Modeling and Analysis of a Manufacturing Plant Using Discrete Event Simulation (original) (raw)

Abstract

Today " s manufacturing systems are characterized by large number of complexities such as random arrival patterns of jobs, random processing times, random failure rates, random repair times, random rejection of parts, etc. The analytical models cannot capture all the randomness mentioned above into the models. There is a need to incorporate them into models to have a practical and real life model. Simulation comes handy in this aspect. Discrete Event Simulation (DES) is used to model a manufacturing system to predict its performance. The inputs to this model include arrival rate, batch size, setup time, processing time, machine breakdown rate, machine breakdown frequency, machines and their capacities, buffers, rejection percentage and inspection time. The outputs that are estimated are work in process, flow time, utilization and throughput.

Figures (8)

Fig 1. Completely finished Excentre It is an important part in piston pump, whose job is to convert rotary motion to reciprocating motion of piston. As shown below, its surface is grinded to mirror finish for smooth operation for over years.

Fig 1. Completely finished Excentre It is an important part in piston pump, whose job is to convert rotary motion to reciprocating motion of piston. As shown below, its surface is grinded to mirror finish for smooth operation for over years.

Table 1: Casting setup time

Table 1: Casting setup time

Based on the above results, the following graphs are drawn to find out the optimum parameters in each stage. The following are NON- DOMENATED or PARETO OPTIMAL points i.e., each point has equal importance with the other, but we can’t make a conclusion which is optimum among those. If a point is good on one objective, the other pareto optimal point is good on other objective. Note: Processes chipping, turning and drilling are efficient from the above plot. The remaining processes are not efficient; when the utilization and WIP are taken as the objectives.

Based on the above results, the following graphs are drawn to find out the optimum parameters in each stage. The following are NON- DOMENATED or PARETO OPTIMAL points i.e., each point has equal importance with the other, but we can’t make a conclusion which is optimum among those. If a point is good on one objective, the other pareto optimal point is good on other objective. Note: Processes chipping, turning and drilling are efficient from the above plot. The remaining processes are not efficient; when the utilization and WIP are taken as the objectives.

Fig 4: Utilization vs. Flow time

Fig 4: Utilization vs. Flow time

Note: Processes casting, chipping and turning are efficient from the above plot. The remaining processes are not efficient; when utilization and throughput are taken as objectives. Note: Processes Casting, Drilling and chipping are efficient from the above plot. The remaining processes are not efficient; when flow time and WIP are taken as objectives.

Note: Processes casting, chipping and turning are efficient from the above plot. The remaining processes are not efficient; when utilization and throughput are taken as objectives. Note: Processes Casting, Drilling and chipping are efficient from the above plot. The remaining processes are not efficient; when flow time and WIP are taken as objectives.

Fig 5: Utilization vs. Throughput Note: Processes casting, Red oxide, Shaft assembly, Inspection are efficient from the above plot. The remaining processes are not efficient; when the utilization and flow time are taken as the objectives.

Fig 5: Utilization vs. Throughput Note: Processes casting, Red oxide, Shaft assembly, Inspection are efficient from the above plot. The remaining processes are not efficient; when the utilization and flow time are taken as the objectives.

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References (7)

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