An intelligent controller for manufacturing cells

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International Journal of Production Research Publication details, including instructions for authors and subscription information: http://www.tandfonline.com/loi/tprs20

An intelligent controller for manufacturing cells a

Y.-L. SUN & Y. YIH

a

a

School of Industrial Engineering, Purdue University , West Lafayette, IN, 47907-1287, USA Published online: 24 Oct 2007.

To cite this article: Y.-L. SUN & Y. YIH (1996) An intelligent controller for manufacturing cells, International Journal of Production Research, 34:8, 2353-2373, DOI: 10.1080/00207549608905029 To link to this article: http://dx.doi.org/10.1080/00207549608905029

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An intelligent controller for manufacturing cells

Downloaded by [Purdue University] at 10:14 20 May 2015

Y.-L. SUN? and Y. YIHtf To meet multiple performance objectives and handle uncertainty during production, a flexible scheduling system is essential. In this study, a neural network based control system is proposed to adapt different scheduling strategies dynamically for a manufacturing cell. The proposed control system consists of an adjustment module and the associated equipment controller for each machine and the robot. At a decision point, the adjustment module will determine the relative importance for each performance measure according to the current performance levels and requirements. The equipment level controller, implemented by a neural network, will select the proper dispatching rule based on its status and the relative importance levels. The problem, which arises from the discrepancy of the user specification and what neural networks are trained by, is addressed. From the simulation results, the proposed refinement procedure could recover this problem so that the controller can perform closer to the actual requirements. In addition, the performance of the controller in the multiple criterion environments and its adaptability are investigated through simulation studies. The results show that this proposed controller performs well under the multiple criterion environments and is able to respond to changes in objectives during production.

Introduction It is well known that the performance of a manufacturing system relies heavily on its control strategies. However, the dynamic control of such a system is very complicated because of the complex interaction among machines, material handling devices, and storage buffers (O'Grady and Lee 1988). Commonly, the control in a manufacturing system is broken into a control hierarchy for each implementation and limited responsibility. Two major hierarchical control structures can be found in the literature: the automated manufacturing research facility (AMRF) and the advanced factory management system (AFMS). The control hierarchy in the A M R F model consists of five levels, which are facility level, shop level, cell level, workstation level, and equipment level (Jones and McLean 1986). For the AFMS model, four control levels are included which, from the top down, are factory level, job shop level, work centre level (cell level), and work unit level (Chryssolouris 1986). Since both hierarchies have a rough equivalence in levels (O'Grady et a/., 1987) and a controller for a manufacturing cell is of interest, our control structure will follow the method that the cell level controller provides the necessary guidelines for the equipment level controller to meet the requirements from the shop floor level. T o develop a cell controller, many researchers adopt a rule based system or a n expert system. Unfortunately, a well-designed rule based system may not be easily constructed and may require considerable computational effort if there is a large number of rules (O'Grady el a/. 1987). Therefore, several researchers have tried to 1.

Revision received August 1995. Industrial Engineering, Purdue University, West Lafayette, IN 47907-1287, USA. f To whom correspondence should be addressed.

t School of

0020-7543196 512.00 0 1996 Taylor & Francis Lld.


Y.-L. Sun and Y. Yih

apply artificial neural networks in the scheduling problem (Foo and Takefuji 1988a, b; Yih et 01. 1993) o r in the rule selection (Yih and Jones 1992, Rabelo et ul. 1993). Artificial neural networks have been studied for several years in the hope of achieving the conceptual tasks and have been successfully applied in pattern recognition, classification, and so forth. Since the neural network classifiers are non-parametric and need weak assumptions regarding the distributions (Lippmann 1987), they can be used to classify the current status and the desired performance levels into a subspace corresponding to a control strategy when applied to the scheduling problem (Yih and Jones 1992). T o meet the various production requirements, a cell control system should be able to perform well under multiple criterion environments and quickly react to criterion changes. T o date, most work for a cell control system has only considered a single objective o r uses cost functions as system performance measures. In this paper, a neural network based controller is developed and verified in multi-criterion environments. Its adaptability to changing objectives is also investigated. This paper is organized as follows. In 5 2, the literature is related to the cell o r shop floor control and the neural network based scheduler will be reviewed. A manufacturing cell and the simulation model are provided in 5 3. The proposed neural network based controller is described in §4. In 5 5, several experiments are conducted and analysed to demonstrate the capability of the proposed controller. Finally, the conclusions and future work are addressed in 5 6. 2.

