A Study on Imperfect Production System Under Maintenance Strategies and Warranty

Author(s):  
Chandra K. Jaggi ◽  
Prerna Gautam ◽  
Aditi Khanna

In every production system, malfunctioning or breakdown during run time can incur heavy loss to the organization, to overcome such a situation it is crucial to use maintenance actions which can be either corrective or preventive depending upon the condition of the system. Also, warranty policy is extensively used world-wide to increase customer confidence in the product and to uplift sales. On account of this, the present chapter presents a problem of a manufacturer dealing with an imperfect production system considering maintenance actions and warranty policy by trading off the rework cost, holding cost, warranty cost and corrective/preventive maintenance cost so as to minimize the manufacturer's total cost. Numerical analysis and sensitivity analysis is performed to showcase model features.

Author(s):  
Guoqing Cheng ◽  
Binghai Zhou ◽  
Faqun Qi ◽  
Ling Li

In this article, we consider an imperfect production-inventory system which produces a single type of product to meet the constant demand. The system deteriorates stochastically with usage and the deterioration process is modeled by a non-stationary gamma process. The production process is imperfect which means that the system produces some non-conforming items and the product quality depends on the degradation level of the production system. To prevent the system from deteriorating worse and improve the product quality, preventive maintenance is performed when the level of the system degradation reaches a certain threshold. However, the preventive maintenance is imperfect which cannot restore the system as good as new. Hence, the aging system will be replaced by a new one after some production cycles. The preventive maintenance cost, the replacement cost, the production cost, the inventory holding cost and the penalty cost of lost sales are considered in this article. The objective is to minimize the total cost per unit item which depends on two decision variables: the preventive maintenance threshold and the time at which the system is replaced. We derive the explicit expression of the total cost per unit item and the optimal joint policy can be obtained numerically. An illustrative example and sensitivity analysis are given to demonstrate the proposed model.


2020 ◽  
Vol 30 (3) ◽  
pp. 339-360
Author(s):  
Aditi Khanna ◽  
Prerna Gautam ◽  
Ahmad Hasan ◽  
Chandra Jaggi

The present paper considers the effect of imperfect quality items on a production system which further undergoes inspection and rework. The demand of the product is price reliant. Two situations to handle the imperfect items are analyzed: selling them at a reduced price and reworking them. The demand is assumed to meet with perfect products in either case. Further, the study incorporates the carbon-emissions borne during production of goods and their holding in the inventory system. The model aims at maximizing the profit function by jointly optimizing mark-up price and production quantity. To demonstrate model characteristics, numerical and sensitivity analysis are also presented.


Author(s):  
Leonardo R. Rodrigues

This paper presents a method to define the optimal maintenance scope of a production system consisting of multiple k-out-of-n systems connected in series. Maintenance recommendations are based on Remaining Useful Life (RUL) predictions obtained from a Prognostics and Health Management (PHM) system for each production unit within the production system. Defining the techniques applied in order to estimate the degradation level of production units is out of the scope of this paper. It is assumed here that a PHM system is available and provides the degradation level and RUL estimates for each production unit. The goal is to find the maintenance scope that minimizes the expected total cost per cycle until the next maintenance activity. A k-out-of-n load-sharing system is assumed, which means that the failure of a production unit results in a higher load (and consequently a higher degradation rate) on the surviving production units. The total cost comprises the production cost and the maintenance cost. Production cost of each k-out-of-n system is also affected by the number of surviving production units. A preventive maintenance cost is incurred to maintain a degraded but still functional production unit. A corrective maintenance cost is incurredto maintain a failed production unit. An Ant Colony Optimization (ACO) approach is adopted, which allows the proposed method to deal with large instances of the problem. A numerical example is presented to illustrate the application of the proposed method.


Author(s):  
Anshu Dai ◽  
Guanzhou Wei ◽  
Zhaomin Zhang ◽  
Shuguang He

Offering appropriate preventive maintenance strategy has an effective impact on achieving higher customer satisfaction. This article presents a flexible preventive maintenance strategy, where customers are grouped into two groups according to their usage rates, and then different preventive maintenance programs are applied to different types of customers. The product follows a two-dimensional failure process and the preventive maintenance cost is shared between the manufacturer and customer on a pro rata basis. The manufacturer’s warranty cost is minimized by jointly optimizing the pro rata proportion of the preventive maintenance cost, the preventive maintenance number and the corresponding preventive maintenance levels. Numerical experiments are presented to illustrate the effectiveness of the proposed approach. Results prove that the flexible preventive maintenance strategy outperforms the conventional unified preventive maintenance strategy in terms of total warranty cost. Besides, some managerial suggestions are also given to provide guidance of implementing the proposed preventive maintenance strategy.


2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Rongcai Wang ◽  
Zhonghua Cheng ◽  
Enzhi Dong ◽  
Chiming Guo ◽  
Liqing Rong

Maintenance usually plays a key role in controlling a multi-component production system within normal operations. Furthermore, the failure of components in the production system will also cause large economic losses for users due to the shutdown. Meanwhile, manufacturers of the production system will be confronted with the challenges of the warranty cost. Therefore, it is of great significance to optimize the maintenance strategy to reduce the downtime and warranty cost of the system. Opportunistic maintenance (OM) is a quite important solution to reduce the maintenance cost and improve the system performance. This paper studies the OM problem for multi-component systems with economic dependence under base warranty (BW). The irregular imperfect preventive maintenance (PM) is performed to reduce the failure rate of components at a certain PM reliability threshold. Moreover, the OM optimization model is developed to minimize the maintenance cost under the optimal OM reliability threshold of each component. A simulated annealing (SA) algorithm is proposed to determine the optimal maintenance cost of the system and the optimal OM threshold under BW. Finally, a numerical example of a belt conveyor drive device in a port is introduced to demonstrate the feasibility and advantages of the proposed model in maintenance cost optimization.


2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Qian Wang ◽  
Zhonghua Cheng ◽  
Qintao Gan ◽  
Yongsheng Bai ◽  
Jianqing Zhang

Aiming to reduce the warranty cost, we put forward a new warranty strategy of two-dimensional (2D) warranty products. In this strategy, the incomplete preventive maintenance and minimal repair are proposed where the preventive maintenance is further divided by the degree of maintenance, and all other failures are repaired minimally. Preventive maintenance of different degree is put forward by the manufacturer and the user, respectively, and the repair factor is used to distinguish the different degree of maintenance. We establish model of warranty cost based on reliability theory and propose a method to solve the model. Finally, the validity of this model is proved by a numerical example, and the sensitivity analysis is carried out.


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