formulation of simulation models for flexible manufacturing systems

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19 International Conference on Production Research

FORMULATION OF SIMULATION MODELS FOR FLEXIBLE MANUFACTURING SYSTEMS O.D. Quiroga, L.M. Ciorciari and G.H. Rossetti Department of Industrial Engineering, Universidad Nacional del Litoral, Santiago del Estero 2829, Santa Fe, Argentina

Abstract A Flexible Manufacturing System (FMS) consists of a computer-controlled, integrated configuration of machines with automated material handling systems. Problems related to flexible manufacturing technology are relatively complex compared to traditional manufacturing systems in which lead times are longer, inventory levels are higher, and utilization rates are lower. In order to take full advantage of the flexibility of the system, operation assignment, planning and scheduling should be done in a collective way. This works proposes the development of simulation models for studying the behaviour of complex FMS. The models are formulated and validated from real case studies, and take into account some aspects not considered in the bibliography, i.e. 1) the machine efficiency is considered less than 100%. 2) The time of part movements among machines is not zero. Keywords: Simulation Models, FMS, Maintenance.

1 INTRODUCTION The FMS is capable of optimizing each step of the manufacturing process, and can involve one or more operations or processes (turning, drilling, milling, and surface grinding), inspection and assembly tasks. An FMS processes many parts and it adapts to changes in the part designs. The limited resources such as pallets and fixtures must be changed or reassigned, and tool magazines must be reconfigured after one batch is completed so as to process the next batch quickly. The FMS must be configured for each batch. After one batch is completed, there is a switch-over involved, e.g., a machine tool magazine must be reconfigured, routing patterns changed, pallets/fixtures changed, etc. The operational problem is to determine which part type goes into a well-defined batch [1]. The main advantages of utilising FMS are: 1) Shorter product development cycle. 2) Reduced set up time and work – in – process (WIP). 3) Faster, lower- cost changes from one part to another which will improve capital utilisation. 4) Consistent and better quality, due to the automated control. 5) Increased product variety to satisfy customer needs. 6) Flexibility to adapt to changes in the market. 7) Improved capital/equipment utilisation. One of the objectives of an FMS is to achieve the flexibility of low volume production while keeping the efficiency of high–volume mass production [2]. Various decisions must be made in order to achieve this efficiency. One of them is the selection of machines and assignment of the operations and required tools to machines to be processed. However, the flexibility and efficiency of an FMS is limited by the availability of equipments. In mass production systems the production planning and control are easier than those of FMS. In FMS each machine can perform a variety of operations on a part when all the needed fixtures, tools, and pallets are available. For that reason, the set up time is reduced. Machines are generally equipped with tool magazines where they can hold several tool types to perform various operations on the part. Increase in part variety means an increase in the cutting tool types. This requires a large tool mix and a great degree of sophistication to plan, control, and monitor tools

in the plant. The works of [3] and [4] recommend that the assignment and scheduling must be done at the same time, in order to obtain the total advantages that the system flexibility permits. The work of [5] is based on the used of simulation methodologies. They research the impact which produces the tool requirement selection and the rules to send them in an environment where they are shared. This paper presents the development of simulation models to analyse the behaviour of complex FMS by using the SIMUL8 package. The models are formulated and validated from real case studies taken from [3], [6] and [7]. The models take into account some aspects not considered in the bibliography, i.e. 1) the machine efficiency is considered less than 100%, and it is supposed that the machines could be broken or they need maintenance. 2) The time of part movements among machines is not zero. This work is organised as follows: Section 2 presents the development of the simulation model. Section 3 shows the simulation model validation by considering [3] and [7] case studies. Section 4 introduces the extended simulation models. Finally, Section 5 presents the conclusions of this work. 2 DEVELOPMENT OF THE SIMULATION MODEL The behaviour of a system that evolves trough the time could be studied by means of the development of a simulation model. This model has a set of hypothesis related with the system operation. In this work an FMS is modelled by using the powerful simulation package SIMUL8. A simulation scheme in SIMUL8 has different objects: storage areas, storage bins or queues, and work centres such as machines. These objects are put on the screen and connected by route arrows, which represent the structure where the transactions of the system model flow. These transactions could be jobs, products, clients, etc. and SIMUL8 call them work items. The simulation model has been developed for representing, verifying, and analysing programs of part

