A Case-Based Reasoning Approach for Due-Date Assignment in a Wafer Fabrication Factory 1
1
2
Pei-Chann Chang , Jih-Chang Hsieh , and T. Warren Liao 1
Industrial Engineering and Management Department Yuan Ze University, Nei-Li, Taoyuan, Taiwan
[email protected] 2 Industrial and Manufacturing System Engineering Department Louisiana State University Baton Rouge, Louisiana, USA
[email protected] Abstract. This study explores a new application of Case-Based Reasoning (CBR) in the due-date assignment problem of the wafer fabrication factory. Owing to the complexity of the wafer fabrication, the manufacturing processes of the wafer are very complicated and time-consuming. Thus, the due-date assignment of each order presents a challenging problem to the production planning and scheduling people. Since the product of each order is closely related to the products manufactured before, the CBR approach provides a good tool for us to apply it to the due-date assignment problem. The CBR system could potentially replace the human decision in the estimation of the due-date. Therefore, a CBR system is developed in this study using the similarity coefficient of each order with previous orders. The experimental results show that the proposed approach is very effective and comparable with a neural network approach.
1. Introduction The manufacturing processes of the wafer are very complicated and time-consuming. The processing steps of each wafer depend on the layout of workstations, production capacity of the shop floor, types of orders. We assume that the readers are somewhat familiar with the production steps of wafer manufacturing and the details are omitted. Basically, the wafer manufacturing processes can be divided into two sections, i.e., the front-end and the back-end processes. In the front-end, bare wafers are processed and packaged. A flowchart of the basic front-end processes is described in Figure 1. Photolithography
Thermal Process
Implantation
Chemical Vapor Deposition
Etching
Physical Vapor Deposition
Chemical Mechanical Polishing
Metrology
Cleaning
Fig. 1. Basic Front-End Processes
In the front-end processes, they include (1) photolithography, (2) thermal processes, (3) implantation, (4) chemical vapor deposition, (5) etching, (6) physical vapor deposition, (7) chemical mechanical polishing, (8) process diagnostics and control (metrology), and (9) cleaning. The production steps introduced above are just D.W. Aha and I. Watson (Eds.): ICCBR 2001, LNAI 2080, pp. 648-659, 2001. © Springer-Verlag Berlin Heidelberg 2001
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a step-by-step process. Real floor shop manufacturing processes are more complicated with many detailed processing procedures. After the front-end processes, wafers are fed into the back-end processes. A simple flowchart of the back-end processes is also shown in Figure 2. Test
Wafer Dicing
Die Attach
Wire Bondong
Encapsulation
Fig. 2. Basic Back-End Processes
The main production steps in the back-end include (1) test, (2) wafer dicing, (3) die attach, (4) wire bonding, and (5) encapsulation. The production characteristics of wafer factories are different from the traditional job shops in the following characteristics: (1) reentry, (2) rework, (3) lot sizing, (4) common machines, (5) work-in-process (WIP) control, (6) random yield, (7) multifunction machines, and (8) diversities of machine types. Owing to the complicated production steps and attributes, due-date assignment becomes a great challenge to the production planning and scheduling department. From the above description, we know that the due-date of each product in the wafer fabrication factory is greatly affected by the following factors: (1) routing of the product, (2) order quantities, (3) current shop loading, (4) jobs in the queue of the bottleneck machine, and so on. Traditionally, the due-date of each order is assigned by the production planning and control people based on their knowledge of the manufacturing processes. However, they may not be able to take all these factors into consideration. Therefore, the CBR approach provides a very encouraging motivation for the due-date assignment problem in the wafer fabrication factory. Moreover, as we know, backpropagation neural network (BPN) is also very prevailing in forecasting. Finally, experimental results obtained from CBR approach and BPN will be compared and concluded.
