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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.

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.

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.

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.

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.

Regulators can adjust penalties to compensate for incomplete monitoring of regulated parties that are subject to legal rules, but compensating penalty adjustments often are unavailable when regulated parties are subject to legal standards. Incomplete monitoring consequently invites greater noncompliance under standards than under rules. This chapter develops a model that quantifies some of the specific tradeoffs that regulators face in designing standards regimes under incomplete monitoring. The model also considers the extent to which suboptimal compliance due to incomplete monitoring is likely to result in deadweight loss in different settings.

The purpose of this paper is to make economic comparison of the proposed X¯ chart with the economic and economic‐statistical design of a multivariate exponentially weighted moving average (MEWMA) control chart proposed by Linderman and Love, using Lorenzen‐Vance cost model.

The economic design of proposed X¯ chart, using Lorenzen‐Vance cost model, is discussed in the paper. It is observed that sampling interval (h) and expected cost/hour (C) depend on various parameters of the chart, used in this model. When there is any change in any parameter of the chart, obviously both sampling interval and expected cost will be different. So it is suggested that one should use Lorenzen and Vance cost model (equation 1) to compute sampling interval and expected cost/hour for the proposed X¯ chart.

The economic design of the proposed X¯ chart has been compared with the economic and economic‐statistical design of the multivariate exponentially weighted moving average (MEWMA) control chart proposed by Linderman and Love. It is found that the proposed X¯ chart performs better than MEWMA chart proposed by Linderman and Love for sample sizes of 7, 9 and 10 for first set of parameters. The proposed X¯ chart also shows lower expected cost/hour than the MEWMA chart for sample size of 2 and 3 and for shifts of 2 and 3 for the second set of parameters.

A lot of effort has been made to develop the proposed X¯ chart for monitoring the process mean. Although optimal sampling intervals are calculated only for two sets of parameters for shifts in the process average of 1, 2 and 3, it can be computed for any set of parameters using the Lorenzen‐Vance cost model.

The research findings could be applied to various manufacturing and service industries, as it is more effective than the Shewhart and EWMA charts.

Various types of warranty programs are offered for consumer products. The two most common are a linear pro-rata warranty or a lump-sum warranty, if product failure occurs prior to the specified warranty time. In this chapter we consider additional types of warranty programs that allow the consumer to purchase a one-time extended warranty in the event of no failure within the initial warranty period. For the extended period, warranty may be linearly pro-rated, starting at an amount that is lower than the initial purchase price. Alternatively, for the extended period, warranty may be a lump-sum amount, that is less than the initial warranty amount. Expressions for the expected costs under each of the programs are derived. Guidelines are provided for determining the parameters of each warranty program under relevant constraints. Sensitivity analysis is also conducted to determine the effect of the problem parameters on the expected warranty costs.

The purpose of this article is to compare maintenance policies based on Weibull and q-Weibull models.

This paper uses analytical developments, several figures and tables for graphical and numerical comparison. Previously published hydropower equipment data are used as examples.

Models for optimal maintenance interval determination based on q-Weibull distribution were defined. Closed-form expressions were found, and this allows the application of the method with small computational effort.

The use of the q-Weibull model to guide the definition of maintenance strategy allows decision-making to be more consistent with sample data. The flexibility of the q-Weibull model is able to produce failure rate modeling with five different formats: decreasing, constant, increasing, unimodal and U-shaped. In this way, the maintenance strategies resulting from this model should be more assertive.

Expressions for determining the optimal interval of preventive maintenance were deduced from q-Weibull distribution. Expected costs per maintenance cycle of Brazilian hydropower equipment were calculated with q-Weibull and Weibull distributions. These results were compared in terms of absolute values and trends. Although a large number of works on corrective and preventive maintenance have been proposed, no applications of the q-Weibull distribution were found in literature.

Due to a lack of data, many maintenance optimisation models have to be initialised on the basis of expert judgment. Rather than eliciting the parameters of a continuous lifetime distribution, experts give more reliable answers when assessing a discrete lifetime distribution. If the prior uncertainty in the probabilities of failure per unit time is expressed in terms of a Dirichlet distribution, Bayes estimates can be obtained of three cost‐based criteria to compare maintenance decisions over unbounded time‐horizons: first, the expected average costs per unit time; second, the expected discounted costs over an unbounded horizon; and third, the expected equivalent average costs per unit time. Illustrates the maintenance model by determining optimal age replacement and lifecycle costing policies, which optimally balance both the failure cost against the preventive repair cost, and the initial cost against the future cost.

Researchers and policymakers are figuring out the adaptation technologies to cope with the changing climate. Adaptation strategies for crop production followed by the farmers at selected study locations had ranged from 6-30 per cent only, and this was mainly due to lack of awareness about the actual cost associated with adaptation and non-adaptation of these strategies.

Hence, this study aims to address the cost of adaptation for rice using joint probability distribution of rainfall and crop prices.

Cost of adaptation varied from INR2,389 to 4,395/ha for System of Rice Intensification (SRI); INR646 to 1,121/ha for alternate wetting and drying (AWD) and INR8,144 to 8,677/ha for well irrigation (WI), whereas expected cost for not using these technologies has ranged from INR6,976 to 9,172/ha for SRI; INR4,123 7,764/ha for AWD and INR10,825 to 17,270/ha for WI. Hence, promotion of the adaptation technologies itself will minimize the income losses to the farmers.

Even though, there are many ways for farmers (other than technology), to adapt to climate change (such as out-migration to cities, selling farm assets, focus on children’s education, etc.), this report, given the framework of the major research study undertaken, addresses only farm-level adaptation of the technologies to enhance farm income.

Public–private partnership in providing the technologies at cheaper costs, capacity building in handling the technologies and creating awareness about the technologies to minimize the expected cost of adaptation are suggested to improve the adoption level.

The purpose of this paper is to review and develop stochastic models for the evaluation of the expected warranty cost, from dealer's viewpoint, for second hand items sold under different warranty policies.

This study makes a useful contribution to the warranty literature by developing a framework to study one dimensional warranty policies for second‐hand products from dealer's viewpoint. Also, numerical examples are provided to illustrate the application of these models.

Despite the fact that warranties for second‐hand products are commonly used, the accurate pricing of warranties in many situations remains an unsolved problem, for both the dealer and customer. The proposed framework allows the dealer to analyze the cost of alternative warranty policies before deciding on the policy to be offered with the sale of a second‐hand item.

The policies and models developed in this paper can be useful in managerial decisions making related to second‐hand products such as automobiles, home appliances, helicopters, electronic equipment and electronics.

In this paper we discuss a mechanism for making business decisions on the basis of an expected penalty function associated with cost variance. We assume that the decision maker is knowledgeable of the economic environment in which the decision will be made, but that he has no hard data” such as a market research report. In this setting fuzzy logic is more applicable than ordinary statistical decision theory. We develop a method of computing a fuzzy expected penalty based on a fuzzy distribution of cost variance and a fuzzy penalty function. These fuzzy expected penalties are then defuzzified” so that a non‐fuzzy decision can be made.

Cost models for the design of control charts based on Duncan’s approach have been studied in recent years. Presents a double assignable‐cause cost model, which is in terms of Taguchi’s loss imparted to society from the time a product is shipped, using renewal theory approach. The expression for the expected cycle length and the expected cost per cycle are easier to obtain by the proposed approach, and the cost model, including the customer’s voice, reveals the importance of quality. Sensitivity analysis performed on a large number of numerical examples illustrates that the cost of repair or replacement and customers’ tolerance, which are related to loss function, are critical when designing economically based on X‐ and S control charts.