processes in FMS. The model has three blocks: A) The Entrance is the initial point of the workpieces or parts which are going to be processed. Each input object corresponds to a part type. B) The FMS is the Flexible Manufacturing System. The processing of the workpieces is developed in this block. It has a Central Buffer, which is a temporal storage point of the workpieces waiting for a machine in order to be processed. C) The Exit, once each workpiece finalises its manufacturing route, the workpiece goes to this block where this event is registered. The simulation model is developed from the second case study presented in [3]. This work analyses the scheduling problem in FMS, i.e. the assignment of workpieces, tools, and machines in an FMS. The authors develop a mathematic model for assigning the machines, machine operations, and tools by minimizing the completion time and the total processing time. The Lingo 6.0 package is used for solving the scheduling problem. The first simulation model considers the following hypotheses: 1) the number of parts is fixed and known. 2) Each processing workpiece is assigned only to a unique machine. 3) The machines do not broke while they are working. 4) The time of part transport among machines are not considered. 5) There are not breakdowns for the material-handling system. 6) There are not setup times in the machines. 7) The following points are well-known: a) the manufacturing route of each workpiece. b) The workpiece assignment to the machines. c) The processing time in each machine. Moreover, this model considers transparent assignment for the tools and machines since it has been taken from the solution proposed by [3], and it is used to establish the processing time of each job in a machine, which is enough to start a simulation process. The Cycle Matrix is used as a routing method for the workpieces and for assigning the processing times. Figure 1 shows as an example the case of a Cycle Matrix, which has been built from the [3] second case study.

machines are taken from the solution proposed by [7], which are better than the assignment case of [3]. 3 THE RESULTS 3.1 The first case For this case, the simulation model has been developed from data of [3], which represents the real system. This work considers two case studies. In the second one, a system with 5 parts, each part requires 5 operations is taken into account. The FMS has 5 machines for processing the parts, and each machine has a tool magazine with a capacity of 60 tools. There are available 22 tool types. The simulation model is applied on the optimal production schedule found by [3]. The results found by means of the model are: A) the percentage of utilisation and inactivity of each machine, B) the completion times of each part, and C) the working diagram of each machine through the time. These results are depicted in Table 1and Figure 2. Table 1: Results Obtained by SIMUL 8 (simulation model developed from [3]) Simulated Object Machine 1 Machine 2 Machine 3 Machine 4 Machine 5 EXIT

Performance Waiting Working Waiting Working Waiting Working Waiting Working Waiting Working Number of completed parts

Results 29.12 % 70.88 % 26.86 % 73.14 % 6.77 % 93.23 % 19.64 % 80.36 % 12.19 % 87.81 % 5

Machine 1

Average time in the system (Last operation on Part 2 in Machine 1)

433 min

Machine 2

Average time in the system (Last operation on Part 1 in Machine 2)

420 min

Machine 3

Average time in the system (Last operation on Part 3 in Machine 3)

443 min

Machine 4

Average time in the system (Last operation on Part 5 in Machine 4)

440 min

Machine 5

Average time in the system (Last operation on Part 4 in Machine 5)

413 min

Figure 1: Example for the development of a Cycle Matrix This matrix considers the machines (work centres) working and executing successive tasks. There are three types of tasks, i.e., Work, Load to, and Unload from. It is specified the required time to do these tasks, and a work characterised by labels, such as Work Type and Job, all defined in the program. The model ran in all cases in a Pentium IV 2.8 GHz with a memory of 512 Mb RAM. The second simulation model also considers the same hypotheses. In this case the assignment of the tools and

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19 International Conference on Production Research

