Esnek üretim sistemleri için bir etkin üretim denetleme modeli

Abstract

ÖZET `Bakım`, genellikle bir yöntemler kümesi olarak düşünülmektedir. Bazen, bakım'ın bir felsefe olduğu da söylenir. Aslında bakım, bir `yaşam tarzı `dır. Hem öyle bir yaşam tarzıdır ki, yönetici o tarza yaklaşmaya yaşamak ve yaşatmak için mecburdur. Günümüzün üretim tesislerinde artan komplekslik, tesisi yaşamı boyunca kabul edilebilir düzeyde tutmak için kendi özelliklerine uygun `bakım`ı şart koşmaktadır. Bakım ve üretim'ın gerçekleştirilen faaliyetler sırasında birbiriyle bütünleşmiş olduğu günümüzün en yeni endüstriyel üretim tesisi tipi olan Esnek üretim Sistemi (EÜS)'nde bakım, sistemin stratejik ve dinamik kontrol alam durumundadır. EOS'nde faaliyetler otomatize, entegre ve kompüterize halde iken, beklenmedik zamanda ortaya çıkan arızalar sistemin esnekliğini ve verimliliğini etkileyecektir. Etkin liği planlanan sistemde bu arızaların etkisi mümkün olduğunca belirlenmeli ve sistemin diğer faaliyetleri sırasında da ilgili kararlar alınırken sistem, bütünleşik olarak denetlenmelidir. Yöneticinin tesisindeki her teçhizatı detaylı olarak tanıması beklenemez. Ancak, tesisi ile ilgili siparişleri alma, siparişleri gönderme ve üretim kararlarım alırken, esnek olan kapasitesini göz önünde bulundurması gerekir. Bu esneklikte en önemli özellik olan arızalar ile ilgili bilgilere de, tesisinde etkinlik sağlayabilmesi açı sından mutlaka sahip olması gerekir. Bu gerçeklerden yola çıkılarak bu tezde, herhangi bir EÜS için temel olabilecek nitelikte genel bir `Etkinlik Kontrol Modeli`olan bir `Etkin üretim Denetleme Modeli` geliştirilmiştir. Geliştirilen modelde herhangi bir EOS, alınan siparişler, gönderilen siparişler, mamul stoku, üretim, kapasite ve rastsal arıza durumları itibariyle Sürekli Simülasyon Yaklaşımı olan Sistem Dinamiği Yaklaşımı ile ana liz edilmiş ve rastgele karşılaşılacak olan arızaların bütünleşik üre tim sistemine etkisi sergilenmiştir. Yönetici bu model ile sistemi durdurmadan mevcut durumu izleye bilecek, aksaklık gördüğü faaliyetlerde revizyon planları hazırlayarak ilgili karar adımlarını yeniden düzenli- yebilecektir. Böylece uy- gulayacağı denetim ile sistemin etkinliği için en uygun tavrı alabilecektir. (xü)

SUMMARY AN EFFECTIVE SUPERVISORY CONTROL MODEL FOR FLEXIBLE MANUFACTURING SYSTEMS A quantitative definition of `reliability` should lead to results which are meaningful in terms of decisions influencing research, de sign, specifications, manufacture, operation, maintenance and logistical, support and replacement. Increase in the number of elements leads to decrease in the reliability of overall performance. But at the same time, the increasing importance of the tasks carried out by such de vices requires their ever-increasing reliability. Resolution of this conflict requires the most careful investigation of the many facets of the problems of increasing the reliability of the elements and de vices; the possibility and desirability of increasing the reliability of the individual elements, the choice of work conditions, the search for suitable designs, evaluation of standby redundancy and optimum `preventive maintenance`, etc,. But the increase in reliability is not to be obtained gratis, and achieving it requires both definite ma-' terial expenditures and systematic scientific research. There are many ways of checking the quality of manufacture. One way is that; to develop measures for ensuring reliability which include periodic pre ventive maintenance `inspections`, `replacement` of units, rules for searching for `breakdowns`, etc,. To evaluate the quality of the ma nufacture we also need to know how often an event; such as a break down, a major overhaul, etc,, must take place. In a large class of reliability situations, maintenance such as replacement, repair or inspection may be performed servicing such systems during their use. Reliability constitutes one of the major design factors in the success ful and effective operation of modern technological systems which are becoming increasingly large and complex of primary importance in the planning and design of such multi-component systems. It is the problem of using the available resources in the most effective way so as to maximize the overall system reliability/availability, or so as to minimize the consumption of resources while achieving specific relia bility/availability goals. The alternative policies for the optimal system reliability/availability include; - using more reliable components, - redundancy, - sequential testing/supervision, - repair/preventive maintenance, (repair limit replacement policy (repair versus replacement optimi zation problem), reliability optimization with repair schemes, re liability allocation under preventive maintenance.) -replacement. So, one of the first areas of reliability to be approached with any mathematical sophistication is the area of `machine maintenance`. The areas of reliability research are; life-testing, structural relia bility (including redundancy considerations), machine maintenance (xiii)problems (a part of queueing theory) and the replacement problems (closely connected to renewal theory). The analysis failure modes and effects on equipment and systems are important for the system's performance. The field of safety or environmental control in electrical and nuclear power systems and in some industries have contributed greatly to such analysis. In such areas, self monitoring techniques and redundancy show improvements. The techniques have been applied to chemical proses plant systems (continuous proses functions) and FMS (discrete proses functions) and the benefits of reliability assess ment have been used, typically