motor tasks requiring different functions of sensorimotor control expected to decline with aging.Analysis revealed that runners had increased aerobic fitness in comparison with the other older participants and that they presented similar performance to older exercisers in the motor tasks. Tennis players outperformed the other groups of older participants on coincident timing and simple reaction time and achieved similar performance to the young group on the timing task.These results suggest selective maintenance of task-specific processing through extensive practice of tennis-related motor skills in older adults.

In this paper, we will consider the selective maintenance problem for multistate series systems with stochastic dependent components. In multistate systems, the health state of a component may vary from perfect functioning to complete failure. The stochastic dependence (S-dependence) between components is discussed and categorized into two types in multistate context. First, the failure of a component can immediately cause complete failures of some other components in the system. Second, as components deteriorate, the reduced working performance rate of a multistate component affects the state as well as the degradation rate of its subsequent components in series structure. The system reliability is evaluated using an approach based on stochastic process. A cost-based selective maintenance model is developed for the multistate system with S-dependent components to maximize the total system profit, which includes the production gain and loss in the next mission as well as possible maintenance costs for the system. Analyses of systems with independent and dependent components are provided. It is observed that ignoring S-dependence in the system may lead to alternative maintenance decision making and an optimistic estimation of the system performance.

Performance of a production system significantly depends upon the effective planning of its shop floor level operations like, production scheduling, maintenance and quality control. These three functions have an interaction effect on each other; hence a combined operational policy of these functions can improve the system performance. The objective of this paper is to develop a methodology for optimizing maintenance and quality plan with the constraint on schedule, availability, repair time and detection time, for a single machine. The approach includes the selective maintenance and a sampling procedure based on the economic design principle. Cost models developed for the problem at hand are presented along with their detailed explanation. Two solution methodologies namely, Simulated Annealing (SA) and Genetic Algorithm (GA) are used for obtaining the near optimal solution. A computational experiment to compare the performance of SA and GA is presented. Results indicate a better performance of the proposed approach over the conventional approach of independent planning.

In many industrial settings, there are systems designed to perform consecutive missions interspersed with finite breaks during which only a set of component repairs can be carried out due to limited time, budget, or resources. The decision maker then has to decide which components to repair in order to guarantee a given performance level. This is known as the selective maintenance problem (SMP). This paper introduces a new variant of the SMP by specifically taking into account the maintenance outsourcing alternative. A novel integrated non-linear programming formulation where both the in-house and outsourcing maintenance alternatives are accounted for is developed and optimally solved. The effect of the outsourcing alternative on maintenance decisions is investigated through numerical experiments. The overall results obtained demonstrate the validity of the proposed approach. (C) 2018, IFAC (International Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

In natural environments with many options of uncertain value, one faces a difficult tradeoff between exploiting familiar, valuable options or searching for better alternatives. Reinforcement learning models of this exploration/exploitation dilemma typically modulate the rate of exploratory choices or preferentially sample uncertain options. The extent to which such models capture human behavior remains unclear, in part because they do not consider the constraints on remembering what is learned. Using reinforcement-based timing as a motivating example, we show that selectively maintaining high-value actions compresses the amount of information to be tracked in learning, as quantified by Shannon's entropy. In turn, the information content of the value representation controls the balance between exploration (high entropy) and exploitation (low entropy). Selectively maintaining preferred action values while allowing others to decay renders the choices increasingly exploitative across learning episodes. To adjudicate among alternative maintenance and sampling strategies, we developed a new reinforcement learning model, StrategiC ExPloration/ExPloitation of Temporal Instrumental Contingencies (SCEPTIC). In computational studies, a resource-rational selective maintenance approach was as successful as more resource-intensive strategies. Furthermore, human behavior was consistent with selective maintenance; information compression was most pronounced in subjects with superior performance and non-verbal intelligence, and in learnable vs. unlearnable contingencies. Cognitively demanding uncertainty-directed exploration recovered a more accurate representation in simulations with no foraging advantage and was strongly unsupported in our human study.

This paper studies the selective maintenance problem for multi-state series systems working in variable loading conditions in the next mission. In the mission, a component's degradation depends on its current state and the load applied on it. A load-dependent degradation model is proposed for multi-state components operating in variable loading conditions. This model is inspired by the load-sharing model where many components share a common workload and the failure rate of a component depends on the state of other components. A Monte-Carlo simulation method is presented to simulate the multi-state component's degradation and to evaluate the system reliability. The final objective is to determine the best selective maintenance strategy to maximize the expected system reliability in the next mission within available resources. An illustrative example, reliability estimation results, and analysis of optimal selective maintenance scenarios for different levels of budget limitation are provided.