Literature review

In general, an intelligent controller must integrate the decision-making process with a control mechanism (O'Grady and Lee 1988, Davis e / 01. 1992). Davis er ul. (1992). suggested that each control module should have four maior functions: -assessment, optimization, execution, and monitoring. In their proposed architecture, scheduling- .problems are formulated by the assessment function and then solved by the optimization function. The execution function communicates with the subordinate controllers, while the monitoring function provides feedback information to the assessment function for further control. Within this control architecture, most of the general concepts in developing an intelligent controller have been included. Several researchers have attempted to use the knowledge-based approach to model the shop floor control problem (Adachi et 01. 1989, Farhoodi 1990, Pluym 1990). Under this approach, a central database with several production rules handles scheduling and monitors system status. Typically, these production rules are generated based on the simulation results from different scenarios, o r the knowledge from the experience of schedulers. Whenever a decision-making point is encountered, the database is scanned to find the condition which could match the current situation, and the associated action is then executed. The problem is that to generate a database consisting of every possible situation for a system is not an easy task. Besides, if this database is large o r the production rules are complex, it will take a long time to search the database and it is impractical for real-time implementation. Cell control systems have been investigated by several researchers. Chryssolouris (1986) proposed a decision making procedure, called MADEMA, to properly assign the resources to production tasks. MADEMA formulates scheduling as a multicriterion decision-making problem. The scheduling task is done in five steps: determining alternatives, determining criteria, determining the consequences regarding to the criteria, applying decision rules, and selecting the best alternative. This



2355

method was then implemented in a cell control system and compared with dispatching rules such as LCFS, FCFS, GPT, and SPT (Chryssolouris et al. 1988). Although the MADEMA performed well in test cases, the authors concluded that the performance could be further improved by more accurate estimation of the criterion values. O'Grady and Lee (1988) proposed a cell control system, called PLATO-Z, by using a rule-based expert system and a multi-blackboard/actor model. This control framework was further implemented by an object-oriented programming technique (O'Grady and Seshadri 1992). Wu and Wysk (1988, 1989) also proposed a multi-pass expert control system for flexible manufacturing cells. Under their proposed system, some candidate rules are selected by the knowledge base and then the performance of each candidate rule is evaluated by simulation. The weighted objective values are compared in order to achieve the multi-criterion objective. Cho and Wysk (1993) then refined it by using a neural network instead of the knowledge based system for selecting the candidate rules in the initial stage. Since the simulation for each candidate rule is conducted after a certain time window, a large computational effort could be expected and thus the real-time response could be limited. Other works to refer to include Adachi et 01. (1989), Gupta et al. (1989), and Huang and Chang (1992). In recent years, many researchers have utilized neural networks in dealing with conceptual tasks such as pattern recognition, classification, and optimization. Artificial neural networks use the interconnection of computational elements to mimic conceptual tasks. Among many network topologies and learning algorithms, the Hopfield network and the multi-layer perceptron are preferred by several researchers for scheduling problems. The Hopfield network can be applied as an associative memory or to solve optimization problems, e.g. the travelling salesman problem. Foo and Takefuji (1988a, b) modified the Hopfield network for solving job shop scheduling problems. The scheduling problem was first mapped into a two dimensional matrix representation. Feasibility constraints and performance measures were then formulated as the energy function, namely cost function. When the schedule is not feasible or the performance is far from expectation, the energy function will output a large value. The solution is obtained by reducing the energy in the network. The authors concluded that this approach could produce near-optimal solutions though the optimality was not guaranteed. In addition, it was claimed that the proposed approach would not be feasible in a large scale problem. Zhou et al. (1991) then modified this approach by using a linear cost function and concluded that this modification not only produced better results but also reduced network complexity. However, this approach is not appropriate for implementation as a real-time scheduler in a dynamic environment because it takes long computational time to formulate and solve the problem a t each decision point. Other works related to scheduling and the Hopfield network include Zhang et al. (1991) and Arizono et al. (1 992). Chryssolouris et al. (1990) suggested that a multi-layer perceptron could be used as the inverse function of simulation. Conventionally, simulation was commonly used to determine performance levels, given the system configuration and a certain control strategy. In their approach, neural networks were trained to learn the inverse function which estimates the system parameters by the given performance measures. It was concluded that the neural network could be used as a tool for the design of a manufacturing system and could reduce the trial-and-error runs of simulation.



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Several works have used multi-layer perceptrons to deal with scheduling o r candidate rules selection. Potvin el a/. (1992) modified the network structure but still utilized the backpropagation learning algorithm to build up the dispatcher for automated vehicles. Rabelo et al. (1993) used modular neural networks to serve as a candidate rule selector. As mentioned above, Cho and Wysk (1993) utilized the multilayer perceptron to take the place of the knowledge based system in selecting some candidate rules. In their proposed structure, the neural network is used to select candidate rules based on a single objective. Yih and Jones (1992) proposed using multi-layer perceptrons in selecting some candidate rules for further evaluation of their performance. In their proposed approach, a multi-layer perceptron will take the attributes describing the system configuration and the performance measures, and will output a proper matching score for each dispatching rule. Also, this approach is constructed for multiple criterion objectives. It is unfortunate that their idea was not implemented for verification. In this study, we will adopt this idea in constructing a rule selector for each equipment level controller, and verify the cell control system under multicriterion environments. 3. System description A manufacturing cell studied in this research is shown in Fig. I. In this cell, there are five machines and a robot which serves as the material handling equipment. Each machine has its own input and output buffers with limited capacity. In addition, there is a temporary buffer to prevent system deadlock. When a part arrives at this manufacturing cell, it waits in the system input buffer and requests the robot for transportation. Once this part is chosen, the robot will move to its current location, pick it up, and then move it to its destination, which is the input buffer of a machine. N o interruption will be allowed within this transportation cycle. After sequentially visiting each machine as its process plan, the part will leave this cell and the associated performance attributes will be collected. The robot is responsible for the transportation of parts within this cell. When

OUT

Figure I. A manufacturing cell

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An intelligent conrroller for manufacruring cells Part tvne

1

2

4

6

7

10

II

14

15


Table I. Setup time for part types on machine I.

