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APLICACIÓN DE LA TEORÍA DE LAS LIMITACIONES SOBRE PLANIFICACIÓN DEL SISTEMA DE DBR
Transcript of APLICACIÓN DE LA TEORÍA DE LAS LIMITACIONES SOBRE PLANIFICACIÓN DEL SISTEMA DE DBR
DRUM-BUFFER-ROPE AND BUFFER MANAGEMENT
"The scheduling system of TOC is often referred as drum-buffer-rope (DBR) system. DBR systems operate by developing a schedule for the system’s primary resource constraint."
The concept of the TOC can be summarized as:
* Every system must have at least one constraint.
* The existence of constraints represents opportunities for improvement.
The logistics paradigm of the TOC has evolved from the scheduling software called Optimized Production Technology (OPT) which in turn, is based on the following nine rules:
1. Balance flow, not capacity.
2. The level of utilization of a non-bottleneck is not determined by its own potential but by some other constraint in the system.
3. Utilization and activation of a resource are not synonymous.
4. An hour lost at a bottleneck is an hour lost for the total system.
5. An hour saved at a non-bottleneck is just a mirage.
6. Bottlenecks govern both throughput and inventories.
7. The transfer batch may not, and many times should not, be equal to the process batch.
8. The process batch should be variable, not fixed.
9. Schedules should be established by looking at all the constraints simultaneously. Lead times are the result of a schedule and cannot be predetermined.
APPLICATION OF THEORY OF CONSTRAINTS ON SCHEDULING OF DRUM-BUFFER-ROPE SYSTEM
* The aim of this paper is to focus on philosophy of TOC and study the behavior of DBR in a serial production line through simulation.
The principle consists of five focusing steps:
1. Identify the system’s constraint(s).
2. Decide how to exploit the system’s constraint(s).
3. Subordinate everything else to the above decision.
4. Elevate the system’s constraint(s).
5. Do not let inertia become the system constraint.
The first part of this step makes TOC a continuous
the system schedule or the pace at which the constraint works.
is strategically placed inventory to protect the system’s output from the variations that occur in the system.
provides communications between critical control points to ensure their synchronization.
The DBR methodology synchronizes resources and material utilization in an organization. Resources and materials are used only at a level that contributes to the organization’s ability to achieve throughput.
"... it is difficult to conclude with confidence that one system is better than the other. However, the general consensus derived from the comparisons is that an organization needs a combination of these production control methods to take advantage of each system’s strength".
During model development, a serial production line having five machines and four intermediate buffer locations have been considered. The model has been developed using simulation software EXTEND 6.
throughput obtained from the serial production line model vs. throughput calculated from Bluemenfield’s formula.
* Comparison of DBR system with conventional serial production line reveals that average work-in-process (WIP) and average wait time of items can be drastically reduced
* the utilization of machines can be significantly improved in a DBR system.
RESULT & DISCUSSIONS
The steady state behavior of both the systems is studied. The model is run for 90000 time units and data is collected for 60,000 time units leaving 30,000 time units as warm-up period. The utilization of last machine in a DBR system having mean processing time of 10 units for all the machines with a coefficient of variation (CV) equal to 0.5 and intermediate buffer capacity of one is plotted in Figure for estimating the warm-up period.
Variation of Throughput with CV and Buffer Capacity
Production Smoothing in DBR System
Although throughput of DBR system decreases in comparison to serial production line, it improves utilization of all the machines, reduces in-process inventory, reduces average wait time of items and percentage of blocking of the machines as shown in Table 2 for CV = 0.3 and B=3.
Shifting of Bottleneck
In the next set of experiments, the mean processing time a machine is considered to be 10 except the bottleneck operation having processing time of 50 with CV = 0.5 and B = 3. The position of bottleneck (P) operation is systematically changed from 2 to 5 in the line.
Throughput vs. Buffer Size
A graph has been plotted between throughput and buffer size variation as shown in Fig 9 considering mean processing time of 10, CV = 0.1 and line length equal to 5.
Throughput vs. change in Mean Processing Time
A graph has been plotted between throughput and variation in mean processing time as shown in Fig 10 considering CV = 0.1 and line length equal to 5. The signal loop is connected to machine 2 in all the cases.
Throughput vs. Change in Number of Machines
A graph has been plotted between throughput and change in number of machines as shown in Fig 11 considering CV = 0.1 and mean processing time 10. The signal loop is connected to machine 2 in all the cases.
Throughput Estimation by the Application of Neural Network
A generalized analytical formula for throughput estimation (jobs per hour) of a DBR production line with variable processing times and limited buffer capacity happens to be difficult. Therefore, a methodology has been proposed using neural networks based on back propagation algorithm for accurate prediction of throughput considering mean processing time, coefficient of variation, buffer size, number of stations and signal buffer.
After the normalization, they are trained in the neural network program using the back propagation method written in C++. The parameter set for the neural network training is as follows:
* A simulation of a flow shop operation was done by the use of Extend 6 software. The model is allowed to run for 30000 time units as warm-up period and the data collection period consists of next 60,000 time units.
* An attempt has been made to establish a relation between the system throughput and the parameters affecting it. The results are compared with the conventional production system and are reported to be matching with the literature.
* A generalized analytical formula for throughput estimation (jobs per hour) of a DBR production line with variable processing times and limited buffer capacity happens to be difficult. Therefore, a methodology has been proposed using neural networks based on back propagation algorithm for accurate prediction of throughput considering mean processing time, coefficient of variation, buffer size, number of stations and signal buffer.
The successful enterprises deliver products and services in shorter throughput time and turnover inventory as quickly as possible. Three important approaches to achieve these goals are Materials Requirements Planning (MRPI, MRPII), Just-in-Time (JIT), and Theory of Constraints (TOC). TOC seems to be a viable proposition because it does not require costly affair of system change; rather it is simply based on scheduling of the capacity constraint resources. The scheduling system of theory of constraints (TOC) is often referred as drum-buffer-rope (DBR) system. DBR systems operate by developing a schedule for the system’s primary resource constraint. Based on simulation of a flow shop operation considering non-free goods, this study suggests that the performance of DBR results in lower in process inventory compared to conventional production system. In addition, the behavior of the system when bottleneck shifts its position has been studied. A methodology has been proposed using neural networks based on back propagation algorithm for accurate prediction of throughput considering mean processing time, coefficient of variation, buffer size, number of stations and signal buffer.