2. Literature Review Recently, case-based-reasoning (CBR) has become very popular for a variety of application areas. Successful applications as reported by Watson and Marir [10] include areas such as academic demonstrators; knowledge acquisition; legal reasoning; expansion of anomalies; diagnosis; arbitration; design; planning; repair and adaptation; and tutoring. In due-date assignment, production planning and scheduling people usually estimates the flowtime of each order by the product they produced before. If the specification of the product is exactly the same, then a flowtime can be derived and the due-date of the product is assigned. However, the status of the shop floor such as jobs in system, shop loading and jobs in the bottleneck machine,…, etc., may not be all the same. As a result, the estimation of the due-date could not be accurate and subject to errors. In this study, we will adopt the case-based reasoning approach to solve the problem that originally is provided by human experts in experience-intensive
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application domains. Two innovative CBR researches and applications developed, namely INRECA in Auriol, et al. [1] and Bisio and Malabocchia [2], are the major driving forces to enable us to perform this new application. In the early periods, different rules as listed in Cheng and Gupta [3] have been proposed for due-date assignment, i.e., TWK (total processing time), NOP (number of operations), CON (constant allowance), and RDM (random allowance) rules. As soon as the processing times are estimated by these rules, the due-date is equal to the order release time plus the estimated processing time, i.e., d i = ri + pi (1) where d i is the due-date of the ith order, ri and p i are the release time and processing time of order i respectively. Many other discussions are concentrated on the relationships between the shop status information and due-dates. Several significant effective factors, for example, jobs-in-queue (JIQ), jobs-in-system (JIS), delay-in-queue (DIQ), and processing plus waiting times (PPW) were explored. Conway, et al. [4] revealed that due-date rules incorporating job characteristics performed better than that which ignored job characteristics. Recent successful applications of the CBR system in bankruptcy prediction by Jo et al. [6], document retrieval system by Watson and Watson [11], identifying failure mechanisms by Liao, et al. [7], and engineering sales support by Watson and Gardingen [9] also inspire this research. Finnie and Witting [5] applied the casebased reasoning technique to the software estimation which performs somewhat superior to the regression models based on the same data. A CBR system is proposed to estimate the due-date of a new order by retrieving the features influential to flowtime from the previous order according to the global similarity coefficient in measuring the closeness between these two orders. Therefore, a similar case of the order to be processed can be retrieved and a new duedate of the order can be assigned.
3. Overview of the Simulated Wafer Fabrication Factory Owing to the complexity of the production process, the history data of each order is only partially available in the factory. Information for the previous orders such as the JIQ, JIS, and waiting time, is not that easy to be collected on the shop floor. Therefore, a simulation model is built to simulate the manufacturing process of a real wafer fabrication factory. Then, the case base can be derived from the shop floor status collected from the simulation model. The basic configuration of the simulated wafer factory is the same as a realworld wafer fabrication factory which is located in the Science Park of Hsin-Chu, Taiwan, R.O.C.. There are 66 single-server or multiple-server workstations in the factory. The program of the simulated wafer fabrication model is coded using the Microsoft Visual FoxPro 6.0. The summarized data about the workstations are given in Table 1, and the basic information of each workstation include: (1) workstation number, (2) mean processing time (hour), (3) number of lots per batch, and (4) number of machines in each workstation. The routing of a sample product is shown in Table 2. The gray-shaded cells (i.e., MT39) represent the workstation of furnace. As
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we can observe from the routing, the sample product passes through the furnace 9 times. The singular production characteristic “reentry” of the semiconductor industry is clearly reflected in the example. It also shows the difficulty for the production planning and scheduling people to provide an accurate due-date for the product with such a complicated routing. Table 1. Parameter Values of the Simulated Wafer Fabrication Factory Workstation MPT Lots per # of Workstation MPT Lots per # of No. (Hour) Batch Machines No. (Hour) Batch Machines MT01 MT02 MT03 MT04 MT05 MT06 MT07 MT08 MT09 MT10 MT11 MT12 MT13 MT14 MT15 MT16 MT17 MT18 MT19 MT20 MT21 MT22 MT23 MT24 MT25 MT26 MT27 MT28 MT29 MT30 MT31 MT32 MT33
0.31 0.28 0.28 1.65 0.36 1.7 3.3 0.31 1.1 0.77 1.65 0.26 0.23 2.4 0.45 4.37 0.27 1.3 0.29 5.17 5.88 0.31 0.29 5.31 0.81 1.22 0.25 0.9 0.55 0.63 1.15 1.18 6.6
1 1 1 6 1 6 6 1 1 1 1 1 6 1 1 6 1 1 1 6 6 1 1 6 1 6 1 1 1 1 1 1 6
2 4 9 4 6 3 2 3 4 6 2 3 3 2 3 1 2 1 2 1 5 6 10 1 3 1 6 3 2 2 8 1 1
MT34 MT35 MT36 MT37 MT38 MT39 MT40 MT41 MT42 MT43 MT44 MT45 MT46 MT47 MT48 MT49 MT50 MT51 MT52 MT53 MT54 MT55 MT56 MT57 MT58 MT59 MT60 MT61 MT62 MT63 MT64 MT65 MT66
0.22 5.52 1.4 1.77 1.16 1.15 0.23 0.26 1.68 0.63 0.23 4.88 1.35 1.32 13.3 4.01 0.29 3.15 0.25 0.32 0.38 1.49 0.77 0.33 1.29 0.56 0.93 0.28 0.3 7.87 4.23 1.58 0.35
1 6 1 1 1 1 6 1 1 1 1 6 1 1 6 1 1 6 1 1 1 1 1 1 1 1 1 6 1 6 6 1 1
1 1 4 2 6 5 2 2 6 2 3 3 1 6 3 2 8 2 1 8 2 6 1 2 1 1 2 3 2 4 1 1 2
Besides the parameter values and routing of products, there are some basic assumptions about this model, which include: (1) The machines within a workstation are all identical. (2) The average production capacity is 20000 pieces of wafers per month. (3) The mean time between order’s arrival follows exponential distribution with SDUDPHWHU HTXDOVWRKRXUV (4) All orders are processed and distributed to available machines according to production steps and no preemption is allowed.