Rule

Description

FIFO

Operations are selected on first-come-first-serve basis.

EDD

The operation with the earliest process due date will be selected.

SPT

The operation with the shortest processing time will be selected.

STT

The operation with the shortest service time, which includes the processing time and required setup time, will be selected.

SST

The operation with the shortest setup time will be selected

SRT

The operation with the shortest remaining processing time will be selected.

CR

The operation with the smallest CR ratio will be selected. The CR ratio is defined as: Part due date-Current time CR = Remaining processing time The operation with the least SLACK time will be selected. The SLACK time is defined by:

SLACK

SLACK = process due date-processing time-current time Table 2. Dispatching rules for machines to select the next operation. available, the robot will follow a certain dispatching rule to select a job from the candidates, which are the parts in the system input buffer and those waiting in the output buffer of each machine. The robot will not only follow its dispatching rule, but also consider the feasibility of transportation. For example, if the input buffer of machine 1 is full, those parts with machine I as the destination cannot be transported. Moreover, if a machine is blocked, higher priority will be assigned to release the blocking situation. That means the robot will serve the parts in the output buffer of a blocked machine prior to other candidates. Each machine will process the parts waiting in its input buffer based on the current dispatching rule. Once a machine finishes an operation, the completed part will be put in the machine output buffer and the next one will be selected. The operation time to process a job depends on its processing time and the required setup time. In this study, the processing time for each operation is independent from dispatching sequence,


2358 Rule

Description

SDlSTjFIFO


LQL (FIFO)/ N EA RQ DOUTjSRT

RSLACKj NEARQ

The part with shortest transportation time including loading and unloading time will be selected. If tied, the first-come-first-serve basis will be applied. Parts will be selected upon the first-come-first-serve basis. The part in the nearest queue will be selected to break the ties. The earliest arrival part of the longest queue will be selected. If tied in queue length, the part in the nearest queue will be chosen. The part which completes all the required operations will be selected. I f more than one part is completed or none of them is finished, the part with shortest remaining process time will be chosen. The part waiting in the system input buffer will be selected. If no newcomer waits, the LQL rule will be applied. The part with least RSLACK time will be chosen while the NEARQ rule will be applied to break the ties. The RSLACK is defined by: Due date-Remaining process time-current time RSLACK = Number of remaining operations

Table 3. Dispatching rules for the robot to select the next job while the setup time depends on the part type of the preceding operation. An example of the setup time table is illustrated in Table I. In this study, there are fifteen part types in this system and four performance measures are of interest, which are cycle time, average waiting time, average tardiness, and tardy job ratio. These measurements will be taken whenever a part is ready to lcave this system. It is known that the control strategies will heavily influence system performance. Specificillly, the dispatching rules for machines and those for the robot are investigated in this paper. The candidate dispatching rules associated with a machine selecting an operation from its input buffer are listed in Table 2. Table 3 lists the rules for the robot to select the next job when it becomes idle. A simulation model to evaluate the system performance is developed by using the SLAM 11 simulation language. The entire simulation period for each run is 50000 time units. Every simulation run is initialized by applying the F I F O rule for machines and the robot to select jobs during the first 1000 time units. Each performance level during the simulation period is recorded to evaluate the system performance under different control strategies.

4. Approaches 4.1. Arcliiteclure of conrroller An intelligent control scheme (Fig. 2), which consists of an adjustment module and related equipment level controllers, is proposed to select a proper dispatching rule at each decision point. The decision point, in this paper, is defined as the time when a machine or the robot needs to select a job from the candidates. In the proposed framework, a request signalq~illbe sent to the adjustment module a t every decision point by the equipment level controller, which could be a machine controller o r the robot controller. After receiving this request, the adjustment module will takc the current performance levels and the desired levels into account, and then determine the relative importance of each performance measure for this equipment level controller. Based on these relative importance values and its current status, the

