جستجوی مقالات مرتبط با کلیدواژه « Preventive Maintenance » در نشریات گروه « مدیریت »

The flexible job shop system is one of the most widely used scheduling systems in production environments, consistently attracting researchers' attention due to its diverse applications. Many studies in this field assume fixed and predetermined processing times. However, processing times can increase due to the deterioration effect, and after implementing rate-modifying activities (RMA), these times return to their original values. This study examines the flexible job shop scheduling system, considering job rejection policies, dual resource constraints (human and machine), and RMA maintenance activities.

The objective of flexible job shop scheduling is to assign each operation to a machine and a worker from a set of eligible machines and workers in a way that optimizes the sequence of operations on the machines. A mathematical model based on the mixed-integer linear programming approach was developed for this purpose. Literature review classifies the problem with the stated assumptions as NP-hard, making the use of meta-heuristic methods essential for finding near-optimal solutions. Thus, Variable Neighborhood Search (VNS), Simulated Annealing (SA), and a combined VNS-SA algorithm were employed to solve the problem.

Twenty sub-problems were analyzed, categorized into small, medium, and large-sized problems. The characteristics of each problem were defined by parameters such as the number of jobs, machines, workers, total operations, and buckets. Meta-heuristic methods, including VNS, SA, and their combination, were utilized to solve the problem. Seven neighborhood structures based on changes in assigned machines and workers, operation and job replacements, execution of RMA activities, and job acceptance/rejection were developed to enhance solution space exploration. The solution generation structure ensures feasibility within the flexible job shop system's requirements. The parameters of the meta-heuristic methods were tuned using the Taguchi method. Parameters related to the combined VNS-SA algorithm, such as initial temperature, number of neighborhood searches, and shake procedure counter, were reported. The results of the meta-heuristic methods were compared, and for small-sized problems, they were also compared with exact solutions.

The results of the twenty sub-problems solved using the three meta-heuristic approaches were compared statistically. The combined method of simulated annealing and variable neighborhood search showed superior performance in solving the problem.

In this research, the intelligent model of preventive maintenance and repairs based on artificial neural network methodology - fuzzy logic with the help of artificial intelligence environment of MATLAB software based on the structure of Falcon's five-layer model of artificial neural networks is presented, the research method is based on systems thinking. After determining the most important factors affecting preventive maintenance and repairs with the help of a questionnaire and based on a dataset of 2,000 samples of data and reports of the Director General of Textile and Clothing Industries of the Ministry of Safety during the years 1396 to 1401 (in the form of six and a half years) and validity Data evaluation by the maintenance and repair experts of 240 industrial units, a smart model was designed, which after the implementation of the model in Borujerd textile factories as the place of implementation of the plan can be claimed if (If); Five "technology" factors have values of 0.9129; Good condition (upper bound of good membership function), "Employees" has values of 0.9239; good condition (upper bound of good membership function), "working environment" has values of 0.8859; relatively good (lower limit of the membership function), "quality" has values of 0.9999; Perfect condition (highest function), "strategy" has values of 0.9999; good status (upper limit) in preventive maintenance and repairs, then: the status of the output variable of the research, i.e."Optimization of preventive maintenance and repairs performance (Y)" will be at its fifth level, i.e. very good, equal to 0.882.

The aim of the research is "designing a smart dynamic model of preventive net using simulation in interaction with production" based on the documentation of textile and clothing industries under the title of dynamic preventive net model. In order to calculate the dynamics of the system, Vansim software and research data were prepared based on a sample of 2000 data and reports from the textile and clothing industries during the years 2016 to the end of 2014 and the textile factories of Borujerd as the location of the project on a semi-annual basis. The results of the research show that the growth rate of "working environment on the net" has reached the lowest growth rate of 1.2% in 2016 and 3.5% in 2018 and a higher growth of 6% in 2014. The growth rate of "Internet technology" has reached an average growth rate of 1.2% in 1396, 5% in 1398, and a higher growth of 8.5% in 1400. The dynamic growth rate of the variable "Strategy in Net" has reached the maximum growth rate of 1% in 2016, 6% in 2018, and a higher rate of growth in 2014, i.e. 9.5% (optimal state).

