Performance Analysis of a Flexible Manufacturing System under Planning and Control Strategies
Principal, IGIT & Professor, Indian Institute of Technology-Delhi, India
Associate Professor, LAP-GRAI -IMS, University of Bordeaux-1, France
Assistant Professor, AMU, UP, India
Research scholar, Mechanical Engineering, Indian Institute of Technology-Delhi, India
Abstract: A typical Flexible Manufacturing System (FMS) has been studied under Planning, Design and Control (PDC) strategies. The chief objective is to test the impact of design strategy (routing flexibility) on system performance under given planning strategy (alternate system load condition) and control strategies (sequencing and dispatching rules). A computer simulation model is developed to evaluate the effects of aforementioned strategies on the make-span time, which is taken as the system performance measure. Shortest Processing Time (SPT), Maximum Balance Processing Time (MBPT) are the sequencing rules for selecting the part from the input buffer whereas for machine selection the dispatching rules are Minimum Number of parts in the Queue (MINQ), and Minimum queue with Minimum Waiting Time of all parts in the Queue (MQMWT). In this paper, the same manufacturing system is modeled under two different system load conditions. These load conditions are Full Balanced Load (FBL) and Unbalanced Load (UBL) with respect to machine load and processing time. The result of the simulation shows that there is continuous reduction in make-span with increase in routing flexibility when both machine load and processing times are unbalanced i.e., under UBL system condition. Modeling of FMS shows that each strategy causes a flow process for each part inside the system. The co-ordination and integration of flexible resources to guide these processes in a desirable direction (lesser conflicts) is important. An FMS can then become a platform for studying interoperability between the various potentially conflicting processes where flexibility helps to reduce these conflicts. The improved performance can then become a measure of this phenomenon.
Keywords: Flexibility, Planning strategy, Design strategy, Control strategy, Interoperability, FMS.
CITE THIS PAPER AS:
Subhash WADHWA, Yves DUCQ, Mohammed ALI, Anuj PRAKAS, Performance Analysis of a Flexible Manufacturing System under Planning and Control Strategies, Studies in Informatics and Control, ISSN 1220-1766, vol. 17 (3), pp. 273-284, 2008.
Owing to the globalization of the market, increasing demands of the customized products and rapidly changing needs of customers, the manufacturers are facing a problem of customer satisfaction and survival in the market among the various competitors. Therefore, they are searching such a manufacturing system, which fulfill the demand of the market within due dates and it should be available on lower cost. Thus, they can continue to exist in the global market. Among all the existing manufacturing system, they require a manufacturing system, which is having the flexibility to make the customized product with medium volume. Therefore, they are allured to the flexible manufacturing system (FMS), which is a compromise between job shop manufacturing system and batch manufacturing system. Flexible manufacturing system is the system, which is equipped with the several computer-controlled machines, having the facility of automatic changing of tools and parts. The machines are interconnected by Automatic Guided Vehicles (AGVs), pallets and several storage buffers. These components are connected and governed by computer using the local area network. The exquisiteness of this system is that it gleaned the ideas both from the flow shop and batch shop manufacturing system. The prominent literature has the several definitions of the flexible manufacturing system which is given by the many a researchers like Upton (1994), Wadhwa et al. (2005) etc. Wadhwa and Rao (2000) have defined the flexibility as the ability to deal with change by judiciously providing and exploiting controllable options dynamically. Due to this flexibility, some decision-making problems have occurred in the system. Therefore to run the system efficiently, the judicious combination of flexibility and information based integration and automation. Thus, most real FMS have various planning, designs and control strategies to harness this flexibility when required.
In the planning strategy, the load condition of the system should be defined clearly. From the past researches, it can be easily concluded that the system should be fully balanced. The balancing of the system can be viewed as the same distribution of task among all the machines. Raman et al. (1989) have investigated that the performance of various dispatching rules depends upon the degree of workload imbalance whereas. The routing flexibility also helps in the scheduling of jobs in very efficient manner (Sethi and Sethi 1990). Wadhwa and Bhagwat, (1998) has performed simulation experiments under various conditions of machine load and processing times balance. They have considered the impact of decision and information delays under various conditions of machine load and processing time balance. In the last decade, some other researchers like Cagliano et al. (2000), Kumar and Shanker (2001) have also shown the impact of different load condition of the system. But all of these prominent researches are concentrated on the impact of balancing of the workload and not focused on the interaction among other strategies. In the present paper, the impacts of the different strategies are studied simultaneously with the different loads in the system.