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Table 2. Routing of a Sample Product Steps
WS No.
Steps
WS No.
Steps
WS No.
Steps
WS No.
Steps
WS No.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40
MT01 MT07 MT63 MT12 MT62 MT17 MT02 MT02 MT15 MT08 MT27 MT54 MT05 MT66 MT03 MT29 MT50 MT39 MT02 MT30 MT05 MT66 MT03 MT07 MT48 MT08 MT23 MT07 MT35 MT27 MT53 MT07 MT20 MT18 MT08 MT07 MT35 MT08 MT07 MT35
41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80
MT05 MT66 MT03 MT08 MT07 MT24 MT16 MT07 MT16 MT39 MT02 MT53 MT34 MT27 MT43 MT03 MT05 MT08 MT66 MT03 MT27 MT53 MT30 MT61 MT64 MT58 MT61 MT51 MT39 MT02 MT55 MT05 MT66 MT03 MT39 MT02 MT53 MT27 MT43 MT03
81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120
MT03 MT27 MT53 MT61 MT09 MT21 MT66 MT58 MT66 MT61 MT33 MT61 MT64 MT16 MT61 MT64 MT39 MT02 MT53 MT34 MT27 MT46 MT03 MT05 MT66 MT03 MT25 MT61 MT16 MT39 MT02 MT53 MT27 MT43 MT03 MT05 MT66 MT03 MT61 MT38
121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160
MT57 MT13 MT23 MT52 MT23 MT08 MT66 MT03 MT39 MT02 MT53 MT34 MT27 MT31 MT44 MT44 MT03 MT27 MT53 MT28 MT49 MT28 MT57 MT13 MT50 MT23 MT52 MT23 MT28 MT39 MT02 MT53 MT34 MT27 MT15 MT08 MT27 MT65 MT03 MT44
161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189
MT06 MT59 MT39 MT02 MT27 MT36 MT03 MT44 MT05 MT03 MT04 MT41 MT62 MT41 MT02 MT40 MT05 MT39 MT02 MT30 MT05 MT08 MT66 MT14 MT21 MT66 MT05 MT08 MT03
(5) The dispatching rule is first in first out (FIFO). (6) The basic processing unit is “lot” and each lot contains 25 pieces of wafers. Each workstation may process the multiples of lot at a time.