An inrelligent controller for manufacruring cells objectives/ desired

q u e s t for mevaluation


measure

Figure 2. Framework of the intelligent controller. equipment level controller will choose a proper dispatching rule for its associated equipment to process the next job. Several advantages can be obtained from this control scheme. First, since the adjustment module considers both desired and current performance levels a t each decision point, the relative importance will indicate how critical each performance measure is a t the current time. Therefore, the performance requirements are transformed into the appropriate critical values and then passed to each equipment level controller. Second, the changes of the desired performance levels can be reflected immediately to the decisions because they are taken into account a t every decision point. With this characteristic, the controller is able to quickly respond to the changes in objectives. Third, since the equipment level controllers select dispatching rules based on their own status and the relative importance values, the decision will include information from the local equipment status and the global performance requirements. 4.2. Developtnent of cot~troller Under the proposed framework, the adjustment module will provide the equipment level controller with the relative importance of each performance measure a t every decision point. The relative importance value can be determined by the ratio of the current performance level to the desired value. In this way, a larger value will indicate that its associated performance measure is more critical in the current situation. That is because smaller values are preferred for those performance measures investigated in this study. In addition, the effective range of these importance values is set between 0 and I, in that 0 means no control effort will be needed while I represents the greatest effort must be made to control the performance level considered. For those with values higher than this range, the maximum value, I, will be assigned to be its importance value. That means this performance measure should be of greatest concern by the equipment level controller at the current decision point. After receiving the relative importance of performance measures from the adjustment module, the equipment level controller will determine the proper dispatching rule based on these importance values and the current equipment status. The multi-layer perceptron is proposed to serve as the equipment level controller because it can be utilized as the inverse function of the simulation model. The neural network


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will take the attributes describing the current status and the relative importance of performance measures as its input, and then provide the proper score for each dispatching rule in its output. From the output of the network, the rule with the highest score will be chosen to dispatch the next job a t this decision point. T o obtain a well-trained network for each equipment controller, generating the training data set is an essential procedure. The training data are collected by simulating different scenarios with the dispatching rules. Each scenario, which represents the system status regarding a single piece of equipment and its input buffers, is created from an intermediate state during the system simulation. The single machine simulation, initialized by each scenario, is then conducted based on different dispatching rules. By examining the impacts of different dispatching rules on the system performance, the training samples are generated. Each training sample is composed of three portions: system configuration, performance measures, and dispatching rules. The system configuration is described by the system attributes which could be the statistics o r measures of those waiting jobs. In this study, the attributes include current queue length, mean and standard deviation of processing time, mean and standard deviation of slack time, mean and standard deviation of completion percentage, as well as the optimistic and pessimistic setup time. The optimistic setup time is obtained by myopically searching the shortest setup time between part types or, for the robot controller, the shortest travel time between machines. O n the other hand, the longest time is estimated in the same way to serve as the pessimistic setup time. The completion percentage of a part is defined as the ratio of its completed operation time to the total required processing time. This information will indicate how much work still remains for this part after the current waiting operation. The performance measures in each training sample are collected from the results or single machine simulations, and they are represented by their relative importance in the proposed control framework. In this study, the performance measures taken in single machine simulations are modified by incorporating the idea of completion percentage and the process due date to reflect their impacts on system performance. The cycle time is replaced by the average time to generate one unit of completion percentage rather than to produce a unit output. The average waiting time measures the average time a job spends in waiting for service. The average tardiness is changed to measure the delay in each process instead of the entire delay for a finished part. The deadline of each process, referred to as the process due date, is determined by the proportional due date to each operation based on its processing time. The number of tardy jobs is determined by the portion of slack time used up in the current process, rather than by the due date of a finished part. Therefore, a job is not counted as tardy only if it is completed before its process due date. Otherwise, the 'number of tardy jobs' is taken from the ratio of the tardiness from its process due date to the remaining slack time for this part if it is completed on time. The dispatching rules, which serve as the output portion of a training sample, are assigned by their degree of achievement under the system status and performance requirements that are specified in the training sample. Since the neural network can only deal with numerical data, the dispatching rules are represented by their associated matching score. The matching score, which is to evaluate the dispatching rule by its achievement of all the measures concerned, was introduced by Yih and Jones (1992). The higher the matching score is, the closer to fulfilling all the criteria sin~ultaneouslyis the dispatching rule. Based on this idea, the matching score is


modified as follows:


where MSjk:Matching score for rule jcompared with rule k when the performance by applying rule k is considered, n: total number of performance measures, Rij: the ratio of ith performance of rule j . In this formula, the matching score is to measure the distance between the outcomes from applying rules j and k when the performance resulting from rule k is considered. If applying rule j leads to better performance in all measures, i.e., Rij 5 Rik for every i, the matching score will output the maximum value, which means the required performance can also be achieved by applying rule j. Otherwise, it will output the normalized distance accordingly. 5. Experiments and results 5.1. Neural network training The backpropagation learning algorithm is utilized to train the neural networks in this study. As mentioned above, the training samples for each equipment level controller are collected from the results of single machine simulations. T o prevent bias from training samples, redundant samples are eliminated. Table 4 lists the number of training samples for each network. Since the purpose of training in this study is to obtain a network with good generalization ability to serve as an equipment level controller, a fitness function is proposed to evaluate networks during training and testing phases. The fitness function is to measure the difference between the highest matching score and the value of the highest output unit in each training sample. The reason for using the fitness function instead of the mean square error of all the output units is that only the one with the highest output value is of interest. The formula of the fitness function is as follows:

where FF: Fitness function value, N: number of patterns, D: highest matching score in each pattern, AH,: the highest output value in training pattern i. Overfitting is another issue concerned in training networks. The overfitting problem arises when the network memorizes the training data rather than generating the appropriate weight matrices to predict data patterns. T o prevent this overfitting problem, the cross validation method (Hansen and Salamon 1990, Levin et al. 1990) is applied to assess the generalization ability of the network based on the training data set and a testing data set during the training period. In this study, several network topologies, as listed in Table 5, have been examined to select a well-trained network for each equipment level controller. From our pilot Equipment level controller Number of samples

Machine

Machine 2

Machine

Machine

Machine

I

3

4

5

Robot

968

872

1 1 12

92 1

1028

73 1

Table 4. Number of training samples for each network (equipment level controller).