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.

Fleet-level selective maintenance refers to the process of identifying the subset of maintenance actions to perform on a fleet of repairable systems when the maintenance resources allocated to the fleet are insufficient for performing all desirable maintenance actions. The original fleet-level selective maintenance model is designed to maximize the probability that all missions in a future set are completed successfully. We extend this model in several ways. First, we consider a cost-based optimization model and show that a special case of this model maximizes the expected value of the number of successful missions in the future set. We also consider the situation in which one or more of the future missions may be canceled. These models and the original fleet-level selective maintenance optimization models are nonlinear. Therefore, we also consider an alternative model in which the objective function can be linearized. We show that the alternative model is a good approximation to the other models.

In this paper, we provided a selective maintenance model for complex system with multilevel structure. Our selective maintenance model is so special that we consider the fact when a specific component is maintained so that the subsystems must be removed. We consider that the components have two kind of status, working or failed. We consider three different maintenance actions for the components with different status. The selective maintenance model is formulated as a non-linear programming problem. The objective is to maximum reliability by maintaining part of components of the system in a limited cost. We provided a numerical example to illustrate the effectiveness of the model.

This paper presents a study on selective maintenance for multi-state series–parallel systems with economically dependent components. In the selective maintenance problem, the maintenance manager has to decide which components should receive maintenance activities within a finite break between missions. All the system reliabilities in the next operating mission, the available budget and the maintenance time for each component from its current state to a higher state are taken into account in the optimization models. In addition, the components in series–parallel systems are considered to be economically dependent. Time and cost savings will be achieved when several components are simultaneously repaired in a selective maintenance strategy. As the number of repaired components increases, the saved time and cost will also increase due to the share of setting up between components and another additional reduction amount resulting from the repair of multiple identical components. Different optimization models are derived to find the best maintenance strategy for multi-state series–parallel systems. A genetic algorithm is used to solve the optimization models. The decision makers may select different components to be repaired to different working states based on the maintenance objective, resource availabilities and how dependent the repair time and cost of each component are.

In many industrial environments, maintenance is performed during successive mission breaks. In these conditions, it may not be feasible to perform all possible maintenance actions due to limited maintenance resources such as time, budget, repairman availability, etc. A subset of maintenance actions is then performed on selected components such that the system is able to meet the next mission requirement. Such a maintenance policy is called selective maintenance. In this article, a selective maintenance strategy is developed for a MultiState System (MSS). The system can have several finite levels of performance in an MSS. Previous studies on selective maintenance have solely focused on MSSs with binary components. However, components in an MSS may be in more than two possible states. Hence, a series-parallel MSS that consists of multistate components is considered in this article. Imperfect maintenance of a component is considered to be a maintenance option, along with the replacement and the do-nothing op...

A cost-based selective maintenance decision-making method was presented. The purpose of this method was to find an optimal choice of maintenance actions to be performed on a selected group of machines for manufacturing systems. The arithmetic reduction of intensity model was introduced to describe the influence on machine failure intensity by different maintenance actions (preventive maintenance, minimal repair and overhaul). In the meantime, a resolution algorithm combining the greedy heuristic rules with genetic algorithm was provided. Finally, a case study of the maintenance decision-making problem of automobile workshop was given. Furthermore, the case study demonstrates the practicability of this method.

Through analyzing maintenance characteristics of various phases in life cycle, it can be found maintenance is essential in each phase of the system. Therefore, it is important to change the traditional maintenance concept, research on the selective maintenance system in life cycle, instead of considering maintenance only in running phase. Based on the PHM (prognostic and health management) in marine power system, defines optimization maintenance timing of the equipment as objective, and set reliability and economy as evaluation criteria for the maintenance program, this article establishes selective maintenance system for marine power system in life cycle.

This paper considers a selective maintenance policy for multi-component systems for which a minimum level of reliability is required for each mission. Such systems need to be maintained between consecutive missions. The proposed strategy aims at selecting the components to be maintained (renewed) after the completion of each mission such that a required reliability level is warranted up to the next stop with the minimum cost, taking into account the time period allotted for maintenance between missions and the possibility to extend it while paying a penalty cost. This strategy is applied to binary-state systems subject to propagated failures with global effect, and failure isolation phenomena. A set of rules to reduce the solutions space for such complex systems is developed. A numerical example is presented to illustrate the modeling approach and the use of the reduction rules. Finally, the Monte-Carlo simulation is used in combination with the selective maintenance optimization model to deal with a number of successive missions.

