The Influence Degree Determining of Disturbance Factors in Manufacturing Enterprise Scheduling

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1 , pp The Influence Degree Determining of Disturbance Factors in Manufacturing Enterprise Scheduling Lijun Song, Bo Xiang School of Mechanical Engineering, Chongqing University of Technology, Chongqing , China Abstract. In this paper, in order to effectively deal with disturbance factors which often appear in the process of practical production of the MTO Manufacturing Enterprise, according to the specific influence degree and form of disturbance factors, it describes and classifies disturbance factors. According to disturbance factors with fuzzy characteristics, this paper proposes a processing method based on fuzzy neural network. Finally, the concrete example is used to simulate and verify, which illustrates the effectiveness of the proposed method. Keywords: disturbance factors, Manufacturing Enterprise Scheduling, fuzzy neural network 1 Introduction In recently days, many scholars have analysed and identified the disturbance in the manufacturing system, and put forward lots of methods for the determination of the disturbance factors. Shi Guoxin [1] used the theory of probability theory and mathematical statistics to make the degree of disturbance become quantified; According to the influence degree of the disturbance factor to the production system, Shan Hui [2] divided the disturbance factors into two classes of dominant disturbance factor and recessive disturbance factor; Heidergott, Bernd [3] introduced a finite perturbation analysis method(cfpa) which customers were as the guides, and established perturbation analysis model; Abell J A[4] etc depended on simulation and perturbation analysis (CSPA) algorithm to achieve the perturbation analysis technology in an object-oriented class hierarchy production system information. The current studies [5-8], the identification of the disturbance factors, the degree determination and processing method were analyzed, and the corresponding solutions were also put forward. However, there are few studies on the degree of disturbance of the system. In order to reduce the negative influence caused by the disturbance factor of manufacturing enterprises, we focus on the method of determining the influence degree of the disturbance factors on the production system. First of all, we classify the common disturbance factors in the production process of manufacturing enterprises: A type, B type, C type disturbance factor, and then for solving the problem of B - type disturbance factor, the fuzzy neural network algorithm is used to evaluate the ISSN: ASTL Copyright 2016 SERSC

2 performance of the fuzzy neural network, According to the results of the evaluation, we select the appropriate response mode. Finally, the validity of the method is verified by simulation, we simulate the relevant numerical examples to verify the effectiveness of the proposed method. 2 The Judgment of Fuzzy Neural Network Based on Fuzzy Neural Network According to the disturbance degree of the disturbance factor, the common disturbance in the manufacturing workshop is divided into A type, B type and C type disturbance factor. Among them, the A type disturbance factor needs to carry on the overall revision to the original plan. B type disturbance factor, which has the characteristic of fuzziness, needs to be analyzed according to the actual situation. As for C type of disturbance factor, we need to use the periodic type weight scheduling strategy to eliminate its impact on the production system. Because there are so many multiple fuzzy parameters in the B type disturbance to influence the disturbance degree of production system, at the same time, these fuzzy parameters and the output value of the degree of disturbance have complex nonlinear relations, it is difficult to establish a suitable function expression. In this paper, we propose a fuzzy neural network model (Figure 1) to evaluate the disturbance degree of the B type disturbance factor to the production system. We use the fuzzy neural network to dispose the B type disturbance factor, including the parameter model and the neural network training two modules. The parameters of the model are to deal with the original data of all kinds of parameters which affect the disturbance degree of production system in B model, obtain corresponding membership degree, and the results as the input of neural network are normalized to the range of [0, 1]; The neural network training module is a nonlinear mapping relationship between the input parameters and the degree of disturbance. Input layer I Is Ic Fuzzy parameter R(Is) R(Ic) hidden layer Summation layer Output layer Pr U Us Uc As R(Us) R(Uc) R(As) De Ig A Am R(Am ) Aa R(Aa) Fig. 1. Fuzzy Neural Network Model Copyright 2016 SERSC 113

