A prncpa component anayss and entropy vaue cacuate method n SPSS for MDLAP mode ZIPENG ZHANG Schoo of Management scence and Engneerng, Shandong Norma Unversty, Jnan, Chna, HONGGUO WANG Schoo of nformaton scence and Engneerng, Shandong Norma Unversty, Jnan, Chna zhangzpeng20@126.com Abstract:- In the anayss of MDLAP, ths paper creatvey combnes the mathematca optmzaton mode of cost-based mutpe targets dstrbuton ocaton probem nto a ogstcs ocaton seecton decson mode wth a mutpe nfuencng factors, then put forward the method of data standardzaton processng, entropy weght, the method of prncpa component anayss and mathematca expressons to sove ths mode. Fnay usng SPSS statstca anayss software of the decson mode are anayzed weghted near regresson method of nfuencng factors whch based on entropy, smarty anayss system custerng method based on anayss of canddate servces area, anayss effect comprehensve scorng factors of servce area wth factor anayss and prncpa component regresson method, fnay cumnatng n the servce area of the 97 canddate n Shandong Provnce seected 10 servce area of Wefang, Qngdao, Pngdu, Qufu as the optma ogstcs center deveopment area. Keywords:-LAP; SPSS; ocaton seecton mode; prncpa component regresson method 1 Introducton Wth the rapd deveopment of Chnas economy and the ogstcs and dstrbuton busness s more and more ncreasng, the dstrbuton center pays a pvota roe n the ogstcs system. ts man targets s that accordng to dfferent customer requrements whch n the regon to make the goods tmey, accuratey and effectvey devered to the hands of customers, so the ocaton probem of ogstcs dstrbuton center s the core of ogstcs system research, and has practca vaue to sove the above probem. domestc and foregn schoars have conducted a ot of research, put forward many ocaton modes, a arge number of studes show that, the ogstcs dstrbuton center ocaton probem s a mut-objectve optmzaton probem wth compex constrants, beongng to the NP- hard probem, therefore, the schoars proposed tabu search agorthm, genetc agorthm and ant coony agorthm and so on, these agorthms have acheved certan resuts,but these agorthms are heurstc search agorthm, when the scae s arge, the searchng speed of these agorthms s sow and easy to fa nto oca optmum, thus unabe to obtan the goba optma ocaton of ogstcs dstrbuton center ocaton scheme ead to the not dea effect. Logstcs ocaton aocaton probem (LAP) [1] can be traced back to the 1909 ssue of Weber, t frst treat the LAP from a mathematca pont of vew, after neary a century of deveopment, ts theory and appcaton have been greaty enrched, the ogstcs ocaton aocaton probem has produced a network ocaton mode(dlap), a snge perod mode (SLAP), uncapactated mut stage mode(umlap), mut product mode(mplap), dynamc mode (DLAP), probabty mode (PLAP)and mut-objectve ocaton mode (MOLAP) [3-5], Most of the peope research the uncapacty-mt snge stage mode to sove LAP up to the present. At present,the study of expressway servce area to expand the functon of ogstcs many focus on the feasbty anayss and management research. Ceng Zhaogeng [6] (2008) ponts out that the expressway servce area n Chna w deveop to 3 drectons: change from rest functon to the esure functon, part of the servce area w become the ogstcs node, the expressway servce area w become an mportant patform for commerce and ISBN: 978-960-474-383-4 137
trade crcuaton. Zheng Zhpng [7] (2011), Mu Guosheng [8] (2011) anayss of Fujan, Shangha, Nanjng expressway servce area deveopment advantages of modern ogstcs base, ponted out that the hghway deveopment of ogstcs has great potenta and puts forward the premnary pan to ntroducton of LAP n ogstcs ndustry. Lu Yng [9] (2009) made Shandong Provnce as the research object, anayzng the thrd party ogstcs servce area LAP feasbty study based on the deveopment of nterna reatons between the expressway servce area and the deveopment of modern ogstcs ndustry, and put forward the strategc pan and strategy of LAP n Shandong expressway servce area. These terature above dscussed the deveopment drecton of expressway servce area, the feasbty and countermeasures