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(7) Pull production system is assumed. (8) The system will not stop until all the orders are processed. (9) The system output includes information such as order no, order type, order production quantity, arrival time, maximum queuing length, average queuing length, maximum waiting time, average waiting time, makespan, flowtime, work-in-process, bottleneck utilization, total works, number of operations, jobs-in-queue, jobs-in-system. These output information is collected for the purpose of flowtime forecasting. The time series plot of 100 simulated flowtime data is shown in Figure 3. As we can observe here, the pattern of the flowtime is not stable and very non-stationary. The traditional approach by human decision is very inaccurate and very prone to failure when the shop status is totally different even for the same product. Variation of Flow Time 4000
Flow Time
3000
2000
1000 Index
10
20
30
40
50
60
70
80
90
100
Fig. 3. Time Series Plot of Flow Time
4. Case-Based Reasoning Due-Date Assignment Model A CBR based due-date assignment model is developed and compared with a backpropagation neural network in this study. These comparisons will be shown in Sec 5.1. 4.1 CBR Model CBR is one of the rising approaches for designing expert systems. There are many advantages of CBR approach, the most important feature of which is that it resembles the way people solve problems in the real world. Typically, there are five steps in CBR: 1. presentation of a new case 2. retrieval of the most similar cases from the case base
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3. adaptation of the selected cases 4. validation of the current solution given by CBR 5. updating of the system by adding the verified solution to the case base The above-mentioned steps constitute the CBR cycle. In this study, CBR is used to forecast the flowtime of each order. Therefore, historical analogous cases are often used to forecast the flowtime of new cases. As discussed earlier in section 2, there are many factors (i.e. order’s attributes) may affect the flow time of each order. Some are influential to the flow time but some are not. Key factors affected the flow time significantly must be first identified with respect to the viewpoint of effectiveness. Backward elimination of regression analysis is a wellknown tool to identify the influential factors. Once the influential factors are identified, the CBR due-date assignment procedure is implemented as follows. 4.1.1 Case Representation The case of each order is represented by its influential attributes. For example, an order with average queuing length 1.043, processing time 331.07, jobs-in-system 58, job-in-queue 7, the shop loading 80% and flowtime 1729.38 can be represented using the row vector [1.043, 331.07, 58, 7, 80%, 1729.38]. Therefore, the case base will be a list of such vectors including all the orders received earlier with their product type and shop status information stored in the database. 4.1.2 Case Retrieval by Similarity Measurement Analogous cases are retrieved to forecast order’s flowtime on the basis of similarity. Hence, the similarity measurement should be defined first. To measure the similarity of the new order with the previous orders, a distance-based approach is applied. The measurement calculates the Euclidian distance between case a = (a1 , a 2 , , an ) and case
b = (b1 , b2 ,
, bn ) as follows:
ab =
n i =1
(ai - bi )2
(2)
That is, the inter-case distances are measured using this formula. As we can observe that the small distances lead to the large similarities. There exists an inverse proportion relationship between the distances and similarities. But we hope the similarities can be reflected in the weights of combining influential attributes to forecast the flowtime. Thus, a simple inverse transformation should be taken to the distances. The exponential decay function is used to transform the distance into similarity. The representation of the exponential decay function is shown as (3)
sab = e - ab , for all ab
(3)
4.1.3 Generating a Forecast of the Flowtime Jo et al. [5] derived the model from the context model of classification proposed by Medin and Schaffer [8]. In the model, the expected target value ( TVt ) of the target case is obtained as follows:
A Case-Based Reasoning Approach for Due-Date Assignment
E (TVt {S tb }t =1,n ) =
=
n b =1
(
n b =1
S tb i =1
P(TVk = TVb {S tb }t =1,n ) TVb
) TVb
n
655
(4)
S ti
Note that n is the number of cases selected to generate the forecasts;
Stb is the
similarity between the new target t and the base case b; and TVb is the flowtime of base b. The similarity ratio (i.e. similarity of each base case with the new target case over the sum of the similarities of all the cases) is used as the case’s weight in the model. Thus, the forecast on the target value of the new case ( TVt ) is represented as a linear combination of the target values of base cases, weighted in proportion to their relative similarities to the new case. The overall flow of the CBR due-date assignment model is depicted in Figure 4. Simulated Wafer Fabrication Factory
Collection of the Shop Floor Status
Identify the Influential Factors
Calculate the Similarities between Flow Time & Influential Factors
Generate the Forcasts of Flowtimes
Fig. 4. CBR Due-Date Assignment Model
4.2 Backpropagation Neural Network BPN is the most commonly used technique to apply neural network to the forecasting problem. The BPN is actually a gradient steepest descent method to minimize the total square error of the output computed by the net. The training of BPN includes three stages: (1) the feedforward of the input training pattern, (2) the calculation of the associated error, and (3) the adjustment of the weights. System parameters in the