The scheduling of parallel machines and preventive maintenance is one of the key issues in the field of production processes, and has always been a topic of interest for researchers. This research aims to design an integrated model for scheduling and preventive maintenance for parallel machines considering the probability of disruption in facilities and uncertainty in parameters of the model. In this regard, a mathematical scheduling model has been proposed with two objective functions of minimizing the weighted completion time of products and maximizing the reliability of the production line. The NP-hard nature of the studied problem from a computational perspective, meta-heuristic algorithms such as NSGA-II and MOPSO were utilized to solve numerical problems in medium and large scales. Therefore, numerical problems were designed in different size and solved by the proposed algorithms. The results showed that the NSGA-II compared to the MOPSO algorithm provide better solutions. However, MOPSO has better efficiency than NSGA-II in term of computation time, this superiority is not considerable and it can not be considered as a definitive basis for comparing two algorithms.

In this study, the identification of the most important influencing factors for intelligent preventive maintenance and repair in the textile industry in interaction with manufacturing was performed using Spss 25 software. The research data set is based on the 2000 sample data and reports from the Director General of Textile Industry under the Ministry of Industry, Mine and Trade and the factories in the textile industry of Borujerd where the project was implemented on a semiannual basis as of 2015 to 2020.  This survey is significant in that it collects information in cooperation with specialists in the maintenance industry and the textile industry (information was collected from 240 establishments of 630 textile factories nationwide). The study sample size included professors, managers, and professionals from the textile industry. A comparison of the pre-test questionnaire (functional status) and the post-test questionnaire (ideal status) and statistical analysis calculations yielded the following results. The relationships between the study variables and the most important preventive maintenance and improvement factors in interaction with production in the textile industry have been classified as follows: strategy factor with the possibility of determining 34%, technology factor 30%, work environment factor 16%, staff factor 10%, the quality factor was identified by 10%.

Due to the effect of random factors such as machine failure on the competitiveness of production organizations and the importance of production planning, failure-prone manufacturing systems have emerged to deal with uncertainty. In order to maintain a competitive market share and increase productivity and safety, industrial systems have resorted to a maintenance strategy to reduce failure rates and increased reliability. Increasing production capacity, providing more flexibility and ensuring customer satisfaction in terms of quantity, quality and timing have made the use of subcontracting with a revenue sharing approach a viable option in this study. In this research, a network of machines with relationship limitation and failure and accidental repair is considered. To prevent shortages, intermediate buffers and a final buffer are used. Another important parameter is determining the optimal frequency of preventive maintenance, which results in minimizing the cost of preventive and corrective maintenance and repairs. The goal is to determine the optimal production rate and preventive maintenance variables and subcontractor variables. Discrete-event-simulation is used for this purpose. After modeling in Arena, the best values of decision variables are obtained in the opt-quest platform, which leads to a 22.5% reduction in total system costs.

The main purpose of this study was to identify and level the most important factors affecting preventive maintenance and repairs using interpretive structural modeling (ISM).

The present study employed descriptive survey method. The data were collected through a variety of methods, including library resources and field studies (interviews and questionnaires) and the use of websites (the Internet). To identify the relationships between variables, the expert judgment system (Delphi method) was used and a questionnaire was distributed to the experts to examine the pair-wise interaction of indicators in terms of their effectiveness and impact on each other. Questionnaires that included the information for the research model were randomly distributed among 10 elites. The qualitative data were analyzed through grounded theory method and the data collected through the questionnaire were subjected to factor analysis. In this model, the initial access matrix was obtained by converting the structural self-interaction matrix into a dual value matrix (zero and one). In the next step, the relationships and leveling of the factors were determined and then the final model was drawn.

MICMAC software was used to analyze the data obtained through the questionnaires. Based on the MICMAC matrix, it was observed that factors 1 to 7 were in the dependent region, which means that they have low driving power but a high degree of dependence on other factors. Factors 8 to 14 were in the driver power zone. These factors have high driving power with minimal dependence.

According to the model obtained from the ISM technique, the required budget index was identified as the most effective factor. Therefore, we must first identify the necessary budget factors for maintenance and repair. The ISM graph shows a clear picture of causal relationships between the factors. Also, the appropriate checklist index was identified as the most highly influenced index.