for the deveopment of ogstcs LAP, n genera can be summarzed as "shoud do", but there was no answer about "how to do", reates to the future under the gudance of the theory of ogstcs functon expanson dd not form, especay there are no depth research n the expressway servce area ogstcs functon network under the condton of deveopment of key technooges. In summary, the hghway ogstcs functon has some academc research papers but mosty feasbe research and management to deveop ts ogstcs functon, the terature of LAP n expressway servce area ogstcs node s rarey, ony Ge Xjun [10] (2006) anayss and dscusses comprehensve evauaton method of aternatve nodes by usng the prncpa component n expressway servce LAP probem, and use the quatatve anayss and quanttatve anayss method to dscuss the eve, ocaton and functon of the ogstcs node servce areas. Qn Lu [11] (2007) appes the method above to the ogstcs servce area of expressway node partton to acheve good resuts n the ange of regona ogstcs and the expressway servce area ntegraton. The paper put the reaty Shandong provnce expressway traffc network topoogca structure as a startng pont, and put the LAP probem of ogstcs nodes n a certan regon as the target, creatvey combnes the mathematca optmzaton mode of cost-based mutpe targets dstrbuton ocaton probem nto a ogstcs ocaton seecton decson mode wth a mutpe nfuencng factors, then put forward the method of data standardzaton processng, entropy weght, the method of prncpa component anayss and mathematca expressons to sove ths mode. Fnay gan the perfect resut through the comprehensve evauaton for each factor usng SPSS statstca anayss. 2 Descrpton of the LAP Probem 2.1 Defnton of the LAP probem In the basc LAP probem, there s a hghway network n the range of a certan area (Shandong provnce), we can know that the poston of the canddate ogstcs center (M) and ogstcs demand (N) has been fxed, n order to provde fnshed products for the ogstcs demand wth ow cost, storage, transfer, processng, management and other servces, the system requrement seects one or a puraty of ogstcs center from the canddate ogstcs nodes. For exampe, there are 97 expressway servce area of expressway n Shandong provnce wthn the scope of ther sze, ocaton, condton, ocaton and other characterstcs are known for each servce area, LAP probem demand to determne the number and ocaton of ogstcs center n the servce area accordng to the specfc decson method. 2.2 Mathematca mode of the LAP probem The LAP probem of ogstcs nodes n the servce area of Shandong provnce ogstcs network s many seect mutpe servce representatve as the ogstcs node from Shandong provnce expressway servce area wthn the 97 aternatves whch based on the tota demand for ogstcs and transport costs, so as to acheve the owest tota cost of ogstcs and transport n ths area. As everyone knows, the goods of transport dversty have varous knds, dfferent types of goods have dfferent dstrbuton costs due to ts weght, voume, tmeness and portabe degree caused by transportaton, and n the prevous research papers, the most modes and methods whch based on the LRP questons put the tota transportaton cost and voume as the target, and the types of transport goods to the mpact of the dstrbuton cost s no reated. Based on ths consderaton, ths paper put up wth a LAP optmzaton mode whch s cose to the reaty of the cassfcaton n the poston of that the canddate ogstcs center and ogstcs demand nodes s known, wthout consderng the transportaton storage fee, management fee and transport cost and freght traffc s proportona to the dstance. The mode s as foows: ISBN: 978-960-474-383-4 138