refer improvements not only in safety but also in maintenance downtime in plants and firms. The importance of the breakdown maintenance and the human factor such as the mainte nance team, emerges from such studies. It is the opinion of numerous engineers who deal with the plan ning of complex systems, that the concepts of reliability theory are, inapplicable to such systems. It is asserted that the concept of re liability of a complicated system has no meaning and that one may speak only of the efficiency of such systems. Efficiency is a measure of the productivity of the system with consideration of the environ ment and the operating procedure. In general, the concept of effici ency is independent of the concept of reliability. As the machines increased in complexity (manually control led-automatically controlled- programmably controlled-numerically controlled), they become less main-1 tainable. But this direct relationship is not true for the reliability of these machines. Reliability is influenced by the way in which a machine is operated. Automatic machines are the most reliable ones than manually controlled, after this, programmable machines come. Pro duction automation has been a cornerstone of increased productivity in manufacturing. It is the technology concerned with the application of complex mechanical, electronic and computer based systems in the operation and control of production. One of the manifestation of inc reased automation is the FMS (Flexible Manufacturing Systems). FMS has extensive control of actual production and has the ability to mo- ' nitor the status of production process for disruptions or deviations. A distinguishing operational characteristic of an FMS is the presence' / of computer control of actual production and materials handling. The key feature of an FMS is the ability to achieve the flexibility of a job-shop operation and the production capability of medium to large lots with the quality of a flow-shop while simultaneously reducing direct labour cost. FMS are intended to fill the gap between high pro duction transfer lines and low production highly flexible stand-alone machines. ı The task of the manager is to ensure as far as possible that the plant is kept in an `acceptable condition`. The acceptable condition depends upon factors such as; plant availability, level of production, operating cost and safety. `Ayai lability` commonly regarded as the index of `system effectiveness.` It is a parameter combining reliabi lity and maintainability and thus, a non-linear function of the module redundancies and the maintenance interval. It can be improved by ad ding redundancy to the system and or by performing preventive mainte nance on the system according to some prescribed schedule. So, system' effectiveness is a complex multidimensional vector of; use requirements, equipment conditions and performance characteristics or availability, (xiv)dependability and capability. Empirical failure and maintenance data as well as working and environmental conditions are necessary for models in industry. Simulation of the empirical data rather than analytic solution of a well- behaved distribution function, is realistic and strongly suggested to evaluate the system effectiveness. Because of the wide variety of the distributions encountered and because of the great difference in operational procedures, it is even less feasible than with other que- ueing situations to setup a `standard model` of a typical maintenance operation the result of which would be applicable to many cases enco untered in practice. Maintenance traditionally, is a mixture of peri odic preventive maintenance (PPM) and oncondition preventive mainte nance (OCPM) with some inevitable failures. A recent trend is towards on failure corrective maintenance (OFCM) with preventive maintenance (PM) restricted to servicing and legal requirements. Reliability is a crucial requirement is FMS. Interest in a real-time environment, such as an FMS, distributed processing with high level of local intel ligence is probably the only reliable solution when error recovery and consistent state recovery is of crucial. The level of risk can be reduced if the possible failures are outlined and recovery procedures designed and tested with simulation or with working machines where possible. In the automated factory, one should be quick to realize that, much of manufacturing is no longer binary, so efficiency alone is not the answer. One must be able to produce upon demand (operates conti nuously). The high cost of owning and maintaining production machinery dictates that; it be kept working on regular basis. This calls for equipment that is `flexible`, thus economical in the face of chan ge, (can be reprogrammed) The three ingredients to improving produc tivity are; efficiency, flexibility and therefore effectiveness. The highly integrated FMS offer the opportunity to combine both the effi ciency of transfer-lines and the flexibility of job-shops. So, model ling has become an essential part of the FMS design, management and organization technology. In the initial design and operation of such a system it is useful to have tools that can predict the system per formance for different operational conditions. So, we can see how these factors contribute to the overall system performance. `Production = operation + maintenance`, so the areas of flexibility which influence `production flexibility` can be outlined as; machine, pro duct, process, operation, routing, volume, expansion flexibilities. The required production flexibility implies special training of opera tors and rigid maintenance requirements, so to match to marketing strategy. There is a tendency within