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Article
Publication date: 15 March 2021

Sofiene Dellagi and Mohamed Noomane Darghouth

In this paper, a maintenance strategy based on improved imperfect maintenance actions with stochastic repair times for multiperiod randomly failing equipment is developed. The…

Abstract

Purpose

In this paper, a maintenance strategy based on improved imperfect maintenance actions with stochastic repair times for multiperiod randomly failing equipment is developed. The main objective is to minimize the total maintenance cost by jointly finding the optimal preventive maintenance (PM) cycle and planning horizon.

Design/methodology/approach

A model based on the mathematical theory of reliability is developed to minimize the total maintenance cost by jointly finding the optimal couple: PM cycle T* and planning horizon H*. The proposed model aims to characterize the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures. The conventional threshold accepting (TA) algorithm is implemented to solve the proposed model. A numerical example for a given set of input parameters is presented in order to show the usefulness of the proposed model. A sensitivity analysis of some of the key parameters is performed to demonstrate the coherence of the developed maintenance policy.

Findings

The obtained results showed a sensitive trade-off between PM frequency and the total maintenance cost. Performing PM actions more frequently helps significantly to reduce the expected number of corrective maintenance actions and the corresponding total cost. It has also been found that improving the efficiency of the PM actions allows for maintaining the equipment less frequently by increasing the time between successive PM actions.

Research limitations/implications

Given the complexity of the objective function to be minimized and the stochastic nature of the model's parameters, the authors limited this study to equally cyclic production periods over the planning horizon.

Practical implications

The present model aims to provide an integrated maintenance/production comprehensive framework to assist planners in establishing maintenance schedules considering multiperiod randomly failing production systems and the evolutionary impact of imperfect PM actions on the equipment failure rate.

Originality/value

Contrary to the majority of existing works in the literature dealing with maintenance strategies, the authors consider that repair times are stochastic to provide a more realistic framework. In addition, the developed model considers the impact of imperfect maintenance on the equipment's mean time to failure. Thus, the evolutionary impact of imperfect PM actions on the equipment failure rate and the resulting mean number of failures is characterized. Simultaneously, the production planning horizon along with the length of each PM cycle is optimized in order to minimize the total maintenance cost over the planning horizon.

Details

Journal of Quality in Maintenance Engineering, vol. 28 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 14 March 2016

M.N. Darghouth, Daoud Ait-Kadi and Anis Chelbi

The authors consider a system which is a part of a complex equipment (e.g. aircraft, automobile, medical equipment, production machine, etc.), and which consists of N independent…

Abstract

Purpose

The authors consider a system which is a part of a complex equipment (e.g. aircraft, automobile, medical equipment, production machine, etc.), and which consists of N independent series subsystems. The purpose of this paper is to determine simultaneously the system design (reliability) and its preventive maintenance (PM) replacements periodicity which minimize the total average cost per time unit over the equipment useful life, taking into account a minimum required reliability level between consecutive replacements.

Design/methodology/approach

The problem is tackled in the context of reliability-based design (RBD) considering at the same time the burn-in of components, the warranty commitment and the maintenance strategy to be adopted. A mathematical model is developed to express the total average cost per time unit to be minimized under a reliability constraint. The total average cost includes the cost of acquiring and assembling components, the burn-in of each component, preventive and corrective replacements performed during the warranty and post-warranty periods. A numerical procedure is proposed to solve the problem.

Findings

For any given set of input data including components reliability, their cost and the costs of their preventive and corrective replacements, the system design (reliability) and the periodicity of preventive replacement during the post-warranty period is obtained such as the system’s total average cost per time unit is minimized. The obtained results clearly indicate that a decrease in the number of PM actions to be performed during the post-warranty period increases the number of components to be added at each subsystem at the design stage.

Research limitations/implications

Given that the objective function (cost rate function) to be minimized is non-linear and involves several integer variables, it has not been possible to derive the optimal solution. A numerical procedure based on a heuristic approach has been proposed to solve the problem finding a nearly optimal solution for a given set of input data.

Practical implications

This paper offers to manufacturers a comprehensive approach to look for the most economical combination of the reliability level to be given to their products at the design stage, on one hand, and the PM policy to be adopted, on the other hand, given the offered warranty and service for the products and reliability requirements during the life cycle.

Originality/value

While the RBD problem has been largely treated, most of the published works have focussed on the development or the improvement of solving techniques used to find the optimal configuration. In this paper the authors provide a more comprehensive approach that considers simultaneously RBD, the burn-in and warranty periods, along with the maintenance policy to be adopted. The authors also consider the context of products whose component failures cannot be rectified through repair actions. They can only be fixed by replacement.

Details

Journal of Quality in Maintenance Engineering, vol. 22 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 5 August 2022

Monika Saini, Deepak Sinwar, Alapati Manas Swarith and Ashish Kumar

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of…

Abstract

Purpose

Reliability and maintainability estimation of any system depends on the identification of the best-fitted probability distribution of failure and repair rates. The parameters of the best-fitted probability distribution are also contributing significantly to reliability estimation. In this work, a case study of load haul dump (LHD) machines is illustrated that consider the optimization of failure and repair rate parameters using two well established metaheuristic approaches, namely, genetic algorithm (GA) and particle swarm optimization (PSO). This paper aims to analyze the aforementioned points.

Design/methodology/approach

The data on time between failures (TBF) and time to repairs (TTR) are collected for a LHD machine. The descriptive statistical analysis of TBF & TTR data is performed, trend and serial correlation tested and using Anderson–Darling (AD) value best-fitted distributions are identified for repair and failure times of various subsystems. The traditional methods of estimation like maximum likelihood estimation, method of moments, least-square estimation method help only in finding the local solution. Here, for finding the global solution two well-known metaheuristic approaches are applied.

Findings

The reliability of the LHD machine after 60 days on the real data set is 28.55%, using GA on 250 generations is 17.64%, and using PSO on 100 generations and 100 iterations is 30.25%. The PSO technique gives the global best value of reliability.

Practical implications

The present work will be very convenient for reliability engineers, researchers and maintenance managers to understand the failure and repair pattern of LHD machines. The same methodology can be applied in other process industries also.

Originality/value

In this case study, initially likelihood function of the best-fitted distribution is optimized by GA and PSO. Reliability and maintainability of LHD machines evaluated by the traditional approach, GA and PSO are compared. These results will be very helpful for maintenance engineers to plan new maintenance strategies for better functioning of LHD machines.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 24 November 2023

Iman Rastgar, Javad Rezaeian, Iraj Mahdavi and Parviz Fattahi

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize…

Abstract

Purpose

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

Design/methodology/approach

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

Findings

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

Originality/value

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

Details

Journal of Quality in Maintenance Engineering, vol. 30 no. 1
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 14 May 2018

Sepideh Eskandari Dorabati, Ali Zeinal Hamadani and Hamed Fazlollahtabar

Due to the fact that the non-standard products, being used by customers, may cause failures in products with sales delays, which naturally affect the warranty policy. Thus, it…

Abstract

Purpose

Due to the fact that the non-standard products, being used by customers, may cause failures in products with sales delays, which naturally affect the warranty policy. Thus, it seems to be necessary to study these two concepts simultaneously. The paper aims to discuss these issues.

Design/methodology/approach

In this paper, a model is developed for estimating the expected warranty costs under sales delay conditions when two operator costs (failing but not reported and non-failing but reported) are included.

Findings

The proposed model is validated using a numerical example for a two types of intermittent and fatal failures occur under a non-renewing warranty policy.

Originality/value

Sales delay is the time interval between the date of production and the date of sale. Most reported literature on warranty claims data analysis related to sales delay have mainly focussed on estimating the probability distribution of the sales delay.

Details

Journal of Quality in Maintenance Engineering, vol. 24 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 16 December 2022

Nan Li, M. Prabhu and Atul Kumar Sahu

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective…

Abstract

Purpose

The main purpose of present study is to model the replacement policy under uncertainty for managerial application based on grey-reliability approach by considering the subjective views of quality control circle (QCC). The study objectively links the optimality between individual replacement and group replacement policies for determining the minimum operational costs. The integrated framework between QCC, replacement theory, grey set theory and supply chain management is presented to plan replacement actions under uncertainty.