In many situations, systems are required to perform maintenance actions at the planned down time. Because of the limitation of down time, it is often impossible to perform maintenance action for all components in system. Therefore, the maintenance manager should make a decision to choose part of the components to be maintained in the limited down time. This problem is called selective maintenance. In this paper, we provide a selective maintenance model for modular system. We formulate the selective maintenance model as a non-linear programming problem. The maximum reliability gained by maintaining the modular system is formulated as objective. And we show the effectiveness of the proposed model via a numerical example.

This paper deals with the selective maintenance problem for a multi-component system performing consecutive missions separated by scheduled breaks. To increase the probability of successfully completing its next mission, the system components are maintained during the break. A list of potential imperfect maintenance actions on each component, ranging from minimal repair to replacement is available. The general hybrid hazard rate approach is used to model the reliability improvement of the system components. Durations of the maintenance actions, the mission and the breaks are stochastic with known probability distributions. The resulting optimisation problem is modelled as a non-linear stochastic programme. Its objective is to determine a cost-optimal subset of maintenance actions to be performed on the components given the limited stochastic duration of the break and the minimum system reliability level required to complete the next mission. The fundamental concepts and relevant parameters of this decision-making problem are developed and discussed. Numerical experiments are provided to demonstrate the added value of solving this selective maintenance problem as a stochastic optimisation programme.

Structures of strategic importance, such as bridges, require careful planning in terms of reliability, durability and safety, qualities which must be guaranteed throughout the entire life cycle of the structure. However, due to the ageing of materials and to aggressive environmental actions which cause deterioration, the response of these structures, just like others, changes over time, resulting in a loss of performance. Yet it is important to maintain a satisfactory level of performance in a bridge throughout its service. To ensure such a performance it is important to apply properly planned maintenance strategies. Appropriate maintenance strategies require knowledge of the process of deterioration and the consequent damages to be expected in order to schedule proper maintenance procedures. It would be fundamental to define a selective maintenance plan which may involve only some parts of the structure, thus allowing bridge viability even during the maintenance activity. This paper proposes the study of strategies of selective maintenance for a steel bridge immersed in an aggressive environment, starting from the simulation of each individual member. Simulation of deterioration is obtained through the application of an appropriate damage law implemented with a Monte Carlo methodology, while the time prediction of occurrence of the deterioration is obtained through the application of a Markovian probabilistic approach. The results of the Markovian approach were the starting point for choosing strategies of selective maintenance, as the Markov process allowed the identification, in probabilistic terms, of the structure members with the highest risk of collapse and the timing for achieving levels of damage related to the possible collapse of compromised members. This timing was used to identify possible intervals of maintenance. Proposed scenarios are compared with each other both in terms of associated risk, and in terms of life-cycle cost effectiveness.

In this paper, we have used the selective maintenance policy to determine, how many components to be replaced & repaired within the limited time interval and cost which maximizes the system's availability. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.

In this paper, we obtain optimum allocation of replaceable and repairable components in a system design. When repair and replace time are considered as random in the constraints. We convert probabilistic constraint into an equivalent deterministic constraint by using chance constrained programming. We have used the selective maintenance policy to determine how many components to be replaced & repaired within the limited maintenance time interval and cost. A Numerical example is presented to illustrate the computational procedure and problem is solved by using LINGO Software.

Many systems are required to perform maintenance actions at the breaks between successive missions. However, it is often impossible to perform all the desirable maintenance actions due to the limited time of the breaks. In such a case, many selective maintenance models were proposed for this problem. In this paper, we propose a selective maintenance model considering the different mission duration time for different subsystems. We formulate the selective maintenance model as a non-linear 0–1 programming problem. The maximum reliability of the next mission is formulated as objective. The effectiveness of the proposed model is demonstrated via a numerical example.

In the area with subway engineering, maintenance optimization is an important issue for engineers and subway administrations because these actions allow maintenance costs to be reduced with acquired reliability. Aiming to optimize the maintenance strategy of subway vehicles, this paper presents a multi-objective maintenance model of cost effectiveness. Moreover, there is no doubt that subway vehicles have more than two statuses, rather than simple linear 0-1 statuses (working or failure). This paper develops a multi-status subway vehicle model to reflect the reality. A multi-population genetic algorithm is employed to solve the non-linear model. Additionally, to subsidize the optimized maintenance strategy, this paper studies a case of driven locking mechanism subsystem in subway vehicle. The result shows the advantages of optimized selective maintenance. The maintenance strategy proposed in this paper reveals a new method of subway maintenance.