firms to expand control on decision-' making. For this kind of control, large amounts of information have to be gathered and processed. This is done by computer technology, only. Decision situations becoming more dynamic and complex as a re sult of environmental change. So, there is growing demand for `dyna mic models`, which give rise to consequences during several time peri ods. These ideas combined with rapid environmental change and centra lized decision-making grow the need for `dynamic control models`. (xv)One example of a computer Aided Manufacturing Systems (CAMS) where the human engages in supervisory control is the FMS, by the means of information, physical and decision links. A control system for an FMS has to consider and utilize all these links in order to achieve the best performance of the overall system. The FMS can be checked occa sionally through the periodic production of the manufacturing parts and the result can provide some statistical data for further FMS operati ons, and estimation can be obtained about the output capacity of the manufacturing system changes, as it's modules are changed. In this dissertation, to provide a general help for an FMS manager is aimed, so that he can justify his FMS whenever he wishes. Given the complexity of FMS, it is clear that a single analytical mo del can not solve all planning problems. The overall objectives and production targets must be served through the FMS decision hierarchy. The FMS production planning problem consists of organizing production such as to satisfy the master production plan as well as to obtain an efficient use of system resources. Because of the undeterministic nature of the environment, the planning and scheduling system for FM should be adaptable to unexpected events or system errors. These events and errors are common events in any manufacturing systems and usually can be corrected manually. But for FM with autonomous control, the planning and scheduling system as the system's decision maker must take proper measure. One option is to interrupt and stop the operation when any of the unplanned events occur. An alternative that can be used is a plan-revision to readjust the planning steps and by-pass the troubled spot of the system. So the plans can be modified when unex pected events occur ( eg; machine failures or breakdowns) and new go als can be accomodated while the current production is still being executed. In FMS operationally, production control and scheduling occur on-line. Due to the regular advance customers orders and peri odic maintenance, some job may be predetermined and scheduled, if new operations arise before completion of the schedule, or the number, of resources varies with time (i.e; the number of operations and/or the number of machines changes-failure and repair of a machine in ti me), or the characteristics and the values of the manufacturing const-, raintsare unknown or variable, the system is a `dynamic system`. Because the objective of an F.MS is to respond quickly to changes in customer demand without carrying large finished goods inventory, plan- revisions are necessary to maintain high speed effectiveness. It is feasible to consider total replanning in the event of deviations or disruptions, because it is certain that even the best production plans will not be followed.precisely in practice. The tight coupling bet ween production control and production planning in FMS environments, requires that, planning techniques must be developed which facilitate ' revision as and when required. FMS1 manager must make simulation in order to gain information on system's behaviour or to approximate the system for a given period of time to evaluate alternative decision rules for his FMS' operation. Because the system's behaviour and the results are time dependent the FMS simulation should be `dynamic simulation`. The parameters chosen for the model are `stochastic variables`, (i.e; machine failures, breakdowns.) (xvi)In this dissertation a computer model of a dynamic FMS is defi ned, so that it's behaviour may be studied without the need for buil ding, disrupting the operation of or destroying the real system. Also, the performance of the system may be predicted under different operating environments. Finally, it can be used as an educational and training device for managers who will be responsible for opera ting the real system. Because the production planning and production operation and management under different alternatives and operating conditions are the most important activities for an FMS manager, an analysis of the effects of changing FMS operating conditions by the possible random breakdowns on system's overall performance and effec tiveness is viewed by this model. The success in the short-term leads' to achieving the goal of an FMS, which is to maintain an overall effec tive configuration. By the developed model, the interrelationships bet ween alternative management policies, decisions and environmental con ditions effecting the operations of the firm are structured by way of `System Dynamics Approach`. So the manager can know what the effect of present policies is in order to know, when and why they should be changed, how they should be changed, and what the probable effect would be. The model requires as input; a set of policies, initial, values and environmental parameters. A General Purpose Simulation Software (GPSS) (i.e; DYNAMO) is selected and the decision variables such as; the levels of shipments of finished goods, customers demand, production, finished* goods inventory are defined. The analysis time and the frequency of using the analysis depend upon the user of the model or the FMS manager. (xvii)