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three-layer BPN should be set in the initial step and these values are listed in the following table. Table 3. Architecture and Parameters of the BPN Number of input nodes Number of hidden layers Number of nodes in hidden layer Number of output nodes Learning rule Activation function
According to experimental settings One The number of input nodes One Delta rule Sigmoid function
5. Experimental Results The simulated wafer fabrication factory was executed and 900 data sets were collected from a steady-state simulated factory. Each datum provides information about order’s attributes include number, type, production quantity, arrival time, maximum queuing length, average queuing length, maximum waiting time, average waiting time, makespan, flowtime, processing time, TWK, NOP, JIQ, JIS…etc. The backward elimination of regression analysis is a common used tool to identify influential factors. Input the attribute “flowtime” as the response variable and the remaining attributes are taken as the explanatory variables. The backward elimination will take the least influential variable out and repeat the elimination steps till all the variables are influential to the response variable. After the iterative steps implemented with the aid of the statistical software ”Minitab”, eight variables are identified as the influential ones. They are order quantity, maximum queuing length, average queuing length, total queuing length, average shop workload when the order arrived, WIP, processing time, and JIQ. Since the influential factors are identified, the cases can be represented by these influential variables and the flowtime. Take the first output record as an example, i.e., [7, 12, 1.043, 194, 10.606, 17812, 331.07, 1828.82, 1729.38]. To reduce the variation caused by the unit of measurement, data are pre-processed by normalization. Among the 900 data sets, 600 sets are randomly selected as the training data and the remaining ones are used as the test data to check the generalization capability in the BPN algorithm. The CBR approach uses the same 600 data sets of the training data as the base cases and the 300 test data sets are regarded as the new cases to be forecasted. Finally, the root mean square error (RMSE) is performed to measure the performances of the CBR and BPN approaches. 5.1 Numerical Comparisons of BPN and CBR Three rules, TWK, NOP, and JIQ are used to evaluate the performances of CBR and BPN. In the TWK rule, cases are consisted of the factors “total work” and “flowtime”. In the NOP rule, cases are consisted of the factors “ number of operation” and “flowtime”. And in the JIQ rule cases are consisted of the factors ‘job-in-queue” and “flowtime”. For the CBR approach, randomly selected 600 data sets are taken as the
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base case and top 20 similar cases are retrieved for case adaptation to forecast the flowtime of the remaining 300 data sets. The estimation errors depend on the sample size used for prediction (i.e., the number of retrieved cases). To study the performance of BPN based on the equal sample size, 20 data sets are randomly selected as the training data for BPN. With the aid of the software NeuralWork Professional II, the BPN algorithm is employed to forecast the flowtime. The forecasting accuracy and results are shown in Table 4. The differences (Diff (%)) of RMSE between BPN and CBR were calculated. As we can see in Table 4, the CBR is more accuracy than the BPN. The results also reflect that the CBR method is more efficient than BPN. This is an encouraging outcome to the CBR due-date assignment approach. Small prediction error (RMSE) is usually required in the forecasting problem. Consequently, CBR seems more powerful in forecasting the flowtime than the BPN does. Table 4. Comparison II of BPN and CBR
RMSE TWK NOP JIQ
BPN 611.74 782.23 543.01
CBR 590.49 617.58 466.00
Diff (%) 3.60% 26.66% 16.53%
5.2 Optimal Design of CBR Model The implementation of CBR model follows the steps that are introduced from subsection 4.1.1 to 4.1.3. The evaluation in last section demostrates the effectiveness of CBR. However, the idea coming from the moving average mothod indicates that the number of retrieved cases will affect the smoothness of the forecasts. Usually, large number of retrieved cases leads to smooth forecasting behavior and small number of retrieved comes out the sharply varied forecasts. Therefore, another topic to be addressed is “ how many cases should be retrieved? ”. The experiment is designed as using k similar cases to forecast the flowtimes of new cases. The top k similar cases of the base case consisted of eight identified influential variables and the flowtime. The values of k are 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 20, 30, 40, and 50. The value of k is selected with the smallest RMSE. The exhaustive enumeration is used to examine fourteen specified numbers of retrieved cases and the results are plotted in Figure 5. And the numerical results are shown in Table 5. The best performance is 231.00 with retrieving seven cases to forecast the flowtime. Since the optimal number of retrieved cases is determined, we turn back to the comparisons of BPN and CBR in Subsec. 5.1. BPN algorithm will randomly select 7 data sets for training the network to minimize the total squared error of the output. As soon as the network is well-trained, the test data will be recalled to monitor the training process to reach maximum generalization capability. The RMSE of test data is recorded in Table 6. Top 7 similar cases will be retrieved in the base case to forecast the flowtime of new cases in the CBR due-date approach. For comparison with the BPN algorithm, the RMSE is also recorded in Table 6. It has clearly shown in Table 6 that when the k (number of retrieved cases) is varied, the performance of CBR seems more stable than BPN. On average, the Diff(%) of using different k causes great fluctuation in forecasting by BPN. Therefore, it reveals that CBR is a robust tool in this study.