Determining preventive maintenance intervals is critically significant from a variety of aspects, such as cost, reliability, and downtime. Currently, in many industries, such intervals are determined based on the viewpoints of experts and technicians or the manufacturer's recommendations in the manual. Determining an optimal interval, by the means of scientific methods, can have many effects in terms of costs, reliability, and downtime. For example, if the interval is assumed as very short, it can increase the cost of spare parts or manpower. Also, if the interval is too long, it increases the possibility of machine failure and downtime. For this purpose, organizations that apply the approach of time-based preventive maintenance should consider the above-mentioned criteria. However, such criteria are not compatible with each other, and each of them has its behavior. Therefore, given the existence of more than one criterion in this decision-making problem and the incompatibility of the criteria, Multi-Criteria Decision-Making (MCDM) methods seem appropriate for this purpose.

 A component of a machine that is currently replaced periodically (and not necessarily at equal intervals) is considered for investigation. It is assumed that the failure distribution function of the component follows the Weibull distribution. First, using the Maximum Likelihood Estimation (MLE) technique, new and more general equations are presented to estimate the parameters of the failure distribution function. Subsequently, three important criteria are taken into account, including reliability, cost, and downtime due to failure and preventive replacement.. Given the existence of several criteria with different behaviors, as well as the vague or linguistic nature of human judgments, a hybrid MCDM model including Fuzzy Analytic Hierarchy Process (FAHP) and Viekriterijumsko kompromisno rangiranje (VIKOR) is proposed.

To indicate the applicability of the proposed model, a real case was investigated in the Isfahan Steel Company. For this purpose, a circuit breaker switch of a compressor in the oxygen workshop was studied. The component was replaced at about a 200 days interval. After implementing the FAHP method, it was found that the reliability criterion was more important than the other two criteria. This is due to the high sensitivity of this component and the consequences of its failure. After Performing the VIKOR method, the set of top alternatives included replacement intervals between 50 and 90 days. It confirms that the importance of the reliability criterion, and the replacement interval, should be reduced by at least a half. Based on the results, reliability was expected to improve by 2.6% and the cost and downtime criteria to improve by 33.7% and 28.6%, respectively.

 To implement the proposed method for each component, the following data are required:Preventive maintenance and breakdown records of the component Cost and downtime of the machine due to breakdown and preventive maintenanc.Three pairwise comparisons to implement the FAHP

If information systems exist in the relevant plant, the data for items 1 and 2 are available and do not constitute a constraint on the proposed model. The only limitation of this model is the inputs of the FAHP method for which, implementing the model depends on the humans. Doing three pairwise comparisons for each component may seem a bit difficult and time-consuming. Consequently, the removal of this limitation can be proposed as a suggestion for the development of this study. Other calculations of this model can be done automatically. Also, the study of other probability distribution functions such as gamma and lognormal can be effective in improving the results of this study.

 To the best of our knowledge, the fuzzy AHP or VIKOR method has not been used to address the subject of this study, even independently. In addition, unlike previous studies, the proposed model was implemented for a component that was undergoing preventive maintenance. In other words, in most of the data collected the component was replaced before failure, which made it impossible to:Discover the component failure distribution function by data fitting. As a result, it can be assumed that the failure distribution follows the Weibull distribution.Applying the existing equations to estimate the parameters of the failure distribution function. As a result, new equations can be proposed by implementing the MLE technique.

In this paper, a non-periodic preventive maintenance scheduling optimization model for multi-component systems is provided based on the maximum availability of system components. In addition to providing the required level of system reliability and satisfy other system constraints (maintenance activities and available resources), total costs (direct and indirect) associated with minimal maintenance and, if necessary, one of the maintenance activities include in inspected and serviced simple, preventive repair and preventive replacement for each component, is proposed. Each of these activities uses various sources and regarding the position of the repairing component, effects differently on the reliability of the system. The costs considered include in direct costs (simple service, repair and replacement) as well as indirect costs (out of order and random failures). Since the proposed model has a complex structure, in order to solve the problem, the Genetic Algorithm (G.A) has been used and the results is presented. In the end, performance and use of this model, for a 10-part series - parallel is presented in the form of a case study.