IN-25-OUT

IN-30-OUT

IN-35-OUT

IN-40-OUT

Table 5. Network topologies tested in training phase.

**: The number indicates how many nodes on each hidden layer.

*: IN represents for the input layer and OUT for the output layer.

IN-20-10-OUT IN-25-20-OUT IN-30-25-OUT

IN-20-15-OUT IN-25-25-OUT IN-30-30-OUT

3.

5

7

IN- 15-15-OUT IN-25-15-OUT IN-30-20-OUT

IN-10-10-OUT IN-20-20-OUT IN-30- 10-OUT

IN-15-10-OUT IN-25- LO-OUT IN-30- 15-OUT

3 n

Two hidden layer networks**

0,

F'

IN-20-OUT

IN- 10-OUT

IN-l 5-OUT

Y

One hidden layer networks**

IN-OUT*

No hidden layer networks


An intelligent controller for mnnufac~uringcells

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Fitness function value (FF)

Controller

Structure of the selected network

RMSE of network

test by the test by the training crossvalidation samples samples

Machine 1 Machine 2 Machine 3 Machine 4 Machine 5 Robot

IN-25-10-OUT IN-10- 10-OUT IN-30-OUT IN-30-30-OUT IN-10-10-OUT IN-30-OUT

0.109304 0.1 14878 0.097577 0.107126 0.097320 0.104988

0.029885 0.041355 0.033330 0.037235 0.037765 0.049224

0.035470 0.050487 0.056724 0.050795 0,029560 0.055342

test by novel testing samples 0.03741 1 0.0457 16 0.03667 1 0.041637 0.030688 0.058025


Table 6. Summary of the selected networks for equipment level controllers. studies, the other parameters for training networks are determined, in which the learning rate is set a t 0.2 and the momentum at 0.15. After testing the training results from different topologies by novel testing samples, the one with the smallest fitness function value will be chosen. Table 6 summarizes the selected networks for the equipment level controllers. 5.2. Analysis criterion and simulation results Since the system performance during the simulation period is of interest to the controller, the performance deviation (PD) is used as the criterion for evaluation. Performance deviation is defined by the percentage of the average difference in each performance measure from the best dispatching rule, with regard to the range between the best and the worst ones in this study. A larger P D value indicates worse performance in the associated measurement. Also, the best single rule in each performance measure will result in a PD value of O%, while the worst one will have a P D value of 100%. T o obtain the evaluation basis, the system performance under all the combinations of dispatching rules applied on machines and the robot was collected. The performance deviation will be examined from 1000 to 50 000 simulation time units for each performance measure except cycle time. For the cycle time, the performance deviation will only evaluate in the first 20000 time units because it will convert to a constant after a long time. Table 7 lists the results in terms of P D value for each performance measure under all the rule combinations. T o be easily referred to, the rule number will consist of two digits denoting the rule applied to machines and the one to the robot, respectively. According to these results, rule 41, which applies dispatching rule STT on machines and SDIST on the robot, outperforms in both criteria of cycle time and average waiting time. Rule 23, with E D D for machines and LQL for the robot, obtains the best result in average tardiness. In the tardy job ratio, rule 46 performs the best, which combines dispatching rule STT for machines and SLACK for the robot. Besides, it is observed that there is no single rule which can dominate in all performance measures. For example, rule 41, which is the best one under this study in cycle time and average waiting time, has a P D value of 21.39% in average tardiness. On the other hand, rule 23, which is the best one in average tardiness, results in P D values of 45.98% and 42.54% in cycle time and average waiting time, respectively.

Y.-L. Sun attd Y. Yih

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Performance measure I: Cycle time Dispatching rules for machines Rules for no. the robot 1 2 3 4 5 6

SDIST FIFO LQL DOUT DIN RSLACK

1

FlFO 67.55% 83.18% 73.81% 77.18% 73.34% 88.33%

2 EDD

3 SPT

4 STT

5 SST

47.85% 4.06% 0.00% 61.79% 61.72% 23.81% 21.1 1% 77.32% 45.98% 5.88% 5.58% 60.89% 54.09% 1.07% 1.91% 59.92% 50.31% 14.79% 16.96% 61.82% 63.14% 22.05% 18.35% 69.53%