Figure 2: Diagram of time assignments of FMS machines, case study of [3]. Table 1 presents the information which is used for the analysis and the validation of the model. The results found in [3] are similar to the results of Table 1 and Figure 2. 3.2 The second case This case considers a model in which the optimal production schedule found by [7] is applied. The work of [7] considers the same system as [3]. The optimal production schedule for the [7] case is better than the [3] case, since the maximum completion time is 437 minutes for [7], and for [3] is 443 minutes. In Figure 3 and Table 2 are depicted the results found by this second model. Table 2: Results Obtained by SIMUL 8 (simulation model developed from [7]) Simulated Object Machine 1 Machine 2 Machine 3 Machine 4 Machine 5 EXIT

Machine 1

Machine 2

Machine 3

Machine 4

Machine 5

Performance

Results

Waiting Working Waiting Working Waiting Working Waiting Working Waiting Working Number of completed parts Average time in the system (Last operation on Part 2 in Machine 1) Average time in the system (Last operation on Part 1 in Machine 2) Average time in the system (Last operation on Part 3 in Machine 3) Average time in the system (Last operation on Part 5 in Machine 4) Average time in the system (Last operation on Part 4 in Machine 5)

17.39 % 82.61 % 36.61 % 63.39 % 4.58 % 95.42 % 20.82 % 79.18 % 10.98 % 89.02 % 5

427 min

420 min

437 min

395 min

413 min

Figure 3: Diagram of time assignments of FMS machines, case study of [7]. The results found in [7] are similar to the results obtained by the simulation model and presented in Table 2 and Figure 3.

4 THE EXTENDED MODELS In this section two extended simulation models for FMS are proposed by taking into account some aspects which usually are not considered. The models which run with the solutions of the optimal production schedule taken from [3] and [7] are considered as references, i.e. the first and second cases proposed in Section 3. The extended models which are presented in Sections 4.1 and 4.2 consider the following points: I) Machine efficiency less than 100%. It is considered that all machines need maintenance, and is supposed for the analysis that there is an exponential distribution between breakdowns with an average value of 500 minutes, and for the maintenance operation it is chosen a fixed value of 30 minutes. II) Part transport times are considered. In the Cycle Matrix the transport times from and to the Central Buffer are configured. For the material-handling system it is considered the load, transport and unloads times, and it is chosen a time of a minute for this total process. The transport times from the Entrance to the Central Buffer and from this to the Exit are neglected. 4.1 The first case In this case, a model has been developed from the second case study of [3], i.e. a system with 5 parts, each part needs 5 operations. The FMS has 5 machines for processing the parts, similar to the case of the simulation model developed in Section 3.1. This extended model is applied on the optimal production schedule found by [3], and the points I and II presented above are also considered. The results found by this extended model are depicted in Table 3 and Figure 4.

Table 3: Results Obtained by SIMUL 8 (simulation model developed from [3]) Simulated Object Machine 1

Machine 2

Machine 3

Machine 4

Machine 5

EXIT

Machine 1

Machine 2

Machine 3

Machine 4

Machine 5

Performance

Results

Waiting

22.69 %

Working

65.64 %

Stopped Waiting

11.67 % 35.02 %

Working

64.98 %

Stopped Waiting

0.00 % 17.32 %

Working

82.68 %

Stopped Waiting

0.00 % 28.79 %

Working

71.21 %

Stopped Waiting

0.00 % 9.45 %

Working

78.88 %

Stopped Number of completed parts Average time in the system (Last operation on Part 4 in Machine 1) Average time in the system (Last operation on Part 2 in Machine 2) Average time in the system (Last operation on Part 3 in Machine 3) Average time in the system (Last operation on Part 5 in Machine 4) Average time in the system (Last operation on Part 1 in Machine 5)

11.67 %

4.2 The second case This extended model considers the effects of points I and II presented in Section 4, and is applied on the optimal production schedule found by [7]. Table 4: Results Obtained by SIMUL 8 (simulation model developed from [7]) Simulated Object Machine 1