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P.-C. Chang, J.-C. Hsieh, and T.W. Liao Table 5. The Corresponding RMSE of # of Retrieved Cases No.
# of retrieved cases
RMSE
1 2 3 4 5 6 7 8 9 10 11 12 13 14
1 2 3 4 5 6 7 8 9 10 20 30 40 50
304.28 267.66 255.97 242.08 236.90 231.90 231.00 235.14 234.01 235.69 253.47 266.02 276.45 291.33
310 300 290
RMSE
280 270 260 250 240 230 0
10
20
30
40
50
No. of retrieved cases
Fig. 5. The Relationship of # of retrieved cases and RMSE Table 6. Comparison III of CBR and BPN RMSE k TWK NOP JIQ
7 634.47 837.31 678.77
BPN 20 611.74 782.23 543.01
Diff(%) 3.72% 7.04% 25.00%
7 629.58 641.64 485.53
CBR 20 590.49 617.58 466.00
Diff(%) 6.62% 3.90% 4.19%
6. Conclusions and Future Directions This implementation has shown how a due-date assignment CBR system can be created using similarity measurement and case adaptive method. And the optimal
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number of cases to be retrieved in this study is determined. Traditionally, the due-date is assigned by the production planning and scheduling people. The department only takes the product specification into consideration to assign the due-date by adding the estimated flowtime with the release date. However, the flowtime is very much dependent on the current shop status such as the TWK, NOP, JIQ, and shop loading. That is the reason why most of the due-date assigned cannot be met by production department. Using the CBR approach as proposed in this study, the performance is comparable to that of the BPN algorithm. This is a very encouraging start for this study and we suggest that the flowtime adaptive method can be further improved by a better design of the CBR model or the fuzzy adaptive network. Since the shop loading can be expressed by using light, middle or heavy, and the similarity of the product can be discriminated by the number of critical resources passed instead of the total number of processing steps. In addition, more sophisticated learning algorithm can be applied in the reuse process. Also, we would like to explore more applications of CBR system in different manufacturing areas.
References [1] Auriol, E., Wess, S., Manago, M., Althoff, D-D., and Traphoner, R. Integrating Induction st and Case-Based Reasoning: Methodological Approach and First Evaluations, Proc. 1 International Conference on Case-based Reasoning, Springer Verlag Berlin. 1995. [2] Bisio, R. and Malabocchia, F. Cost Estimation of Software Projects Through Case-Based st Reasoning, Proc. 1 International Conference on Case-based Reasoning, Springer Verlag Berlin. (1995). [3] Cheng, T. C., and Gupta, E. Survey of Scheduling Research Involving Due-Date Determination Decisions. European Journal of Operational Research, 36(11):1017-1026, 1985.. [4] Conway, R.W., Maxwell, W. L., and Miller, L. W. Theory of Scheduling. Massachusetts: Addison-Wesley 1967. [5] Finnie, G. R. and Witting, G. E. Estimating Software Development Effort with Casest Based Reasoning. Proc. 2 International Conference on Case-based Reasoning, Springer Verlag. (1995). [6] Jo, H., Han, I., and Lee, H. Bankruptcy Prediction Using Case-Based Reasoning, Neural Networks and Discriminant Analysis. Expert Systems and Applications, 13: 97-108, 1997. [7] Liao, T. W., Zhang, Z. M., and Mount, C.R. A Case-Based Reasoning System for Identifying Failure Mechanisms, Engineering Applications of Artificial Intelligence 13:199-213, 2000. [8] Medin, D. L. and Schaffer, M. M. Context Theory of Classification Learning. Psychological Review, 85:207-238, 1978. [9] Watson, I. and Gardingen, D., A Distributed Cased-Based Reasoning Application for th Engineering Sales Support, In, Proc. 16 Int. Joint Conf. on Artificial Intelligence (IJCAI99), Vol. 1:600-605, 1999. [10] Watson, and Marir. Case-based reasoning: A Review. Knowledge Engineering Review. 9(4): 327-354, 1994. [11] Watson, I. and Watson, H. CAIRN: A Case-Based Document Retrieval System, In, Proc. rd of the 3 United Kingdom Case-Based Reasoning Wrokshop. University of Manchester, Filer, N & Watson, I (Eds) 1997.