Distributing the production activities among the supply chain facilities with regard to the considered criteria can have a significant impact on the productive management. In this paper, a comprehensive mathematical model for reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. The uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. Job assignment to production facilities and job scheduling are determined in the proposed model by considering multiple objectives include Cmax minimization, production cost minimization, and average tardiness. Due to the NP-hard nature of the proposed flow shop scheduling problem, a new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is adopted. The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem. In certain manufacturing industries, it has been observed that the classical assumption of flow shop scheduling, stating that each job visits each machine exactly once, is occasionally violated. The prime example can be noticed in the high-tech industries, i. e. semiconductor wafer fabrication in which the operation processes of the jobs are performed by re-visiting some workstations (Gupta & Sivakumar, 2006). The scheduling problem of this nature of processing is categorized as a distinct flow shop with reentrant line configuration, called reentrant flow shop scheduling (RFS) (Katragjini et. al. , 2015). The significance of RFS is the processing layers l. Each layer begins from the  first workstation and completes on the last workstation. It means that once a job finished a layer of a set of operations, it will repeat its process to the next layer starting on the first workstation until all operations are completed. The RFS scheduling has been an active research area and attracted a considerable attention since the past decade due to the development and improvement of high-tech industry. The complexity of RFS cannot be circumvented since it involves more operations than the classical flow shop. Moreover, the cyclic operations where the jobs with higher layers may overlap other jobs in the same work station are essential to be considered. As a result, these complexities have triggered the development of the efficient scheduling approaches to improve the system performance. Various researchers surveyed the scheduling techniques in semiconductor manufacturing and providing the global view on reentrant scheduling problems. Another form of RFS is reentrant permutation flow shop (RPFS) where at each level no passing is permitted, that is, not only the machine sequence the same for all jobs, but also the job sequence is the same for each machine (Rifai et al. , 2016).    Despite the enormous literature on the RFS, most studies -if not all- base their research on the assumption that the process only involves a single production line. Some studies exploredthe problem on hybrid RFS where the production stages have more than one machines available to process the jobs. Nevertheless, hybrid RFS is based on the single production line. Nowadays, single factory firms are less common, with multi-plant companies and supply chains taking a more important role in practice. Several literatures mentioned that multiple production lines with more than one production center, named as distributed manufacturing system, enables companies to achieve higher product quality, lower production costs and lower management risks. However, existing studies focused more on the economic field anddistributed finite capacity scheduling is seldom tackled. In this section, a novel hybrid meta-heuristic via considering the specific assumptions of the flow shop problem as a NP-hard problem is proposed. The proposed solution algorithm incorporates adaptive large neighborhood search and the simulated annealing algorithms. Various new construction and deconstruction neighborhood structures are applied in the proposed adaptive large neighborhood search algorithm. Details of the proposed algorithm is presented in Fig. 1. The results of the proposed solution algorithm assessment are presented based on the two common performance assessment criteria which are proposed in the literature after 10 times runs of the applied solution algorithms. These criteria are the average number of obtained Pareto solution at each iteration of  the algorithm and average number of Pareto solutions which are not dominated by solutions from  other compared algorithms. In addition, computational time is considered as a third criteria for performance assessment of the proposed solution algorithm (See Table 1). Obtained results indicate the superiority of the  proposed solution algorithm.   In this study, a comprehensive optimization model for an extended reentrant permutation flow shop scheduling via considering a preventive maintenance and distributed jobs on different facilities is proposed. To enhance the applicability of the proposed model, uncertainty of the time of preventive maintenance operation is handled using robust optimization technique based on the uncertainty budget approach. In the proposed mathematical model, multiple objectives include Cmax minimization, production cost minimization, and average tardiness are considered. The aim of the proposed model is to determine the job assignment to production facilities and job scheduling. A new hybrid meta-heuristic based on the novel adaptive large neighborhood search and the simulated annealing is applied as a consequence of the NP-hard nature of the proposed flow shop scheduling problem, . The obtained results from an extensive numerical experimentation indicate the efficiency of the proposed model and solution algorithm to tackle the proposed problem.

Taking attention to reliability and maintenance management conception has substantially significantly increased over the recent decades. This paper proposes and integration of DATA MINING, Fuzzy DEMATEL, and FMEA technique in order to improve the reliability and effectiveness of maintenance management in Shazand Petrochemical Company. Firstly, the most critical cluster is selected among final clusters obtained from software by utilizing DATA MINING technique. Secondly, Fuzzy DEMATEL technique is used to identify a collection of most critical and most effective equipment of critical cluster under fuzzy condition. Finally, the FMEA and FTA techniques applied to identify the risks numbers and the main causes of the failure and then the solution will be proposed to solve the problems and improve the system.