Machine 2

Machine 3

Machine 4

5 Machine 5 483 min EXIT 475 min

487 min

514 min

487 min

Machine 1

Machine 2

Machine 3

Machine 4

Machine 5

Performance

Results

Waiting

22.69 %

Working

65.64 %

Stopped Waiting

11.67 % 35.02 %

Working

64.98 %

Stopped Waiting

0.00 % 17.32 %

Working

82.68 %

Stopped Waiting

0.00 % 28.79 %

Working

71.21 %

Stopped Waiting

0.00 % 9.45 %

Working

78.88 %

Stopped Number of completed parts Average time in the system (Last operation on Part 2 in Machine 1) Average time in the system (Last operation on Part 1 in Machine 2) Average time in the system (Last operation on Part 3 in Machine 3) Average time in the system (Last operation on Part 5 in Machine 4) Average time in the system (Last operation on Part 4 in Machine 5)

11.67 % 5

485.50 min

446.43 min

495.50 min

395.00 min

439.43 min

Figure 4: Diagram of time assignments of FMS machines. Table 3 shows that the maximum completion time is increased from 413 minutes to 514 minutes. This rise is due to the maintenance in Machine 1 and 5, and Figure 4 depicts the effects of maintenance.

Figure 5: Diagram of time assignments of FMS machines.

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19 International Conference on Production Research

In Table 4 are presented the results obtained by this extended model. The maximum completion time is increased from 437 minutes (simulation model proposed in Section 3.2) to 495.5 minutes. This effect is caused by the maintenance in Machine 1, and could be seen in Figure 5.

5 CONCLUDING REMARKS This work presents different formulations of simulation models for FMS by using the package Simul8. Simulation models for FMS have many advantages, i.e. 1) they allow to study and experiment with real FMS, in applications of complex cases for industry. 2) They permit to evaluate performance parameters. This paper makes a contribution in the analysis of FMS by considering some aspects that bibliography does not take into account. In the first part, two simulation models have been proposed from real data [3] and [7]. In Section 4, the models are reformulated by considering that the machine efficiencies are less than 100%, and they need maintenance. An exponential distribution between breakdowns (with an average value of 500 minutes) and a fixed value of 30 minutes for the maintenance operation are chosen. Moreover, part transport times are considered. 6 REFERENCES [1] Sharafali, M., Co, H., and Goh, M. 2004, Production Scheduling in a Flexible Manufacturing System Under Random Demand, European Journal of Operational Research 158, 89–102. [2] Roh, H.K. and Kim, Y.D. (1997), “Due Date Loading and Scheduling Methods for a Flexible Manufacturing System with an Automatic Tool Transporter” International Journal of Production Research 35, 11, 2989–3003. [3] Gamila, M.A., and Motavalli, S., 2003, A Modeling Technique for Loading and Scheduling problems in FMS, Robotics and Computer-Integrated Manufacturing 19, 45–54. [4] Quiroga, O.D., Zeballos, L.J., and Henning, G.P., 2004, Machine Loading, Tool Allocation and Scheduling Problems in Flexible Manufacturing Systems, 4th CIRP Intern. Seminar on Intelligent Computation in Manuf. Eng. (ICME) ’04, July 2004. Sorrento (Italy). [5] Kashyap, A.S., and Khator, S.K., 1996, Analysis of Tool Sharing in an FMS: a Simulation Study, Computers and Industrial Engineering 30, 1, 137– 145. [6] Sarin, S.C. and Chen, C.S., 1987, The Machine Loading and Tool Allocation Problem in Flexible Manufacturing System, Int. Journal of Production Research 25, 7, 1081–1094. [7] Quiroga, O.D., Zeballos, L.J., and Henning, G.P., 2005, A Constraint Programming Approach to the Tool Planning and Scheduling Problems in FMS. Proceedings of the IEEE International Conference on Robotics and Automation ICRA05. pp. 3726-3731. April 2005. Barcelona (Spain).