MnC = [ C X D Q + C X D Q ] j jk j j j jk j j G Lk N G j G j H (F S W Q ) j jc j jc j (1) j Gj + + Symbo Tabe1 Symbo and defnton n the agorthm Defnton G = { / = 1, 2 m} sets of cty of vehce startng nodes G = { / = 1, 2 m} sets of cty of vehce fnshng nodes G = { j / j = m+ 1, m+ 2 m+ p} expressway servce areas j N = {k/ k = 1,2 } the set of vehces L= { / = 1,2,3,4 } Dfferent types of goods F jc W jc j budng cost of the ogstcs center node Operatng costs of the ogstcs center node C transportaton cost of goods from startng j node to fnshng node j D transportaton dstance of goods from j startng node to ogstcs node j Q transportaton traffc of goods from startng node to ogstcs node j C transportaton manage cost of goods from j j ogstc node j to fnshng node D transportaton dstance of goods from ogstc node j to fnshng node Q transportaton traffc of goods from ogstc j S j Q j node j to fnshng node the scae of expressway servce area the maxmum amount of transt goods of ogstcs node j Ths s the constrants condton about the mathematc mode of the LAP probem. Decson varabes constrants. X H X H (2) jk j j k j We can know from the formua(2) that the servce area whch was not seected for the ogstcs center w not provde transt functon for any transport servce. Transport capacty constrants Q Q (3) j j G L G j G j Q Q (4) j j G L G j G j The formua (3) show that the tota freght voume of goods from the orgnatng staton to ogstcs center ess than transt goods from the orgnatng staton to the termna; the formua (4) represents that the tota freght voume of goods from the ogstcs center to fnshng node ess than transt goods from the orgnatng staton to the termna. Capacty constrants constrants. Q Q (5) j j Gj j Gj The formua(5) above means that the capacty of ogstcs center can meet the demand of passenger transt freght ogstcs network. 3 Reazaton of the Agorthm The reasonabe methods of sovng the ogstcs LAP probem can save cost, speed up crcuaton effcency of goods, ncrease soca benefts. n the LAP probem n expressway ogstcs network [12], n order to determne ogstcs centers from the canddate servce areas, we often screenng out varous nfuencng factors whch assocated wth the LAP probem (ncudng the subjectve factors and objectve factors) [13] accordng to the actua stuaton and ogstcs theory, fnay obtaned dea resuts of our through the comprehensve anayss about the factors. 3.1 Coecton for the decson data The reated data of expressway servce area n ths paper are not ony taken from the authorty of the statstca yearbook and the offca webste, they are aso obtaned through the fu market nvestgaton, and ths paper makes a ratona anayss about the resut whch based on the actua data of canddate ocatons. It s the capta that the deas of the survey data anayss concernng the process of decdng on canddate servce area n LAP probem that requred cost data, at the same tme the canddate servce area economy, potcs, popuaton, resources, envronment and other data. A knds of statstca data requred for ths paper many ncude the foowng aspects: We gan the date of each canddate ogstcs center about the number of popuaton, the state of the economy scae, the ogstcs demand, ISBN: 978-960-474-383-4 139
resources by statstca yearbook. We can gan the nformaton of Shandong provnce expressway servce area date, nfrastructure, convenent transportaton, to staton spacng, servce area scae and functon, the coverage of the canddate servce area and the of dstance nformaton of canddate servce area from the man urban area through the offca webste (Shandong provnce transportaton ha webste, Shandong hgh speed group webste) We can aso know the pocy envronment of canddate ogstcs center, the degree of pubc approva, the government support and other nformaton from the government work report. In order to ensure data reabty and tmeness, ths paper obtan nformaton from the authortatve statstca yearbook, t aso at the tabe 2 Logstcs center ocaton decson factors of Shandong Expressway Group In the above tabe of the decson factors shows that: W 1 represents the economy condton of the cty whch contan canddate servce area, W 2 represents the mportance of the route n canddate servces, W 3 defnes a nfuence ndex of canddate servces area, and W 4 descrbes the dstance between a canddate servce area and ts adjacent cty, W 5 expans a ndex of ogstcs demand voume of the regona area whch canddate servce areas n, W 6 s the popuaton ndex of cty, W 7 s the sze of the canddate servce area, W 8 gves the gross area of same tme make the exstng statstca data anayss and processng, whch can make the date whch s used to evauate