Within the study of design strategies, the diverse types of flexibilities are studied. According to Sethi and Sethi (1990), there are eleven flexibilities are existed in the system. To work with these all the flexibilities, a very efficient decision support system is required and effectively handling of all the flexibility is more tedious and difficult task. Therefore, Browne et.al.(1984) have comprehended it in only eight types, which are known as: machine flexibility, process flexibility, routing flexibility, operation flexibility, product flexibility, volume flexibility, expansion flexibility and production flexibility. Among all these, the routing flexibility is one possible manifestation of manufacturing flexibility at the shop floor. Browne et al. (1984) have stated that routing flexibility is potential flexibility, which is utilized only when needed, such as a part being re-routed when a machine breakdown occurs. In the late 1990’s, Caprihan and Wadhwa (1997) have presented a framework based on a Taguchi experimental design for studying the impact of varying levels of routing flexibility. They concluded that an increase in routing flexibility is not always beneficial. A precious study has been done by Chan (2001) to give an idea about the effect of routing flexibility on an FMS. Another definition of the routing flexibility has been given by Barad et al. (2003), according to this the routing flexibility is the capability of processing a part through varying routes. Mohammed and Wadhwa (2005) have also explained the effectiveness of the routing flexibility in the partial flexible manufacturing system. They have shown the impact of flexibility on the three different levels of routing flexibility. The combined effects of the routing flexibility with real world situations are not studied in the previous researches while the present paper has provided a new insight to get the combined effect of both. The impact of these is also combined with another strategy known as control strategies.
The controlling action in any manufacturing system is having increasing importance. In the flexible manufacturing system, the real time part priority control and routing machine priority are the two control actions, which are studied under the alternative control strategies (Wadhwa and Browne, 1990; Caprihan and Wadhwa 1997, Wadhwa and Bhagwat, 1998 etc). Choi and Malstrom (1988) have given a new thought about the combination of rules and they have also shown that the combination of SPT and MINQ (minimum queue at buffer) is dominated over all the other sets of rules. Karsiti et al. (1992) have shown that in most of the FMS systems, the combination of SPT/MINQ performs better. Chan (2003) has studied the effect of dispatching and routing decisions on the performance of an FMS with the impact of buffer capacities. In all the above-mentioned researches, the different dispatching and sequencing rules are studied, but all these is no consideration of load condition any type of flexibility.
In the present paper, a study has been accomplished with the combination of planning, design and control strategies. This paper has employed the two different load conditions in the planning strategy that is known as Full Balance Load (FBL) and Unbalanced Load (UBL). While in the design strategy, the impact of routing flexibility has been taken in to the account. The sequencing and dispatching rules are taken as the control strategies. In this study, SPT and MBPT are considered as the sequencing rule whereas the dispatching rules are MINQ and MQMWT (minimum queue with minimum waiting time). For studying all the above, the make span time is considered as the performance measure. However, this paper presents a real time simulation and the effects of routing flexibility with the different sets of sequencing and dispatching rules.
The remainder of the present paper has been organized in the following manner: section 2 delineates the description of an FMS and the problem with assumptions. The working procedure of the simulator has been illustrated in the section 3. In section 4, the obtained results have been discussed. The GRAI macro reference model has been depicted in section 5. Finally, the summary and conclusions with a note about its future scope is reported in the section 6.
The main aim of this paper is for knowing the impact of the design strategy and different combinations of planning and control strategies and on the whole FMS. In the proposed model, it has been clearly shown routing flexibility as a design option can reduce the make-span effectively. It is seen that improvement from no flexibility to first flexibility level is most productive. With two different conditions of the system and different sets of sequencing rules, the make span is seen to be reduced. One of the valuable implications is that judicious level of flexibility depends on planning and control strategies. Further flexibility helps to change the flow processes for each part in a desirable direction. Such studies offer a good platform to study interoperability issues between two flexible subsystems in any enterprise. The aim of system design and control is to integrate the flexible flow processes to minimize detrimental process flow conflicts. Make-span can act as a measure for interoperability potential between two or more flow processes in flexible systems. A future research direction is the development of generic models that focus on the synchronization between knowledge, objectives, decisions and information systems to create minimal conflicts between different flow processes. It appears useful to develop these generic models with capabilities to model the multiple entity flows using suitable simulation primitives.
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