6 SRT

7 CR

8 SLACK

82.00% 92.39% 57.10% 96.13% 98.23% 100.00%

61.87% 79.28% 68.87% 71.68% 75.76% 74.46%

49.40% 69.32% 54.42% 62.67% 54.32% 65.05%


Performance measure 2: Average waiting time Dispatching rules for machines Rules for no. the robot

I 2 3 4 5 6

SDlST FIFO LQL DOUT DIN RLSACK

I FlFO 62.20% 74.55% 66.16% 73.58% 64.65% 76.15%

2 EDD

3 SPT

4 STT

44.17% 0.57% 0.00% 54.65% 12.94% 11.33% 42.54% 0.29% 1.20% 52.90% 5.75% 4.89% 45.72% 6.92% 7.86% 57.71% 15.54% 13.26%

5 SST

6 SRT

7 CR

8 SLACK

61.17% 69.85% 61.16% 67.03% 62.33% 67.54%

86.92% 93.24% 66.59% 100.00% 97.14% 99.82%

61.14% 72.29% 62.27% 72.68% 67.72% 73.25%

46.77% 59.51% 49.27% 56.67% 49.86% 59.85%

7 CR

8 SLACK

Performance measure 3: Averaee tardiness Dispatching rules for machines Rules for no. the robot

I 2 3 4 5 6

SDlST FlFO LQL DOUT DIN RLSACK

I FlFO

2 EDD

3 SPT

4 STT

5 SST

6 SRT

35.74% 41.38% 38.08% 43.44% 36.82% 33.18%

0.28% 6.29% 0.00% 6.30% 1.30% 3.79%

22.24% 24.00% 21.08% 21.76% 24.42% 13.55%

21.39% 22.64% 20.98% 22.21% 24.72% 13.58%

42.98% 44.35% 41.00% 45.47% 44.45% 34.07%

88.84% 88.34% 72.08% 100.00% 96.79% 83.14%

0.09% 6.80% 2.83% 1.53% 8.10% 244%

8.52% 4.94% 2.08% 6.83% 3.06% 3.85%

7 CR

8 SLACK

Performance measure 4: Tardy lob ratio Dispatching rules for machines Rules for no. the robot

I 2 3 4 5 6

SDlST FlFO LQL DOUT DIN RLSACK

I FlFO 71.08% 77.85% 72.37% 77.80% 69.71% 75.74%

2 EDD

3 SPT

59.03% 1.10% 69.01% 12.99% 56.96% 1.24% 71.71% 7.58% 58.54% 4 4 4 % 60.76% 5.70%

4 STT

5 SST

1.62% 8.27% 244% 6.16% 6.49% 0.00%

57.27% 64.50% 59.63% 67.34% 60.65% 59.03%

6 SRT

36.03% 76.32% 56.62% 46.07% 98.24% 74.47% 31.91% 74.11% 61.50% 4161% 100.00% 74.95% 39.89% 91.22% 61.65% 37.21% 83.40% 64.56%

Table 7. Simulation results for dispatching rules in terms of PD values.



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5.3. Refinement of the adjustment module Since neural networks classify the unknown pattern based on the information from the training data, the network cannot provide a good result if there is a discrepancy between the user specification and what the networks were trained by. This problem will arise when neural networks are used as decision making mechanisms and directly take the user preference as inputs without further consideration. In this study, when the user has no preference in a performance measure, he or she may specify an extreme value for this criteria so that it will not become a constraint a t every decision point. Specifically, it is to set a large value as the desired performance level because a small value of a measure indicates better performance and thus a tighter constraint. This large value will then be transformed be a small relative importance value in the adjustment module, and the equipment level controller will take such a small importance value as a user preference for decision-making. Unfortunately, in this situation, the equipment level controller may not provide a good decision because a small value in training data indicates bad performance instead of no preference in the measurement. T o illustrate this problem, a n example is given as follows. Suppose we have two training samples: one indicates that the best performance is found in measures 1, 2, and 4 by applying rule 1; the other one points out that applying rule 2 will lead to the best performance in measure 3 but the worst in measures 1, 2, and 4. Thus, in the training data set, the first training sample will use [I, I, 0, I] as its performance measure in the input pattern and [ I , 0] as the output pattern, while the second one uses [O, 0, 1, 01 and [O, 11 accordingly. If the user prefers to the best result in performance measure 1, he or she will specify a small value for this criterion and large values for others, e.g., [0, w, co,w]. Although rule 1 is a good choice in this case, the controller may select rule 2 since the adjustment module provides the network with the performance requirements [I, 0, 0, 0] which share the same degree of similarity with both training samples. This problem comes from that in the training data, 0 means 'the worst' instead of 'don't care'. Thus, the performance requirements [I, 0, 0, 01 indicate 'the best performance in the first criterion and the worst in others', rather than 'the best in the first criterion while ignoring others'. The refinement procedure is proposed to remedy this problem by providing each user-specified element a 'modifier' based o n the relationship examined from the training samples. In this study, the linear regression model is employed to predict the 'modifier'. At each decision point, the relative importance of each performance measure will be compared with its 'modifier' and refined by the larger one before it is passed to the equipment level controller. For instance, if the 'modifier' are 0.8, 0.7, 0.2, and 0.5, respectively, in the previous example, the network will select the rule by considering the performance requirements as [I, 0.7, 0.2, 0.51 rather than [I, 0, 0, 01. Several experiments were conducted to verify this idea. Experiment Exl 1 was the extreme case with desired values [0, w, w, w] for performance measures, which means the controller should be concerned with the first criterion, cycle time, and ignore others. Similarly, experiments Ex12, Ex13, and Ex14 only focused on the second, third, and fourth criteria, respectively. The results of the performance concerned in terms of the PD values are summarized in Table 8. From these results, it is observed that with the refinement procedure, the controller performs better. In experiment Ex1 I , which focused on the cycle time, the controller with the refinement procedure resulted in a PD value of 27.97%. Compared with the 77% PD value from the controller without refinement, the