s coser to the LAP probem of ogstcs center, fnay make the resut more convncng. For exampe, n the decson-makng ndex of the mportance of servce route, there s no drect data to dspay the mportance ndex, whch ndex can ony be obtaned by statstca anayss the route nformaton accordng to the shortest path nformaton between each par of the ctes. To sum up, ths paper seects 24 nformaton of expressway servce area as the evauaton ndex from the Shandong Expressway Group, whch are shown n the foowng tabe 2: ogstcs center ocaton decson factors of Shandong Expressway Group: NUM Canddate W1 W2 W3 W4 W5 W6 W7 W8 W9 W10 W11 W12 W13 Score 1 De zhou 1915 16 2 10 6563 557 150 13000 10000 1 50 5.00 8.00 2 Xa jn 1915 5 1 80 6563 557 200 6000 2000 0 40 1.00 7.00 3 De nan 1915 16 3 30 6563 557 120 3826 4200 2 60 3.00 8.00 4 Gao tang 1905 5 1 40 7131 579 200 9687 5600 1 80 3.00 7.00 5 Yu cheng 1915 16 1 50 6563 557 180 12800 15000 1 50 3.00 8.00 6 Tan qao 4400 25 4 5 10705 681 120 15691 2625 1 100 7.00 8.00 7 Ta an 2475 25 4 0 7018 549 130 11042 3625 2 60 7.00 8.00 8 Nng yang 2475 30 3 40 7018 549 150 6170 5000 1 50 5.00 8.00 9 Qu fu 2820 30 3 30 6431 808 350 9639 6350 2 50 5.00 8.00 10 Zou cheng 2820 16 1 20 6431 808 80 5420 4168 1 50 4.00 7.00 11 Teng zhou 1560 16 1 10 4243 373 100 6000 6000 1 60 4.00 8.00 12 Zao zhuang 1560 16 3 0 4243 373 130 11000 8000 1 80 5.00 8.00 13 Xue cheng 1560 16 1 5 4243 373 150 6800 3500 0 20 1.00 8.00 14 Cao zhou 1440 16 2 20 4836 829 230 7000 3600 1 30 3.00 8.00 15 Zou png 3280 22 3 15 12119 453 90 6392 6500 2 60 7.00 8.00 16 Z bo 3280 22 4 0 12119 453 192 12000 9830 2 80 8.00 8.00 17 Qng zhou 3600 42 3 20 11862 909 170 6789 6400 2 70 6.00 8.00 18 Fang z 3600 42 2 15 11862 909 246 5168 3800 1 80 4.00 8.00 19 We fang 3600 42 5 0 11862 909 250 12000 12000 3 80 8.00 8.00 20 Gao m 3600 15 2 30 11862 909 80 5600 3000 1 30 2.00 8.00 21 Png du 4907 20 4 32 15802 697 278 11000 2000 3 30 7.00 7.00 22 La x 4907 15 3 38 15802 697 225 3054 500 2 30 6.00 7.00 23 Wen deng 2203 6 2 20 6869 280 170 5000 2700 1 30 3.00 7.00 24 Qng dao 6608 21 5 0 25371 872 164 16801 13000 1 100 9.00 8.00 the canddate servce area, W 9 represents the turnover of the canddate servce area, W 10 represents the accessbty of the canddate servce areas. W 11 s the cost of transformaton and operaton of servce areas, at the same tme the deveopment prospect of the ogstcs servce area s on behaf of W 12, W 13 descrbes the nfrastructure n the servce area. (. The data of W 1, W 5, W 6 are obtaned from the economc deveopment stuaton of Shandong Provnce Statstca Yearbook and the government work report n 2011; data of W 6, W 7, ISBN: 978-960-474-383-4 140
W 8, W 11, W 13 come from the statstca data whch beong to Shandong provnce transportaton ha of offca webste, W 2, W 10 are coected from the expressway network and the dates of W 3, W 12 are obtaned through nvestgaton and statstcs. 3.2 Data standardzaton and ts formua there s no doubt that we often encounter a varety of data types n the process of anaysng about LAP probem, and the dfference between the unt of measure for varous statstca data w ead to the fna evauaton resuts for the convenence of anayss, n order to make the perfect decson, we need put the varous data mode of anayss to be normazed. The standard method used n ths thess s the standard method of maxmum and mnmum vaue, the foowng specfc standardzaton method: Postve ndex (arge for optma ndex) processng method: X Xmn X = (6) Xmax Xmn Negatve ndex (sma s better ndex) processng method: Xmax X X = (7) Xmax Xmn Where X descrbes a knds of ndex vaue for the raw data, X * I represents a knds of data ndex for the normazed vaue. 3.4 Determne the weght The concept of entropy s proposed by the German physcst Causus n 1865, whch s a functon descrpton for the state of system. Vaue and varaton of entropy not ony commony carres to be used on the anayss and comparson. but aso be used to cacuate a dsorder of one system. For the dates n the mode of LAP, whch st exst dfferences between each other even after normazaton. the concept of entropy s put forward to measure the dfference between the same degree factors n statstcs: when a statstca data of each evauaton object s arger, the smaer, entropy, whch s sad the nformaton provded by the ndex s arger; when a statstca data of each evauaton object s smaer, the arger,entropy, whch descrbes the effectve nformaton ndex provde s smaer. When the dfference between the data of a certan evauaton factor s tte, entropy tends to maxmze the, show that the vad nformaton of the ndex s very ow, so we can remove such ndcators from the decson mode. Defnton of entropy: 1 X H = n X n q X (8) q q Defnton of weght: 1 H W =, (0 W 1, W = 1) p H (9) p p where p s the number of ndex data, q s the number of decson-makng object decson makng probem. Prncpa component anayss method: * * 1 ( X )( ) * * X X j X j Rj = 1 q q SS j (10) * X * q X = q (11) * * ( X ) 2 X q S = q 1 (12) Ths paper got a knds of data whch reated to decson mode of LAP by usng Statstcs - descrpton functon based on SPSS software wth the mathematca methods, then tmakes a standardzaton to orgna data based on mathematca expressons above. Lke the foowng tabe: Tabe3 Normazed ocaton factors n decson mode of LAP N canddate X1 X2 X3 X4 X5 X6 X7 X8 X9 X10 X11 X12 X13 score 1 Dezhou 0.09 0.30 0.25 0.13 0.11 0.44 0.35 0.72 0.76 0.33 0.38 0.50 1.00 2 Xajn 0.09 0.00 0.00 1.00 0.11 0.44 0.61 0.21 0.12 0.00 0.25 0.00 0.00 3 Denan 0.09 0.30 0.50 0.38 0.11 0.44 0.20 0.06 0.30 0.67 0.50 0.25 1.00 4 Gaotag 0.09 0.00 0.00 0.50 0.14 0.48 0.61 0.48 0.41 0.33 0.75 0.25 0.00 5 Yucheng 0.09 0.30 0.00 0.63 0.11 0.44 0.51 0.71 1.16 0.33 0.38 0.25 1.00 6 Tanqao 0.57 0.54 0.75 0.06 0.31 0.64 0.20 0.92 0.17 0.33 1.00 0.75 1.00 7 Taan 0.20 0.54 0.75 0.00 0.13 0.43 0.25 0.58 0.25 0.67 0.50 0.75 1.00 8 Nngyang 0.20 0.68 0.50 0.50 0.13 0.43 0.35 0.23 0.36 0.33 0.38 0.50 1.00 ISBN: 978-960-474-383-4 141
9 Qufu 0.27 0.68 0.50 0.38 0.10 0.84 1.36 0.48 0.47 0.67 0.38 0.50 1.00 10 Zoucheng 0.27 0.30 0.00 0.25 0.10 0.84 0.00 0.17 0.29 0.33 0.38 0.38 0.00 11 Tengzhou 0.02 0.30 0.00 0.13 0.00 0.15 0.10 0.21 0.44 0.33 0.50 0.38 1.00 12 Zaozhuang 0.02 0.30 0.50 0.00 0.00 0.15 0.25 0.58 0.60 0.33 0.75 0.50 1.00 13 Xuecheng 0.02 0.30 0.00 0.06 0.00 0.15 0.35 0.27 0.24 0.00 0.00 0.00 1.00 14 Caozhou 0.00 0.30 0.25 0.25 0.03 0.87 0.76 0.29 0.25 0.33 0.13 0.25 1.00 15 Zoupng 0.36 0.46 0.50 0.19 0.37 0.28 0.05 0.24 0.48 0.67 0.50 0.75 1.00 16 Zbo 0.36 0.46 0.75 0.00 0.37 0.28 0.57 0.65 0.75 0.67 0.75 0.88 1.00 17 Qngzhou 0.42 1.00 0.50 0.25 0.36 1.00 0.45 0.27 0.47 0.67 0.63 0.63 1.00 18 Fangz 0.42 1.00 0.25 0.19 0.36 1.00 0.84 0.15 0.26 0.33 0.75 0.38 1.00 19 Wefang 0.42 1.00 1.00 0.00 0.36 1.00 0.86 0.65 0.92 1.00 0.75 0.88 1.00 20 Gaom 0.42 0.27 0.25 0.38 0.36 1.00 0.00 0.19 0.20 0.33 0.13 0.13 1.00 21 Pngdu 0.67 0.41 0.75 0.40 0.55 0.66 1.00 0.58 0.12 1.00 0.13 0.75 0.00 22 Lax 0.67 0.27 0.50 0.48 0.55 0.66 0.73 0.00 0.00 0.67 0.13 0.63 0.00 23 Wendeng 0.15 0.03 0.25 0.25 0.12 0.00 0.45 0.14 0.18 0.33 0.13 0.25 0.00 24 Qngdao 1.00 0.43 1.00 0.00 1.00 0.94 0.42 1.00 1.00 0.33 1.00 1.00 1.00 4 Decson-makng Mode of LAP Method we frst through data standardzaton w we were normazed n the ocaton of decsonmakng reated to the data n the mode, n order to meet the target estabshed at the start of the chapter, that s fndng the optma ogstcs center n LAP probem, ths paper use varous methods based on thought and ayers of SPSS software to mne effectve nformaton. 4.1 Lnear regresson method n SPSS Take steps of Anayss Regresson Weght estmaton n SPSS software to cacuate the weght of nfuence factors X 1, X 2, X 3 X 13 by mathematca expressons. Tabe 4 Weght of nfuence factor Index Weght Index Weght X 1 0.352 X 8 0.119 X 2 0.524 X 9 0.115 X 3 0.129 X 10 0.893 X 4 0.236 X 11 0.265 X 5 0.484 X 12 0.214 X 6 0.087 X 13 0.923 X 7 0.032 Accordng to the concept of entropy weght, we can ceary fnd that the vaue of factor X 13 was sgnfcanty hgher than the other nfuence factors, and cose to 1, so ths knd of nfuence factor ndex X 13 from the 24 canddates have no dfference, and the goba nformaton t contans was sgnfcanty ess than the rest factors, thus the factors X 13 can be sted as a weak correaton factor, and deeted, then decson factors W 13 n tabe3 (red ne) has been removed. 4.1 Lnear regresson method n SPSS In the process of anayss about decson factors, we graduay found that the decson contents (each ndex of canddate servce area) between each other have smar resuts because of the smarty factor data, so the ndex of 24 canddates are dvded nto severa categores whch have approprate argescae system custerng method n SPSS, that not ony can reduce the anayss scae, but aso make the dfferences of the ndex wthn the categores as sma as possbe, dfference between categores s as arge as possbe. Correspondng anayss of the operaton as foows: Step 1: Cck the "anayss" -- "cassfcaton" -- "custer Step 2: Make The decson nfuence factor "X1", "X2"... "X12" be seected nto "varabes" st box. Step 3: Seect the method: make the converson vaue standard for "Z score", seect the button beow "case", "group". Step 4: seect 2-12 n custerng scheme s, determne the preservaton. what the custer number shoud we seect s not determned before the above operaton, so t w requre the cacuaton of a the resuts of the 2to12 cass, anayss about the date as foows: t can be seen from the chart that t can dspay good group and dfference group sex f the canddate are dvded nto 6,7 or 8categores. 4.2 Comprehensve decson of LAP mode 4.2.1Correaton factor The method of factor anayss n SPSS software, to be used to make the correaton varabes dvded nto a fewer sets of varabes whch wth hgh ISBN: 978-960-474-383-4 142
correaton n the same group, and ow correaton n dfferent groups,makes a the orgna varabes can be nstead by a few factors to sove the orgna probem by reducng the number of varabes It shoud be prove that there s a strong correaton between a varabes before we appy the method of factor anayss to sove LAP probem. For exampe, the 12 varabes of ndex are provded n tabe 3 s not ndependent for each other, n order to anayss of the reatonshp between the orgna varabes, we shoud nvestgate the correaton between each other. Ths paper uses the correaton anayss n SPSS to dscuss the correaton, and get the foowng varabe correaton tabe beow: In order to factate the ayout, ths paper ony dscusses the correaton matrx of the frst 8 resuts, whch are vsbe that there s strong or weak postve correaton between these varabes, that s to say the nformaton between the above varabes are overap 4.2.2Factor concentrate by SPSS In a decson probem, there are p decson hypothess, decson sampe n the ndex T data, X = ( x1, x2, x3,, x p ) s random varabes, The common factor whch the paper search s T F = ( f1, f2, f3,, f p ),then we can see factor anayss mode as: X1 = a11f1+ a12f2 + + a1 mfm + ε1 X2 = a21f1 + a22f2 + + a2mfm + ε 2 (13) X = a F + a F + + a F + ε p p1 1 p2 2 pm m p A = (a j) s the oadng matrx of the factor, a j descrbes the oad factor ε s the speca nfuence factors outsde the factor (the actua anayss s neggbe). We can use regresson estmaton method to compute the mathematca mode of factor scores after cacuatng the common factor, and then evauate the case by further cacuatng the factor scores. The formua of factor scores s: F = bx 1 1+ bx 2 2 + + bx n n ( = 1, 2, 3 m) (14) Accordng to the step of "anayss", "reducton" and "factor anayss", we can have the foowng tabe5: C Tabe5 Tota varance expaned Inta Egenvaues Extracton Sums of Squared Loadngs Tota Varance% Cumuatve Tota Varance% Cumuatve 1 4.83 43.98 43.98 4.84 43.98 43.98 2 1.90 17.29 61.27 1.90 17.29 61.27 3 1.15 10.46 71.74 1.15 10.46 71.74 4 1.01 9.23 80.97 1.01 9.23 80.97 5.76 6.94 87.91.76 6.93 87.91 6.43 3.96 91.87 7.37 3.44 95.31 8.20 2.27 97.58 9.14 1.32 98.90 10.10.92 99.82 11.02.17 100 the prncpa component anayss method Tabe6 Component Matrx (Method: Prncpa Component Anayss) Component 1 2 3 4 5 X1.781.549 -.274.123 -.003 X2.648.170.252 -.589 -.168 X3.891.025 -.037 -.232.198 X4 -.515.409 -.023.672.181 X5.759.283 -.090.202.050 X6.490.540.082.298 -.106 X7.253.331.554.301.331 X8.610 -.213.547.401.226 X9.426 -.353.507.341.050 X10.642.315.284 -.694.314 X11.640 -.377.003.151 -.576 X12.922 -.116 -.058 -.191.178 Tabe 7 Component Score Coeffcent Matrx Component 1 2 3 4 5 X1 -.088.460 -.087 -.047 -.049 X2.109 -.238 -.039.620 -.084 X3.310.078 -.029 -.131 -.003 X4 -.269.169.010 -.102.427 X5 -.122.477 -.028 -.107 -.015 X6 -.293.146 -.029.593.050 X7.111 -.122.117.009.680 X8 -.076.052.445 -.205.154 X9 -.130 -.130.498.018.177 X10.500 -.122 -.154 -.101.208 X11 -.129.005.269.215 -.217 X12.275.078.035 -.139 -.046 From the tabe 5 we can see that the 12 orgna factors can be summarzed nto fve components (factors), the cumuatve percentage of the frst component s 43.9% n the tota data, second component s 17.3%, thrd, four or fve components of the proporton s respectvey 10.5%, 9.2%, 6.9%. That s to say the mportance degree of frst component n the fve components s much arger. The cover rate of the fve components s 87.91% n ISBN: 978-960-474-383-4 143
a data, so we can determne that the prncpa component anayss method have very dea effect. Combned wth the component matrx data of tabe 6, factors of X 3, X 5, X 12 score hgher n the frst prncpa components, X 1, X 6 were hgher n the prncpa component 2, and X 7, X 8, X 9 have hgh score n the man components of 3, at the same tme X 2, X 4, X 10 have hgh score n the components of 4, then X 11 scores hgher n the man composton of 5. So t can be cassfed the hgher scores of factors nto the correspondng prncpa component, whch make prncpa components of Z 1 as the ogstcs nfuence factors (ncudng X 3, X 5, X 12 ), the prncpa component Z 2 as the factor of economc popuaton (sze X 1, X 6 ), the prncpa component Z 3 factor as scae of canddate servce area (X 7, X 8, X 9 ), the man composton of Z 4 as convenence degree of the traffc n canddate servce area (ncudng X 2, X 4, X 10 ), the prncpa component Z 5 as nvestment and operaton cost factors (X 11 ). 4.2.3To cacuate the comprehensve scores n ths paper, from Tabe 7 we can gan the score coeffcent matrx of varous components, whch can drecty gan the man components of each fve expresson, the expressons of frst composton are as foows: F = 0.150* X + 0.125* X + 0.171* X 0.099* X + 0.146* X + 0.094* X + 1 1 2 3 4 5 6 0.049* X7 + 0.117* X8 + 0.082*X9 + 0.124*X10 + 0.123*X11+ 0.178*X (15) 12 It w automatcay generate the fve new varabes ke FAC1-1, FAC1-2 FAC4-5 and the ndex data of nfuence factor n the mathematca mode of 24 canddate servce area whch based on fve varabes (prncpa component) n SPSS software. Tabe 7 Prncpa component anayss of the LRP mode num canddate Z1 Z2 Z3 Z4 Z5 score 1 Dezhou -.19310 -.23121.43171.51615.35326-0.18 2 Xajn -1.70152.87836 -.52923 1.77392.46605-0.46 3 Denan -.53884.14081.03075-1.06185.01839-0.31 4 Gaotang -.74439 -.39378.04199 1.31084.39227-0.24 5 Yucheng -.56428 -.89756 1.02267 2.32593.63897-0.04 6 Tanqao 1.00307 -.90986-1.10385 -.03176 -.67934 0.12 7 Taan.46523 -.61212.17287-1.60620.33403-0.01 8 Nngyang -.29266.21194.25602 -.34314 -.34767-0.12 9 Qufu.34766 1.14313 2.17930.61989.59852 0.68 10 Zoucheng -.65233.05567 -.70039 -.23620-1.45890-0.47 11 Tengzhou -.89195-1.11230.01205 -.83744 -.33264-0.68 12 Zaozhuang -.16508-1.83325.36005 -.52998.24491-0.38 13 Xuecheng -1.47313 -.55258 -.04661 -.47863 -.27548-0.81 14 caozhou -.75448.63854.91603.11161 -.28017-0.13 15 Zoupng.30699 -.37888 -.78478-1.24556.30603-0.11 16 Zbo 1.02575-1.02600.20725 -.39490 1.11374 0.34 17 Qngzhou.80867.74105.62835 -.34144-1.64334 0.4 18 Fangz.38277 1.01734.73070.26337-2.34205 0.28 19 Wefang 1.89434.09325 1.89190 -.24333 -.16831 1.01 20 Gaom -.56723.93155-1.48281.16544-1.27883-0.32 21 Pngdu.92539 1.80661 -.24507 -.31498 2.19353 0.82 22 Lax.14267 2.09787-1.27411 -.53466.97137 0.31 23 Wendeng -1.11234 -.07635 -.57442 -.85497 1.28069-0.55 24 qngdao 2.34878 -.73223-2.14036 1.96791 -.10504 0.86 ISBN: 978-960-474-383-4 144