Design/methodology/approach

The study proposes the concept of grey-reliability index and built a decision support model, which can deal with the imprecise information for determining the minimum operational costs to plan subsequent maintenance efforts.

Findings

The findings of the study establish the synergy between individual replacement and group replacement policies. The computations related to the numbers of failures, operational costs, reliability index and failure probabilities are presented under developed framework. An integrated framework to facilitate the managers in deciding the replacement policy based on operational time towards concerning replacement of assets that do not deteriorate, but fails suddenly over time is presented. The conceptual model is explained with a numerical procedure to illustrate the significance of the proposed approach.

Originality/value

A conceptual model under the framework of such items, whose failures cannot be corrected by repair actions, but can only be set by replacement is presented. The study provides an important knowledge based decision support framework for crafting a replacement model using grey set theory. The study captured subjective information to build decision model in the ambit of replacement.

Details

Grey Systems: Theory and Application, vol. 13 no. 2
Type: Research Article
ISSN: 2043-9377

Keywords

Article
Publication date: 22 April 2022

Lijun Shang, Qingan Qiu, Cang Wu and Yongjun Du

The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product…

Abstract

Purpose

The study aims to design the limited number of random working cycle as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability during the warranty period. By extending the proposed warranty to the consumer's post-warranty maintenance model, besides the authors investigate two kinds of random maintenance policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating depreciation expense depending on working time, the cost rate is constructed for each random maintenance policy and some special cases are provided by discussing parameters in cost rates. Finally, sensitivities on both the proposed warranty and random maintenance policies are analyzed in numerical experiments.

Design/methodology/approach

The working cycle of products can be monitored by advanced sensors and measuring technologies. By monitoring the working cycle, manufacturers can design warranty policies to ensure product reliability performance and consumers can model the post-warranty maintenance to sustain the post-warranty reliability. In this article, the authors design a limited number of random working cycles as a warranty term and propose two types of warranties, which can help manufacturers to ensure the product reliability performance during the warranty period. By extending a proposed warranty to the consumer's post-warranty maintenance model, the authors investigate two kinds of random replacement policies to sustain the post-warranty reliability, i.e. random replacement first and random replacement last. By integrating a depreciation expense depending on working time, the cost rate is constructed for each random replacement and some special cases are provided by discussing parameters in the cost rate. Finally, sensitivities to both the proposed warranties and random replacements are analyzed in numerical experiments.

Findings

It is shown that the manufacturer can control the warranty cost by limiting number of random working cycle. For the consumer, when the number of random working cycle is designed as a greater warranty limit, the cost rate can be reduced while the post-warranty period can't be lengthened.

Originality/value

The contribution of this article can be highlighted in two key aspects: (1) the authors investigate early warranties to ensure reliability performance of the product which executes successively projects at random working cycles; (2) by integrating random working cycles into the post-warranty period, the authors is the first to investigate random maintenance policy to sustain the post-warranty reliability from the consumer's perspective, which seldom appears in the existing literature.

Details

Journal of Quality in Maintenance Engineering, vol. 29 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 11 February 2021

Rodrigo E. Peimbert-García, Jesús Isaac Vázquez-Serrano and Jorge Limón-Robles

Literature shows that the economics of early failures in maintenance and electric utilities have not been deeply analyzed. This study aims to focus on quantifying the economic…

Abstract

Purpose

Literature shows that the economics of early failures in maintenance and electric utilities have not been deeply analyzed. This study aims to focus on quantifying the economic impact that early failures in current transformers have on total maintenance costs. The empirical study is conducted in a regional transmission division of an electric utility located in Mexico.

Design/methodology/approach

The utility's database was accessed to collect 219 maintenance records. Clustering techniques were used to identify early failures from a bimodal distribution of failures. Confirmatory goodness-of-fit procedures followed the analysis, and finally, direct and opportunity costs were estimated by adapting the cost-of-quality (PAF) Model.

