Machine Scheduling Performance with Maintenance and Failure

In manufacturing control, machine scheduling research has mostly dealt with problems either without maintenance or with deterministic maintenance when no failure can occur. This can be unrealistic in practical settings. In this work, an experimental model is developed to evaluate the effect of corrective and preventive maintenance schemes on scheduling performance in the presence of machine failure where the scheduling objective is to minimize schedule duration. We show that neither scheme is clearly superior, but that the applicability of each depends on several system parameters as well as the scheduling environment itself. Further, we show that parameter values can be chosen for which preventive maintenance does better than corrective maintenance. The results provided in this study can be useful to practitioners and to system or machine administrators in manufacturing and elsewhere. (c) 2006 Elsevier Ltd. All rights reserved

Joint scheduling of jobs and preventive maintenance operations in the flowshop sequencing problem: A resolution with sequential and integrated strategies.

International audienceUsually, scheduling of maintenance operations and production sequencing are dealt with separately in the literature and, therefore, also in the industry. Given that maintenance affects available production time and elapsed production time affects the probability of machine failure, this interdependency seems to be overlooked in the literature. This paper presents a comparative study on joint production and preventive maintenance scheduling strategies regarding flowshop problems. The sequential strategy which consists of two steps: first scheduling the production jobs then inserting maintenance operations, taking the production schedule as a strong constraint. The integrated one which consists of simultaneously scheduling both maintenance and production activities based on a common representation of these two activities. For each strategy, a constructive heuristic and two meta-heuristics are proposed: NEH heuristic, Genetic algorithm and Taboo search. The goal is to optimize an objective function which takes into account both production and maintenance criteria. The proposed heuristics have been applied to non-standard test problems which represent joint production and maintenance benchmark flowshop scheduling problems taken from Benbouzid et al. (2003). A comparison of the solutions yielded by the heuristics developed in this paper with the heuristic solutions given by Taillard (1993) is undertaken with respect to the minimization of performance loss after maintenance insertion. The comparison shows that the proposed integrated GAs are clearly superior to all the analyzed algorithms

Penentuan Interval Waktu Preventive Maintenance Mesin Dengan Tindakan Berdasarkan Metode Reliability Centered Maintenance (RCM) Pada PT PLN (Persero) UPK Bukittinggi

UPK Bukittinggi is a state electricity company in charge of 3 hydropower plants in West Sumatra with a vision to become the leading electricity company in Southeast Asia. To realize this vision, the company must continue to monitor a good level of productivity on the quality of engine performance which must be maintained. For this reason, it is necessary to propose a preventive maintenance plan for each hydropower plant with actions based on the Reliability Centered Maintenance (RCM) method. The purpose of the study was to calculate the amount of time for repair (Mean Time To Repair) and the amount of time the damage occurred (Mean Time To Failure) as well as determine the maintenance time interval schedule for the equipment experiencing the highest downtime at UPK Bukittinggi. Where the RCM method is integrated with the Failure Mode Effects Analysis (FMEA) method which is used as an analysis of the failure mode that occurs by calculating the highest Risk Priority Number (RPN) value on the machine and then calculating the time interval for damage as the basis for determining the maintenance schedule. The result of the research is that with routine scheduling of machine maintenance will be obtained with time intervals obtained by time intervals and maintenance actions using the RCM method

Simulation Of Preventive Maintenance Schedule In Reducing Machine Stoppage And Downtime

In the past several decades, there have been diverse and significant research activities on maintenance scheduling. Machines are usually down periods due to plenty of reasons such as preventive maintenance activities. Preventive maintenance is defined as regularly scheduled maintenance actions based on the average failure rates. An optimize implemented preventive maintenance strategy can provide many benefits to an organization in terms of extending equipment life and machine availability. Few researches have addressed the issues of preventive maintenance. For instances, proper preventive maintenance scheduling can maximize the machine lifecycle and production throughputs. In this thesis, the objective is to study the machine availability that affected by the preventive maintenance scheduling. The model was developed based on the Surface Mounted Technology (SMT) in manufacturing system. The SMT line is a continuous flow production line since every machine are connected in line. In this research, the model that has been build consists of five different machine which is screen printer, glue dispenser, chip shooter, pick and place and reflow oven. WITNESS 14 Manufacturing Performance Edition software was used to imitate the Surface Mounted Technology (SMT) production system. This is to carry out the optimisation of preventive maintenance scheduling by using different breakdown intervals and preventive maintenance duration. Results of simulation study comparing the availability of machine with different preventive maintenance scheduling. The total system availability was calculated since the SMT line is continuous flow production. The findings of this project shown that the optimise preventive maintenance should be done every one week with 30 minutes PM duration

PERENCANAAN PEMELIHARAAN DAN OPTIMASI BIAYA PERAWATAN PADA SISTEM UTILITY DENGAN METODE PREVENTIVE MAINTENANCE

A company oil refinery based on condensate or petroleum using corrective maintenance type, one of them is in utility system. Corrective maintenance is less suitable with the course of production in a company it is continuous because the company can lose due to production stopped. Therefore, reliability analysis is performed using preventive maintenance method the scheduling check or inspection is used to prevent machinery or equipment from being damaged. Thus, the performance of the machine can run smoothly so that the production process is not disrupted and more efficient. Cost operational from 2011-2017 will be identified failure with FTA and FMEA. So that will get the value of reliability. Then performed the scheduling in accordance with the resulting MTTF value. Then the cost will be calculated to produce more efficient maintenance cost and the result more efficient cost is 30%

OPTIMIZATION OF MACHINE PREVENTIVE MAINTENANCE SCHEDULING USING STEADY STATE GENETIC ALGORITHM

Maintenance management is one of the important factors to support the success of industrial activities. In order for an industry to have a high level of profit. Good maintenance management is needed to minimize costs lost due to the engine failure. Preventive maintenance activities are one of the company's efforts to be able to maintain the life span and engine performance. In conducting preventive maintenance activities, the company wants to maximize machine reliability with minimum costs. The existing maintenance activities implemented by the company are to doing the maintenance every 2 months, but with the implementation of this maintenance policy it still has many obstacles in its implementation. Therefore optimization is needed to overcome this problem, one of the methods proposed to do preventive maintenance scheduling is the steady state genetic algorithm optimization method. On completion, 3 types of fitness functions are used, Fitness function 1 is a fitness function by giving weights to total costs and reliability functions with conditions w1 + w2 = 1. Fitness function 2 is a fitness function that is used by having a given budget limit. While Fitness function 3 is a fitness function that is used to provide required reliability or reliability that the company wants to achieve. The input from the steady state genetic algorithm has 3 components, the time to failure distribution parameter, the cost and budget, and the iteration input from the genetic algorithm. Based on data that has a 2 parameter Weibull distribution with scale parameter lambda = 0.00184 and shape parameter beta = 1.38194. Found 3 preventive maintenance scheduling proposals for 24 months period. The first result using fitness function 1 produced a total cost of 28.66 million rupiahs with a reliability value of 91.78%. The second proposal using fitness function 2 produced a total cost of 29.75 million rupiahs with a reliability value of 92.47%. The third uses using fitness function 3 resulting in a total cost of 30.79 million rupiahs with a reliability value of 92.52%.Keywords: Preventive maintenance, Optimization, Reliability, Total cost, Steady State Genetic Algorithm

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS