Application of Fuzzy Cluster Analysis in the Location of Building Waste Recycling Center Ning Jiang

Transcription

1 International Conference on Autoation, Mechanical Control and Coputational Engineering (AMCCE 01) Application of Fuzzy Cluster Analysis in the Location of Building Waste Recycling Center Ning Jiang Departent of Business Adinistration, Qingdao Vocational and Technical College of Hotel Manageent, Qingdao,100, China Keywords: building waste, recycling center, fuzzy cluster analysis, location. Abstract. Building waste is a kind of recyclable resources, how to carry on the recovery and treatent of the building waste has becoe an iportant proble in the anageent of the city. Many enterprises have set up the recycling center, but the effect is poor, the reason is that the layout and location of waste recycling center affects the overall efficiency of recycling. In this paper, through the analysis of factors affecting the layout of the building waste recycling center, list soe evaluation inde about address feature, such as econoic benefit, social benefit, environental benefit and anageent level, apply fuzzy cluster analysis to the location decision of recycling center, and finally carry on the cluster analysis to verify the practical eaples by SPSS software. Introduction Building waste refers to waste generated due to an-ade or natural and other reasons in the proect, including the waste residue, waste soiland, clay residue and waste aterial, is a kind of can be recycled resources. Establish the site appropriate recycling center is an iportant part of the recycling network, not only can effectively iprove the recovery efficiency, but can bring higher recovery benefit. The usual solution to the location proble is the center-of-gravity ethod and ied integer prograing odel ethod, but the two either only consider geographical factors without considering other effects, either The aount of calculation increases with increasing proble size [1,]. This paper puts forward the fuzzy cluster analysis ethod used to solve the location proble of recycling center, and an eaple using SPSS software to verify the feasibility and effectiveness of the ethod.. Fuzzy cluster analysis Fuzzy cluster analysis is according to certain requireents and rules to deal with fuzzy probles. According to the property of obect to construct fuzzy atri, on the basis of this, with the degree of ebership to deterine the cluster relationship, that is by the ethod of fuzzy atheatics to deterine the fuzzy quantitative relationship between saples, and then cluster, ake the between class data differences as large as possible, within class data differences as sall as possible.fuzzy cluster analysis steps [3,4]: Step1: (Establishent of the evaluation inde syste) The site selection and layout of building waste recycling center, ainly by the following factors: econoic benefit, social benefit and environental benefit and anageent level [1,,], the specific evaluation inde syste. As shown in Fig. 0 The authors - Published by Atlantis Press 03

2 Fig. 1: The classification syste of evaluation inde of building waste recycling center Step: (Saple data standardization) Assuing = {1,,...,n} as classified obects, each obect is coposed of M indees represents the characteristics: i= {i1, i,... i}, n1 n... n i=1,,... n. So, get the original data atri. Because the diension and quantity of M indees are different, so can not directly use the original data to calculate, the raw data ust be standardized. Methods the saple data standardization processing coonly used are: Translation-standard deviation transfor and shift-range transforation. Translation - standard deviation transfor odel is: = ( i = 1,,... n; = 1,,... ) s n n 1 1 =, s = ( ) n i = 1 n 1 i = 1,, shift-range in { 1 i n} = a transforation odel is: { 1 i n} in { 1 i n}. Step3: (Establishent of fuzzy siilarity atri) The ethods about establishent of fuzzy siilarity atri, coonly used are: the correlation coefficient ethod and distance ethod. The atheatical odel of correlation coefficient ethod is: i k i k k = 1 r = 1 1 ( i k i ) ( k ) i = ik, = k k = 1 k = 1, k = 1 k = 1. The atheatical r = 1 cd ( i, ) odel for distance ethod is:, c is the appropriate selected paraeter, akes 0 r 1. d( i, ) = i k k 1Haing distance: k = 1 Euclidean distance: d(, ) = ( ) i i k k k = 1 d(, ) = {,1 k } 3Chebyshev distance: i i k k Step4: (Cluster, Cluster diagra drawn) Starting fro fuzzy siilar atri R, solving transitive closure t(r) with square ethod, sequentially selecting λ-atri, then divide the saple. Using the theory ethod, according to the classification requireents, alternative address can be divided into several categories and levels, and obtain the corresponding definite conclusion. Cluster analysis can be ipleented in statistical software SPSS. 04

3 3. Application of fuzzy cluster analysis for site selection process to building waste recycling center One area plans to build three recycling center for disposal building waste. The eisting eight alternative address, i.e. ={1,, 3, 4,,,, }. According to the relevant statistical data of each address, the coprehensive evaluation score by eperts, obtained initial evaluation inde value of each alternative address. As shown in Table Table 1: Initial evaluation inde value of alternative address Address Inde Econoic Social Environ ental Manage ent level Capital inv estent cost (illion) Transport c ost Operation cost (illion) Supply chain ebers support rate Service area range(nu ) Sustainable develope nt capability The ability to reduce The ability to save energ y The ability to reduce ei ssions Inforatio n anagee nt Facilities anagee

4 nt Personnel anagee nt Because there is soe correlation between indicators, soe indicators ay lead to inforation overlap, reducing the actual operation, so screening initial evaluation inde in order to achieve the optial location. Here, using principal coponent analysis ethod to siplify the inde []. As shown in Table. Table : Evaluation inde value after screening Alternative address Econoic Transport cost Social Service area range(nuber) 3 9 Environental The ability to reduce 4 Manageent level Inforation anageent According to the data in Table, establish the data file in SPSS software, considering the various indicators have the diension difference, so standardize nueric variables, here adopts the standard difference transfor, calculate the Z score of each variable. As shown in Table 3. Table 3: Z score of each variable Alternative address Z Transport cost Z Service area range(nuber) Z The ability to reduce Z Inforation anageent With SPSS, get the fuzzy cluster results of the proble and validation. As shown in Table 4, Table, Fig. and Table. Table 4: Proiity atri Squared Euclidean Distance Case

5 This is a dissiilarity atri Table : Cluster Cobined Cluste Sta Cluste r ge r 1 Aggloeration schedule Stage Cluster First Appears Cluste Coeffici Cluste r ents r 1 Net Stage Transport cost Service area range The ability to reduce Table : Fig. : Dendrogra by fuzzy cluster analysis Significance test by one-way anova Su of df Mean F Sig. Squares Square

6 Inforation anageent Cluster Analysis Fro Table and Fig., the alternative address of 1, as a class;,, as a class; 3, 4, as a class. So, the region can choose a place fro the address of 1 and to establish a building waste recycling center, choose a place fro, and to establish, a place fro 3, 4 and to establish a recycling center, in order to realize the recycling of building waste []. Fro Table, the value of sig is less than 0, iply four observation indees in cluster analysis have significant differences, and the indees have played a significant role, and the results of the fuzzy cluster analysis is effective.. Conclusion Recycling of building waste is based on the concept of circular econoy to realize the sustainable developent of regional econoy. Although there are any enterprises have found building waste recycling can reduce the cost of enterprises, realize greens environental protection and enhance iage. But the recycling center location is not scientific causes supply chain ebers support rate is not high, and the enterprise operating costs increase and ineffective. In this paper, through the analysis of four aor factors which influence the location of building waste recycling center, established the corresponding evaluation inde syste, analyzed recycling center location using fuzzy cluster, and used SPSS software to realize cluster and prove effectiveness. The location of building waste recycling center is a cople atheatical prograing proble, it has any influence factors. Because of the availability of data and coputed strength, this paper only analyzed four influencing factors about econoic benefit, social benefit, environental benefit and anageent level, not including all. The future will continue to strengthen theoretical research and investigation cobined with the actual, to ake the analysis ore coprehensive. References [1] Fenggen Liu, Yaorong Cheng: Application of Fuzzy Cluster Analysis in the Site of Multi-level Logistics Distribution Center. Railway transportation and econoy, 9(3), pp.9-1, 00. [] iuyu Li: Construction waste "turning waste into treasure". Shenzhen Coercial Daily, (A01). [3] Qingguo Ma: Manageent Statistics. Being: Science Press, 00. [4] iaolan Zhu: Fuzzy Clustering for Logistics Park Network Layout. Shanghai: Shanghai Jiao Tong University, Master degree theses, 00. [] Fei ia: Evaluation Study on the Site Selection of Reverse Logistics Processing Center under Low Carbon Econoy. Hunan: Central South University, Master degree thesis, 01 [] Yunyan Li, Caiqing Zhang and Lun Wang: Research on City Logistics Center Location Based on Fuzzy Clustering. The Collection of Acadeic Annual Meeting of The Tenth National Enterprise Inforation and Industrial Engineering, 00. 0