Findings

Around 11% of all maintenance activities are triggered by early failures, and they account for up to US$2.2m during the eight-year period under study, which represents 16% of total maintenance costs. Additionally, opportunity costs represent close to two-thirds of the total costs due to early failures. This was obtained after finding and validating a clear-cut border of 3.5 months between early failures and the rest.

Originality/value

Failures in energy grids and power transmission can have a large economic impact on the power industry and the society in general. Thus, the maintenance function in equipment such as current transformers is a crucial entry of the budget of any electric utility. This study is one of the very few that highlights the magnitude and importance of direct and opportunity costs derived from early failures.

Details

Journal of Quality in Maintenance Engineering, vol. 28 no. 2
Type: Research Article
ISSN: 1355-2511

Keywords

Article
Publication date: 19 November 2021

Zhiting Song and Jianhua Zhu

Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart…

Abstract

Purpose

Smart manufacturing is the prime gripper for the transformation and upgrading of the manufacturing industry. Smart manufacturing systems (SMSs) largely determine how smart manufacturing evolves in technical and organizational dimensions and how it realizes values in products, production or services. SMSs are growing rapidly and receiving tons of attention from academic research and industrial practice. However, the development of SMSs is still in its fancy, and many issues wait to be identified and solved, such as single point failures, low transparency and ineffective resource sharing. Blockchain, an emerging technology deriving from Bitcoin, is competent to aid SMSs to conquer troubles due to its decentralization, traceability, trackability, disintermediation, auditability and etc. The purpose of this paper is to investigate the blockchain applications in SMSs, seek out the challenges faced by blockchain-enabled SMSs (BSMSs) and provide referable research directions and ideas.

Design/methodology/approach

A comprehensive literature review as a survey is conducted in this paper. The survey starts by introducing blockchain concepts, followed by the descriptions of a literature review method and the blockchain applications throughout the product life cycle in SMSs. Then, the key issues and challenges confronting BSMSs are discussed and some possible research directions are also proposed. It finally presents qualitative and quantitative descriptions of BSMSs, along with some conclusions and implications.

Findings

The findings of this paper present a deep understanding about the current status and challenges of blockchain adoption in SMSs. Furthermore, this paper provides a brand new thinking for future research.

Originality/value

This paper minutely analyzes the impacts that blockchain exerts on SMSs in view of the product life cycle, and proposes using the complexity science thinking to deal with BSMSs qualitatively and quantitatively, including tackling the current major problems BSMSs face. This research can serve as a foundation for future theoretical studies and enterprise practice.

Details

Chinese Management Studies, vol. 16 no. 5
Type: Research Article
ISSN: 1750-614X

Keywords

Article
Publication date: 22 September 2021

Panagiotis Tsarouhas

The purpose of this research is to calculate and enhance the cheese cheddar manufacturing plant efficiency under actual workplace conditions by measuring reliability, availability…

Abstract

Purpose

The purpose of this research is to calculate and enhance the cheese cheddar manufacturing plant efficiency under actual workplace conditions by measuring reliability, availability and maintainability (RAM) indices. The authors highlight how RAM analysis is important in determining periodic maintenance and in scheduling and managing the appropriate maintenance policy.

Design/methodology/approach

The current work is conducted using statistical approaches to evaluate failure and repair statistics. The RAM estimation was calculated on the basis of quantitative data obtained over a span of 32 months. Descriptive statistics, Pareto analysis, as well as the presumption of independence were ensured through trend and serial correlation tests. In addition, the reliability and maintainability of the cheddar cheese processing plant and its machines were calculated at various mission periods.

Findings

The primary goal of the implementation approach is to understand the fault patterns and the accurate quantitative assessment of the reliability and maintainability of the cheddar production plant. The findings revealed the essential aspects of the line, which need improvement by an appropriate maintenance program.

Originality/value

This study is intended to serve to highlight the RAM assessment and its impact on the performance of the real-time system. The benefit of the technique is the continual control of the manufacturing process by means of acceptable indexes, whose use corresponds to a continuous improvement process.

Details

International Journal of Productivity and Performance Management, vol. 71 no. 2
Type: Research Article
ISSN: 1741-0401

Keywords

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