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The purpose of this paper is to explore the impact of the Kijima Type II imperfect repair model on the availability of repairable systems (RS). Since many individuals are interested in measuring the extent to which the system will be available after it has been run for a long time, the specific interest in this study is in the steady-state (limiting) availability behavior of such systems. Furthermore, the authors study the impact of age-based preventive maintenance (PM) on the RS performance.

Because of the complexity of the underlying assumptions of the Kijima Type II model, the authors use simulation modeling to estimate the system availability. Based on preliminary simulation results, the availability function achieves a steady-state value greater than zero. The system steady-state availability is then estimated from the simulation output by computing the average of the availability estimates beyond the initial transient period. Next, the authors develop a meta-model to convert the system reliability and maintainability parameters into the coefficients of the limiting availability estimate without the simulation effort. Using a circumscribed central composite experimental design, the authors confirm the accuracy of the meta-model.

The results show that the meta-model is robust, and provides good estimates of the system limiting availability. Also, the authors find that when using a Kijima Type II model for a system repair process, age-based PM can improve the steady-state availability value. Therefore, an optimal age-based PM policy that maximizes the system’s steady-state availability can be identified.

In practice, it is important to study the system steady-state availability because many individuals, i.e. engineers, are more interested in measuring the extent to which the system will be available after it has been run for a long time. Therefore, this study represents a significant addition to the body of knowledge related to virtual age modeling, in that it incorporates a Kijima type II model and considers system steady-state availability.

This paper seeks to improve solution procedures for solving a larger version of the integrated preventive maintenance planning and production scheduling model with a total weighted expected tardiness objective function introduced in a 2003 paper by Cassady and Kutanoglu using a genetic algorithm heuristic procedure.

In this paper, heuristics based on genetic algorithms are developed to solve the integrated model.

The performance of the proposed genetic algorithm heuristics are evaluated using multiple instances of several problem sizes. The results indicate that the proposed genetic algorithms can effectively be used to solve the integrated problem.

The heuristics presented in this paper significantly improve the ability of the decision‐maker to consider larger instances of the integrated model. One may ask, “how significant is that improvement?” The answer depends on the specific industrial context under consideration and the definition of a “job”.

Typically, production scheduling and preventive maintenance planning is planned and executed independently in spite of the inter‐dependent relationship between them. However, the 2003 paper by Cassady and Kutanoglu demonstrates the benefit of using the integrated model to solve these two problems simultaneously. However, their solution procedure is limited to small problems (6‐jobs or less). Therefore, this study intends to improve the solution procedure to solve larger instances of the problem.

The purpose of this paper is to develop an improved, enumerative solution procedure for solving the original selective maintenance problems. Selective maintenance refers to the process of identifying the set of maintenance actions to perform from a desirable set of maintenance actions.

A series of four improvements to a previously proposed enumerative solution procedure are presented. The improvements are defined and tested sequentially on an experimental set of problem instances. The improvements are characterized relative to the achieved reduction in CPU time for a software application.

The improved enumerative procedure reduces the CPU time required to solve the selective maintenance problems by as much as 99 per cent. There is a corresponding increase in practical problem size of more than 200 per cent.

Almost all organizations use a variety of repairable systems to achieve their mission. Typically, these systems have to share the limited maintenance resources possessed by the organization. Therefore, an improved ability to solve selective maintenance problem is relevant to many industries.

The body of knowledge relative to selective maintenance continues to grow. However, this is the first study aimed at improving the capability of engineers to solve practically‐sized selective maintenance problems.

In many industrial environments, systems are required to perform a sequence of operations (or missions) with finite breaks between each operation. During these breaks, it may be advantageous to perform repair on some of the system’s components. However, it may be impossible to perform all desirable maintenance activities prior to the beginning of the next mission due to limitations on maintenance resources. In this paper, a mathematical programming framework is established for assisting decision‐makers in determining the optimal subset of maintenance activities to perform prior to beginning the next mission. This decision‐making process is referred to as selective maintenance. The selective maintenance models presented allow the decision‐maker to consider limitations on maintenance time and budget, as well as the reliability of the system. Selective maintenance is an open research area that is consistent with the modern industrial objective of performing more intelligent and efficient maintenance.

The purpose of this paper is to develop a preventive maintenance (PM) model that encounters the problems of traditional methods of conducting PM within high component/machine variety environments.