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Y.-L. Sup2 and Y . Yih

Experiment (performance measure concerned) Ex I I (cycle time) Ex12 (average waiting time) Ex 13 (average tardiness) Ex 14 ltardv iob ratio)

Controller without refinement procedure

Controller wirh refinement procedure

77.1 8% -2.22% 1.69% 50.02%

27.97% -2.67% 1.00% 34.2 1 %


Table 8. Simulation results in terms of PD values for the controller with/without the refinement procedure in the performance measure concerned. refinement procedure does help the controller to improve its performance. In experiment Ex 12, which was concerned with the average waiting time, the controller without refinement already outperformed the dispatching rules. However, the performance becomes even better after the refinement procedure is applied (PD value drops rrom -2.22% to -2.67%). It indicates that the refinement procedure can remain or even improve the performance if the one without refinement already works well. The results from experiments Ex13 and Ex14 also follow these observations. From experiment Ex13, the performance in average tardiness was improved from a PD value of 1.69% to 1.00%. The tardy job ratio in Ex14 was reduced from 50.02% to 34.21% by including the refinement procedure. 5.4. Multiple criterion evulua~ion As mentioned above, one of the objectives in constructing the controller is that the controller itself can adapt to a multiple criterion environment. This task can be achieved by combining the functions in the adjustment module and the trained neural networks. At each decision point, the adjustment module will inform the equipment level controller how critical each measurement is. By these messages and the current machine status, the trained neural network will determine the matching score for each rule and the one with the highest score will be selected to dispatch the next job. Seven extreme cases listed in Table 9 were conducted to examine the performance of the controller under multiple criterion environments. As in the previous experiments, the '0' indicates the most important criterion while 'm' represents the criterion on which users have no preference. Table 10 summarizes the performance, with regard to PD values, in the criteria concerned. From the results, it was observed that the controller performed the best or near the best when the performance requirements were the combination of any two criteria bong cycle time, average waiting time, and tardy job ratio. For example, in experiment Ex21, the controller outperformed dispatching rules in both criteria concerned, cycle time and average waiting time (PD values of -1.52% and -0.04%, respectively). When emphasizing the average waiting time and tardy job ratio as in experiment Ex25, the controller had better results in both criteria (PD values of -4.37% and - 1.45%) than the best dispatching rule did. In experiment Ex23, the controller outperformed in cycle time though it had a minor loss to the best rule in tardy job ratio (PD value of 5.83%). From the simulation results, it is observed that there was no single rule which can dominate in all the criteria. Therefore, a trade-off should be made by the controller when both required performances cannot be achieved simultaneously. Experiments Ex22, Ex24, and Ex26 illustrated these situations. In experiment Ex24, the controller outperformed the dispatching rules in average waiting time and resulted in a P D value


A n intelligent controller for manufacturing cells


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Performance measures Exoeriment

Cvcle time

Average waiting time

Average tardiness

Tardy jobs ratio


Best dispatching rule for each performance measure in this study Rule 41 Rule 23 Rule 46

0% 45.98% 18.35%

0% 42.54% 13.26%

21.39% 0%

13.58%

1.62% 56.96% 0%

'don't care' performance measure in the experiment Table 10. Simulation results for experiments Ex21 to Ex27 in terms of PD values.

-:

of 17.3% in average tardiness. However, it is interesting to note that the controller performed better in both criteria than rule 41, which was the best one in average waiting time but had a P D value of 21.39% in average tardiness. Furthermore, by examining the simulation results of those investigated dispatching rules, there is no single rule which can perform better in both criteria than the controller does in experiments Ex22 and Ex26. For experiment Ex27, which required good performance in all criteria, the controller outperformed the dispatching rules in three of them while being compromised in average tardiness. When compared to rule 41, the best rule in both cycle time and average tardiness, it is also interesting to note that the controller performed better in all criteria in terms of the P D values. Again, it is observed that no single dispatching rule investigated in this study can obtain better results in all criteria than the controller does. These observations indicate that the controller is able to adapt to multiple criterion objectives. I t is also noteworthy that in these experiments, each decision can be made in a considerably short time. In this study, the system was simulated in a Could NPI machine and each decision requires less than 0.05 seconds of CPU time. This property makes us believe that the proposed controller is able to determine an appropriate control strategy in a real-time basis. 5.5. Adaprability evoluarion In addition to meeting the multiple criteria objectives, the controller should be able to quickly respond to changes in objectives. The adaptability analysis is to investigate the reaction of the controller when the objective changes during the simulation period. In this study, only the changes between the objectives with conflicting criteria are of interest, because the controller needs to consider the trade-off seriously at this time and then make decisions regarding the current objectives. Two experiments were conducted in evaluating the adaptability of the controller. The first one, related to the extreme cases, was to examine the changes between