Fgure1 Accordng to the prncpa component features charts n Tabe 4 and the component ndex vaue n Tabe 5 we use formua: comprehensve score equa to prncpa components varance contrbuton rate*prncpa component coeffcents. R = 0.43918* Z1 + 0.17292* Z2 + 0.10467* Z3 + 0.9233* Z4 + 0.6938* Z5 The specfc steps as shown n above Fgure1, t w generate the comprehensve score of decsonmakng n raw data generaton, see tabe dotted ne. When we take the n descendng order to the scores of the tems, t s easy been found that the seven servce area ogstcs center such as Wefang, Qngdao, Pngdu, Qufu, Qngzhou, Zbo, Fangz, tanqao are more satsfed the constructon of ogstcs transt demand n the 24 canddate servce area. To make Wefang, Qngdao, Pngdu, Qufu bud nto a ogstcs center s the most sutabe, whch coverng the rest of the 20 servce areas of the ste. we can take the method extend to 97 servce area of Shandong provnce, n addton to the above four s out of the servce area of East Lawu, Taan West, Jnan, Jnng, Yshu, Lny North can aso be seected as the aternatve servce area. 5 Concuson In order to sove the ogstcs center ocaton (LAP) probem, Ths paper puts forward a ogstcs center ocaton mode (MDLAP) of expressway, whch based on the mnmum transportaton and cost, and use the mut factor decson method of ocaton to determne the ocaton optmzaton probem nto a decson-makng probem. n ths paper, we take the rea data whch from the statstca yearbook, the offca webste of the acquston as the foothod and startng pont, and anayze ogstcs center ocaton probem(mdlap) n great deta of Shandong expressway through ntroducng standardzed data processng, anayss of the entropy weght, the prncpa component method. Ths paper many make fu used of the method of entropy weght regresson, custer anayss, factor anayss and prncpa component anayss whch are the functons of data anayss of SPSS n SPSS to sove the MDLAP probem. The fna decson ocaton probem make 97 servce area under the jursdcton of Shandong Expressway Group n Shandong provnce as the research object, fnay choose ten servce area of Wefang, Qngdao, Pngdu, Qufu, East Lawu, Taan West, Jnan, Jnng, Yshu, Lny North as the most sutabe to expand the servce area. References: [1]HU X, ZHANG Y, LI Z. The vehce route schedung ocaton probem of mutpe dstrbuton centers and ts souton: An SPSS and genetc agorthm based approach[j]. Logstcs Technoogy, 2010, 1: 029. [2]Ross A, Jayaraman V. An evauaton of new heurstcs for the ocaton of cross-docks dstrbuton centers n suppy chan network desgn[j]. Computers & Industra Engneerng, 2008, 55(1): 64-79. [3]Hu X H, Lu C Z, L M, et a. Mathematca modeng for seectng center ocatons for medca and heath suppes reserve n Hanan Provnce[J]. Asan Pacfc journa of tropca medcne, 2014, 7(2): 160-163 [4]Yao- Z. The Constructon of Evauaton Index System of Logstcs Servce Quaty n Onne Shoppng wth Sma B2C and C2C as Exampe[J]. Journa of Anhu Agrcutura Scences, 2013, 1: 177 [5]Gajšek B, Grzybowska K. A cross-county contextua comparson of the understandng of the term ogstcs patform n practce[j]. Research n Logstcs & Producton, 2013, 3 [6]Ser A B, Tompkns L. The bg four: anayzng compex sampe survey data usng SAS, SPSS, STATA, and SUDAAN [C]//Proceedngs of the Thrty-frst Annua SAS Users Group Internatona Conference. 2006: 26-29. [7]Panda S, Padhy N P. Optma ocaton and controer desgn of STATCOM for power system stabty mprovement usng PSO[J]. Journa of the Frankn Insttute, 2008, 345(2): 166-181. [8]Hozbeeren J M, Lopez-Corona E, Bochner B H, et a. Parta cystectomy: a contemporary ISBN: 978-960-474-383-4 145
revew of the Memora Soan-Ketterng Cancer Center experence and recommendatons for patent seecton[j]. The Journa of uroogy, 2004, 172(3): 878-881. [9]Jang Z, Wang D. Mode and agorthm of ocaton optmzaton of dstrbuton centers for B2C e-commerce[j]. Contro and Decson, 2005, 20(10): 1125. [10]J S W, Huang T T, Zhang Y F. Study on Manufacturng Enterprses Dstrbuton Center Locaton[J]. Advanced Materas Research, 2014, 834: 1938-1941. [11]L Y, Lu X, Chen Y. Seecton of ogstcs center ocaton usng Axomatc Fuzzy Set and TOPSIS methodoogy n ogstcs management[j]. Expert Systems wth Appcatons, 2011, 38(6): 7901-7908. [12]Azadeh B, Burkard R E. Unform-cost nverse absoute and vertex center ocaton probems wth edge ength varatons on trees[j]. Dscrete Apped Mathematcs, 2011, 159(8): 706-716. [13]Sun W, Tao L. Coud Logstcs Mode-Based Locaton of Regona Logstcs Dstrbuton Center[J]. Brdges, 2014, 10: 9780784413159.360 ISBN: 978-960-474-383-4 146