3 1) Parameter fuzzy quantization We define the disturbance degree of the B type perturbation factor to the production system as δ, and the main parameters of the model are the following 3 types: (1) Intensity of disturbance I: This parameter is fuzzy. Fuzzy subset T(I)={Is,Iw,Im,Ia,Ic}, Is,Iw,Im,Ia,Ic stand for respectively the intensity of the disturbance factor is mild, weak, moderate, strong and serious in this Set. The evaluation basis is affected by the processing steps, which uses the relative process nr to assess. As shown in the formula n r = n a / N. In this formula, N represents the the total processing operations in the scheduling optimization set, n a indicates that the the processing steps affected by the optimization set is affected by the disturbance factors. (2) Emergency degree of disturbance factors U:This parameter is also fuzzy, Fuzzy subset U(I)={Us,Uw,Um,Ua,Uc}. Us,Uw,Um,Ua,Uc stand for respectively the level of the disturbance factor is mild, weak, moderate, strong and serious. The decision is based on the relative priority of the working procedure which is affected by the disturbance factor. As shown in the formula P m =( na i=1p i /n a )/P max. In this formula P m is the proportion of the highest priority in the process of the machining process which is affected by the disturbance factor, n a indicates that the total processing steps affected by the scheduling optimization set is affected by the disturbance factors, and the P i is the priority of the process operation in the scheduling optimization set, P max is the priority of the scheduling optimization set. The Pm is divided into [0,1], Us,Uw,Um,Ua,Uc are taken as the type R(x), and the corresponding membership degree is obtained. (3) Cumulative intensity of disturbance factors A: This parameter has the Aw, Am, Aa the 3 represents the fuzzy subset of the intensity of the disturbance factor, Specific for T (A) ={Aw, Am, Aa}. Among them, Aw, Am, Aa stand for respectively, the degree of the disturbance factor is low, moderate and high, and it describes the processing steps that are not affected by the various disturbance factors. The parameters are evaluated by the relative amount of n ar in the process of accumulation, As shown in the formula n ar = n aa / N. In the formula, n aa indicates that the the total the total processing operations is not affected by the disturbance factors, and the n aa is the total processing operations. N represents the total processing operations for scheduling optimization set.the n ar is divided into [0,1], Aw, Am, Aa are taken as the type R(x), and get the corresponding membership degree. 2) Neural network Because the second part of the quantitative analysis of the disturbance factor is based on the fuzzy neural network is the part of the neural network, we choose the probabilistic neural network to analyze the B type disturbance factor. As shown in the Figure 1, The probabilistic neural network consists of four layers, which are input layer, hidden layer, layer and output layer. We choose 3 main parameters as the input of neural network. The 3 main parameters are Intensity of disturbance Ig, emergency degree of disturbance factors U, intensity of disturbance factors A; Setting 114 Copyright 2016 SERSC

4 the Pr, De, Ig these three kinds of B type disturbance factor to trigger the rescheduling request as the output of the neural network, which Pr said that the immediate implementation of the request, De said that the delay in the rescheduling request, Ig can be ignored. At the same time, the output value is as follows:the acceptance of the rescheduling request is 1, the acceptance of the rescheduling request is 0. Type of B type disturban ce factor 3 Numerical Example Analysis and Simulation In this paper, we use the Matlab2012b version of the proposed fuzzy neural network algorithm to simulate. As follows: at first, we can generate 6 kinds of B type disturbance factors, and then set up the corresponding data of 6 kinds of disturbance factors. After getting data generation, we calculate the membership degree of fuzzy sets as the input of neural network, according to the method of fuzzy parameter. At the same time, for the input data, the experts which in the field of the relevant production scheduling judge their response strategies by the previous experience. That is, Pr, De, and Ig in the 3 kinds of heavy scheduling requests which well select one of the 3 as a neural network output. According to the above method, 150 cases are generated, and 120 cases are randomly selected as training sample, and the data of 30 cases were taken as test sample. Table 1 is a training sample data. Table 1. Partial training sample data Absolute the affected Input data Occurrence intensity Emergency degree Cumulative intensity Relative highest the affected priority average. priority Expected output Pr De Ig C C C C C C C C Comparing the expected output and the actual output in Table 2, we can get satisfactory results in the vast majority of cases when using fuzzy neural network algorithm to solve The influence degree of B type disturbance factor on production system, that is to say, the validity of this method is verified. Copyright 2016 SERSC 115

5 Type of B type distur bance factor Table 2. Partial test sample data Absolute the affected Input data Occurrence intensity Emergency degree Cumulative intensity Relative highest the priority average affected processe priority s P r Actual output D e I g Expecte d output C C C C C C C C C C C C P r D e I g 4 Conclusions In this paper, we divide the influence of the disturbance factors of manufacturing enterprises in the MTO mode into the A type, B type and C type. As for B type perturbation factor with Fuzzy feature, we present a method based on fuzzy neural network to solve the problem. Finally, through calculating relevant Numerical simulation, it erifies that the method is feasible and effective. Acknowledgements. This research is funded by The Fundamental and Advanced Research Projects of Chongqing city in China (CSTC2013jcyjA0564). References 1. Guoxin, S.: Research and quantitative evaluation of uncertainty factors in production scheduling, China: Wuhan University of Technology (2007) 2. Hui, S.: Research based on, Job Shop dynamic random rescheduling method for real-time working condition of Hefei, China: Hefei University of Technology (2008) 3. Heidergott, B.:Max-Plus Linear Stochastic Systems and Perturbation Analysis, Springer US (2007) 4. Abell JA., Judd, RP.: Perturbation analysis of object-oriented discrete event systems, American Control Conference, (1993) 116 Copyright 2016 SERSC

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