In this paper, the problem of job scheduling and preventive maintenance activities is investigated simultaneously in an unrelated parallel machine’s environment with machine deterioration. Due to the existence of deterioration phenomenon and its effect on the job scheduling, the aim of this paper is to find the ideal time, the optimum number of maintenance activities and the sequence of the jobs, for minimizing the sum of job's completion times. Preventive maintenance activities may be needed because of tool wears or machine deterioration in a job scheduling horizon. Here, two different deterioration time function and tool wear are studied, in which the machines are reverted to the original condition after any preventive maintenance activity. The duration of each activity is considered as a function of its starting time and its position on the sequence of job scheduling. A mathematical model is proposed and due to its complexity, a simulated annealing Mehta-heuristic algorithm is used for solving the model. Finally, numerical examples are given and sensitivity analysis with various parameters is applied to evaluate the performance of the proposed method.

Scheduling of preventive maintenance and replacement is one of the crucial issues in the literature of system reliability and maintenance engineering. The importance of this issue is more evident in series system in which by stopping a component the whole system will stop. However, the quest for the efficient periodic maintenance for all components of a system which has antagonistic objectives is not a trivial task. The aim of this paper is to present scheduling model of preventive maintenance and replacement for series systems by variable and not-predetermined periods and also by applying inflation in the model. The proposed multi-objective model minimizes breakdown, maintenance, replacement and fixed costs and also maximizes system's reliability. The model is designed based on fuzzy multi-objective and integer non-linear programming. The proposed model is solved with a numerical case. Furthermore, analysis of various scenarios and the effects of inflation on the model have been considered. The results show that applying inflation in the model changes not only scheduling for replacement and maintenance, but also affect the effective age of components.

Maintenance is one of the Inseparable parts of each Industry and relations between this part and others consist of many dynamics and feedbacks. By this regard systems dynamic is a tool that can be used to analyze this part. In this paper for a company that produces Wire and cable, cause and effect (feedbacks) between maintenance and others part are modeled. After creating stock-flow diagram, model validation analyzed with Behavior Reproduction, Extreme Condition, and Sensitivity test. Also after model validation, effect of changing companies attitude from corrective maintenance to preventive maintenance, discussed. Results show this shifting is beneficial for company from different views and increase sales and customers. By this regard company managers should increase their Attention to preventive maintenance.

The determination of optimal production lot size has been always noticed by the researchers and various models have been proposed in this area. Among the issues under less consideration in these models is the issue of machine failure. Obviously، the machine may be encountered during the work with random failures and stop working; this makes clear the need for applying an appropriate maintenance policy for the machinery. In this paper by the consideration of some assumptions as authorized shortage، taking into account the work in process inventory and the possibility of producing imperfect products with and without the ability to re-work، EPQ model were investigated to select preventive maintenance policy for the machinery. Also by minimization of the system total cost over a period of time، a formula for determining the optimal production lot size is presented and by the presentation of various numerical examples، its applications is shown.

The purpose of preventive maintenance management is to perform a series of tasks that prevent or minimize production breakdowns and improve reliability of production facilities. An important objective of preventive maintenance management is to minimize downtime of production facilities. In order to accomplish this objective، personnel should efficiently allocate resources and determine an effective maintenance schedule. Gopalakrishnan (1997) developed a mathematical model and four heuristic approaches to solve the preventive maintenance scheduling problem of assigning skilled personnel to work with tasks that require a set of corresponding skills. However، there are several limitations in the prior work in this area of research. The craft combination problem has not been solved because the craft combination is assumed as given. The craft combination problem concerns the computation of all combinations of assigning multi skilled workers to accomplishment of a particular task. In fact، determining craft combinations is difficult because of the exponential number of craft combinations that are possible. This research provides a heuristic approach for determining the craft combination and four new heuristic approach solution for the preventive maintenance scheduling problem with multi skilled workforce constraints. In order to examine the new heuristic approach and to compare the new heuristic approach with heuristic approach of Gopalakrishnan (1997)، 81 standard problems have been generated based on the criterion suggested by from Gopalakrishnan (1997). The average solution quality (SQ) of the new heuristic approaches is 1. 86% and in old heuristic approaches is 8. 32%. The solution time of new heuristic approaches are shorter than old heuristic approaches. The solution time of new heuristic approaches is 0. 78 second and old heuristic approaches is 6. 43 second، but the solution time of mathematical model provided by Gopalakrishnan (1997) is 152 second.