Bending tools are frequently used to manufacture freeform surfaces. In such cases, the geometry of the workpiece is determined wholly or in part by that of the tool (e.g. die forming). The quality of the finished product depends largely, therefore, on the geometry of the tools. Tool geometry is changed in the course of the manufacturing process by wear and optimisation. When the time comes for repairs or the manufacture of new tools, there is no record of the geometrical modifications which have been carried out. A model in the form of a quality control loop to feed back geometrical modifications to CAD and CAM systems is presented in this contribution. A means of creating the individual components in the quality control loop is described. Particular care has been taken to ensure that both the measurement and the feedback are limited to selected areas in order to minimise the cost and time involved.

In many applications, the break between successive missions provides an opportunity to perform maintenance under limited resources. Such maintenance policy is called selective maintenance. Traditionally, it was assumed that a component after repair may be as good as new or as bad as old. However, maintenance can bring a component in between these two extreme cases as well. This maintenance policy is called imperfect repair. Selective maintenance optimization under imperfect repair is studied in this paper. Age reduction model is used to represent imperfect repair for selective maintenance. It is suggested in [1] that age reduction factor depends on the maintenance cost, and a constant is used to reflect whether a component is relatively young or old. In this paper, a formulation is used for this characteristic constant which depends on the effective age of the component. It is shown that the formulation helps in establishing a relationship between age reduction factor, cost of maintenance, and effective age of the component. Also, advantage of selective maintenance with imperfect repair is shown and results are compared with the case when only minimal repair and replacement are considered as repair options.

Many engineering systems are exposed to run in load-varying environment. Load has a significant impact on the deteriorating process of components and system. Distributing the loads optimally among components of a system can reduce the failure probability and prolong the system life. In this paper, an approach to load distribution for multi-state systems under selective maintenance strategy is developed. In addition to optimally allocating the limited maintenance budget among components, the proposed method also provides a feasible way to distribute loads imposed on components so as to maximize the probability of system successfully completing the next mission. The resulting optimization problem is solved by the genetic algorithm (GA). As observed from our comparative study, the proposed method always yields a better result that the existing method which doesn't take load distribution into account.

The primary objective of this research was to develop a modeling-based methodology for managing selective maintenance decisions when the planning horizon is more than one future mission. First, the research literature for selective maintenance is presented. The selective maintenance literature is limited in that current models only consider a single, future mission. Next, background research is presented in which the selective maintenance model treating decision-making relative to a single, future mission is defined. This model serves as the foundation for our multi-mission analysis. In this work, we define a scenario in which a system must perform a sequence of missions. The reliability characteristics of the system are defined, and we extend the single- mission scenario parameters such that decision variables for one mission are the input parameters for the next mission. An objective function that maximizes the expected number of successful missions remaining in the planning horizon is defined. Finally, we formulate a stochastic dynamic programming model to solve the multi-mission scenario and present a numerical example. This example shows that the selective maintenance decisions relative to a multi-mission scenario may differ from the decisions for a single-mission scenario.

The objective of this research is to investigate the use of a mathematical modeling methodology for integrating maintenance planning and sortie scheduling issues. First, the relevant research literature for both selective maintenance and fleet assignment is presented. Next, background research is presented, which extends a current selective maintenance model to incorporate sets of systems. Here a selective maintenance model for a set of systems that must execute a set of missions with system maintenance performed only between missions is defined. Finally, we formulate a more complex optimization model that addresses a more dynamic mission profile. Specifically, missions start and end at different times, and maintenance and scheduling decisions are made over a series of time "buckets." We consider a planning horizon such that each system in the set returns from its previous mission and begins its future mission; however, no system returns from its future before the end of the planning horizon.

Owing to limited maintenance resources, it may not be possible to perform all feasible maintenance actions on a system during the maintenance break between two successive missions. A subset of maintenance actions can then be selected from the available maintenance options so that the system can meet the next mission requirement. This maintenance policy is called selective maintenance. In this paper, selective maintenance has been performed for a system that has been subjected to the two types of failure modes: maintainable and the non-maintainable failure modes. The hazard rate due to the maintainable failure modes depends on the cumulative effect of the non-maintainable failure modes. A model has been used to show this relationship. An illustrative example has been used to demonstrate the effect of the two failure modes and the advantages of imperfect maintenance in selective maintenance decision making.

This chapter reviews general functional consequences of vegetative (in)compatibility in fungal populations and considers the question of to what extent these functional aspects may be responsible for the selective maintenance of the observed high levels of incompatibility. Special attention will be paid to the role of vegetative (in)compatibility in pathogenic fungi, especially in relation to biological control.