A new platform to conduct planning of the PM actions by using clustering based on the Group Technology concept to create PM virtual cells of equipment/machines is introduced. A real case study at Arab Potash Company was used to illustrate the model. The component/machine variety that requires PM at the considered company is in thousands of items.

PM for high component/machine environments are not enough addressed in the literature. The concept of clustering and similarity coefficient was used and found very useful to model this situation.

The proposed procedure will assist maintenance managers/engineers in too many ways. It will help to optimize the inventory of the spare parts, and to create standard process plan for executing the preventive maintenance operation.

This paper presents a new procedure to implement preventive maintenance in high component/machine environments using clustering technique concept. Models that address this concept are rare and very limited in the literature.

The purpose of this paper is to develop an efficient and effective preventive maintenance (PM) plan that considers machines’ maintenance needs in addition to their reliability factor.

Similarity coefficient method in group technology (GT) philosophy is used. Machines’ reliability factor is considered to develop virtual machine cells based on their need for maintenance according to the type of failures they encounter.

Using similarity coefficient method in GT philosophy for PM planning results in grouping machines based on their common failures and maintenance needs. Using machines' reliability factor makes the plan more efficient since machines will be maintained at the same time intervals and when their maintenance is due. This helps to schedule a standard and efficient maintenance process where maintenance material, tools and labor are scheduled accordingly.

The proposed procedure will assist maintenance managers in developing an efficient and effective PM plans. These maintenance plans provide better inventory management for the maintenance materials and tools needed using the developed virtual machine cells.

This paper presents a new procedure to implement PM using the similarity coefficient method in GT. A new similarity coefficient equation that considers machines reliability is developed. Also a clustering algorithm that calculates the similarity between machine groups and form virtual machine cells is developed. A numerical example adopted from the literature is solved to demonstrate the proposed heuristic method.

Many jurisdictions fine illegal cartels using penalty guidelines that presume an arbitrary 10% overcharge. This article surveys more than 700 published economic studies and judicial decisions that contain 2,041 quantitative estimates of overcharges of hard-core cartels. The primary findings are: (1) the median average long-run overcharge for all types of cartels over all time periods is 23.0%; (2) the mean average is at least 49%; (3) overcharges reached their zenith in 1891–1945 and have trended downward ever since; (4) 6% of the cartel episodes are zero; (5) median overcharges of international-membership cartels are 38% higher than those of domestic cartels; (6) convicted cartels are on average 19% more effective at raising prices as unpunished cartels; (7) bid-rigging conduct displays 25% lower markups than price-fixing cartels; (8) contemporary cartels targeted by class actions have higher overcharges; and (9) when cartels operate at peak effectiveness, price changes are 60–80% higher than the whole episode. Historical penalty guidelines aimed at optimally deterring cartels are likely to be too low.

It is found that one unit root, common trend is shared by the quarterly auction price series of five frequently auctioned types of stamps. The common trends analysis provides specific, stationary linear combinations, or cointegrating portfolios, of the auction price levels. The quarterly returns for the system of cointegrated auction prices can be represented by an error correction model using past returns and cointegrating vectors. There is evidence of a positive relationship between changes in the common trend and leading changes in industrial production

Recently, American social behavior during the 1980s has been compared, both favorably and unfavorably, with the attitudes and culture of the United States during the years 1950–1959. The past two decades of rebellion, student protest, liberal sexual practices, radical politics, and strong civil and women's rights movements have all passed.

Greater fruit and vegetable (F&V) intake has been linked to more positive mood. Here, the purpose of this paper is to examine if this relationship is mediated by expectancies about their benefit to health/mental health.

Participants completed a new questionnaire to assess expectancies related to F&V intake. This was administered alongside a validated food-frequency measure of F&V intake, an assessment of positive and negative mood state and other measures.

Participants held strongly positive expectations about the physical and mental health benefits of consuming F&V. The authors observed a significant relationship between self-reported F&V intake and positive mood (d = 0.52). Importantly, this effect was largely (but not completely) independent of expectancies. The authors also observed that expectancies about F&V intake were independently predictive of positive mood (d = 0.47).

This is the first study to explore expectancy effects in the mental health benefits of F&V intake. These data suggest that positive expectancies about F&V intake, and F&V intake itself, are both predictive of positive mood. The former finding is probably a placebo effect, whereby people believe they are consuming sufficient F&V (even if they are not) and so experience mood-related benefits due to their positive expectations. The latter finding is consistent with F&V exerting a biologically beneficial effect on the brain.