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An inreNigent controller for nianufacturing cells Average Waiting Time

Obj 1 ==> Obj2 Obj 1 [-.O.-.-I


--

Obj2 [-.-,O.-1

Simulation Time Average Tardiness Obj I=>

Obj2

Obj 1 I-.O.-.-I

0

5000

10000

15000 20000 25000

30000 35000 40000 45000 50000

Simulation Time Figure 3. Simulation results for adaptability evaluation (case 1). average waiting time and average tardiness, because these two criteria were conflicting based on the results of earlier experiments. In this experiment, the controller was given the performance requirements of [m, 0, co,co] from the beginning, and the criteria changed to [co,co, 0, co] at simulation time 26000. The performance in average waiting time and average tardiness is plotted in Fig. 3. The result demonstrates that the controller is able to adapt itself to meeting the changing objectives. The other one used specific values to indicate the objectives from users. In this experiment, the critical requirement was the average tardiness at the beginning, then changed to the average waiting time, and finally both of them. The following details

Y.-L. Sun and Y. Yih Average Waiting Time

Controller**' Situation 2**


Situation I *

0

5000

I0000

15000 20000 25000 30000 35000 40000 45000 50000

Simulation Time

Average Tardiness

Controller*" - Situation 2**

Situation I

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10000 15000 20000 25000 30000 35000 40000 45000

Simulation T i m e

Figure 4. Simulation results for adaptability evaluation (case 2). *: Performance requirement is [14, 160, 20, 0.51. **: Changes to [14, 120, 60, 0.51 at simulation time 15000 units. ***: Changes again to [14, 120, 20, 0.51 at simulation time 35 000 units.

50000

An itrtelligetit controller for manufacfuritrg cells

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the requirements for the controller during the simulation period: In the beginning, the performance requirement was [14, 160, 20, 0.51 for cycle time, average waiting time, average tardiness, and tardy jobs ratio, respectively. At simulation time 15000, the objective vector changed to [14, 120, 60, 0.51. At simulation time 35000, the objective vector became (14, 120, 20, 0.51.


The simulation results are illustrated in Fig. 4 for the average waiting time and average tardiness. The controller focused on the performance in average tardiness at the first stage and then shifted to reduce the average waiting time a t the second stage. At the third stage, since the average tardiness was worse than the requirement, the controller chose to improve the average tardiness level while compromising some performance in average waiting time. This result also suggests that the controller is able to respond to the objective changes in a very short time.

6. Conclusions and future work A neural network based controller, consisting of an adjustment module and the equipment level controllers, was proposed for scheduling and controlling a manufacturing cell. The adjustment module considers the user objectives and the current performance levels to determine the relative importance of performance measures. Based on these importance values and current machine status, the equipment level controller, implemented by a neural network, will select a proper dispatching rule, and the jobs will be processed accordingly. T o enhance the generalization ability, a cross validation method was utilized in selecting the network during a training period. The problem, arising from the discrepancy of the user specification and what the neural networks were trained by, was addressed. A refinement procedure was proposed to reduce the errors resulting from this problem and several experiments were conducted to verify this procedure. The simulation results showed that the refinement procedure is able to improve the performance of the controller and make it closer to the requirements. A simulation model was constructed to evaluate the performance of the proposed controller. Specifically, the extreme cases were examined for comparison. The results showed that the controller performs well under multiple criterion environments. Besides, from the results in adaptability evaluations, the controller is able to quickly respond to the changing objectives. Although unpredicted events such as machine breakdown can be implemented by a rule-based system preceding our control system, it has not been included in this study. Also, in the real world there may be some alternative process plans available for the controller to choose. Therefore in future work flexible routeing and unpredictable events will be incorporated as features of a controller. In addition, the training samples for each equipment level controller are modified to reflect the impacts of different dispatching rules on the system performance. In this way, the decisions made by equipment level controllers can be more accurate. However, the training samples will depend on the investigated part types and performance measures. Once the system introduces a new part type o r another performance measure is of concern to managers, the related networks should be retained. Although training neural networks is a n off-line task, it is time-consuming to find the proper training parameters and network topologies. Therefore, how to eliminate the dependence in part types and performance measures, and how to reduce the training effort, will be further investigated in the future work.

Y.-L. Sun and Y. Yih Acknowledgments T h i s research is in part supported by t h e National Science F o u n d a t i o n Y o u n g Investigator Award, the National Science F o u n d a t i o n Engineering Research Center a t Purdue University, a n d t h e National Institute o f S t a n d a r d s a n d Technology.


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