Zpeng Zhang A prncpa component anayss usng SPSS for Mut-objectve Decson Locaton Aocaton Probem ZIPENG ZHANG Schoo of Management Scence and Engneerng Shandong Norma Unversty No.88 Cuture Rode, Jnan Cty, Shandong Provnce CHINA zhangzpeng20@126.com Abstract: - In order to sove the ocaton aocaton probem wth mut-objectve decson (MDLAP), ths paper creatvey combnes a cost-based mathematca optmzaton mode, whch transforms the dstrbuton ocaton probem nto a two-stage ogstcs ocaton seecton decson one. In the process of sovng the bottom mode, ths paper puts forward many methods such as data standardzaton, entropy weght, prncpa component anayss and mathematca expressons by usng SPSS soft. In the process of sovng the top mode, ths paper use mmune agorthm to appy expermenta smuaton whch based on ogstcs demand and ocaton data. In the process of anayss, there are many methods to be provded to sove the above mode, such as the weghted near regresson method, the smarty anayss system custerng method, the prncpa component regresson method and the Immune agorthm. As a resut, the 97 canddate servce area n Shandong Provnce are seected nto 9 servce areas of We Fang, Qngdao, Png Du, Q Fu and so on, n order to be the better optma ogstcs deveopment area. Ths mode avods the ambguty of the tradtona methods, and we can better sove the optma number and ocaton probem n the LAP. Key-Words: - SPSS; ocaton seecton mode; prncpa component regresson method 1 Introducton Wth the rapd deveopment of Chnas economy, the ogstcs and dstrbuton busness have attracted many researchers from varous feds workng on t over the past few years. Many sgnfcant theores have been proved that the dstrbuton pays a pvota roe n the ogstcs system. In order to make dfferent requrements to be devered to the hands of customers accuratey and effectvey, the man targets of ths paper s to make a tmey ogstcs system research, whch can gan practca vaue to sovng the above probem. Domestc and foregn schoars have conducted a ot of research and put forward many ocaton modes. A arge number of studes show: the ogstcs dstrbuton ocaton probem s a mut-objectve optmzaton probem wth compex constrants, and t beongs to the NPhard probem. These optmzaton agorthms, such as taboo search agorthm, genetc agorthm and ant coony agorthm, aso caed heurstc search agorthm, often acheved no better resuts when the scae s arge. we unabe to obtan the goba optma ocaton resut, because these agorthms searchng speed s sow and easy to fa nto oca optmum. To quantfy and extrapoate a better dea, we shoud deveop more effcent anaytca and computatona approaches. Logstcs ocaton aocaton probem (LAP) [1] can be traced back to the 1909 ssue of Weber, t frst treats 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), capactated mut stage mode (CMLAP) and mut-objectve ocaton mode (MOLAP) [2-5]. Most of the peope research the capacty-mt snge stage mode to sove LAP up to the present. At present, the study of ogstcs functon n expressway servce area many focuses on the feasbty anayss and management research. Ceng Zhaogeng[6] (2008) ponts out that the expressway servce area n Chna w deveop to three drectons: frst, some w change from rest functon to esure functon, second, part of them w become the ogstcs node, fnay, the expressway servce area w become an mportant patform, and t can be used for commerce and trade crcuaton. Zheng Zhpng[7] (2011), Mu Guosheng[8] (2011)there s a anayss about Fujan, Shangha, Nanjng E-ISSN: 2224-2872 698 Voume 14, 2015
Zpeng Zhang expressway servce area deveopment advantages of modern ogstcs base, t ceary 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, hs anayss about the thrd party ogstcs servce area n LAP feasbty s orgna, and t s based on the deveopment of nterna reatons between the expressway servce area and the deveopment of modern ogstcs ndustry, fnay he put forward the strategc pan and strategy of LAP n Shandong expressway servce area. In short, souton of the MDLAP yeds many nsghts ncudng the foowng: one s a dscusson about the deveopment drecton of expressway servce areas. The second s the feasbty and countermeasures for the deveopment of ogstcs ocaton probem. But the methods above have not become an mportant ssue of ong standng nterest to economsts and transportaton scentsts, and especay there s no deep research on the expressway servce area ogstcs functon network. It shoud be under the condton of deveopment of key technooges. In summary, the hghway ogstcs functon has some academc research papers, but t s mosty feasbe research and management to deveop ts ogstcs functon, the terature of LAP n expressway servce area ogstcs node s rarey, ony X Jun [10] anayss and dscusses comprehensve evauaton method of aternatve nodes, t use the prncpa component n expressway servce LAP probem, and aso 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, he use t to acheve good resuts n the ange of regona ogstcs and the expressway servce area ntegraton. The am of the paper s to sove ogstcs ocaton probems by usng dfferent souton approaches. In ths paper, a decson-makng mathematca mode, wth mut-attrbute group, s proposed. Ths paper uses SPSS soft to anayze the reatonshp between many reevant ndcators and ogstcs demands. In ths paper, we put forward four steps to sove the reaty network probems of expressway traffc n Shandong provnce. Frst t takes the topoogca structure as a startng pont. Secondy, t puts the cost-based and transfer-based LAP probem as the target. In the thrd step, ths paper creatvey combnes the mathematca optmzaton mode wth a two-stage ogstcs ocaton. In the end, we obtan the souton to cost-based decson probem and the transport-based optmzaton probem. Ths paper not ony puts forward a method of data standardzaton processng to sove the bottom mode, but aso appes the artfca mmune agorthm to determne the number and ocaton of the ogstca centres. The above two methods remedy the dsadvantages of a decson method whch s wth smpe factor. It s easer to get accurate resuts of ocaton. 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 (M) and ogstcs demand (N) has been fxed. In order to provde fnshed products for the ow cost ogstcs demand, storage, transfer, processng, management and other servces, the system requrement seects one or a puraty of ogstcs from the canddate ogstcs nodes. For exampe, there are 97 expressway servce area of expressway n Shandong provnce, and t s 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 n the servce area accordng to the specfc decson method. 2.2 Mathematca mode of the LAP probem The seecton of a ogstcs ocaton among aternatve ocatons s a mut target decsonmakng probem ncudng both quanttatve and quatatve crtera. The mathematca mode s used as the ogstcs node from 97 aternatves one from Shandong provnce expressway servce area, of course t shoud based on the tota demand for ogstcs and transport costs, thus we can acheve the owest tota cost of ogstcs and transport n ths area. As everyone knows, the goods of transport dversty have varous knds. And dfferent types of goods have dfferent dstrbuton costs, whch due to ts weght, voume, tmeness and portabe degree caused by transportaton. The tota transportaton cost and voume s provded as the target n ths paper, we defned a condton that the dstrbuton cost s no reated to the types of transport goods. In ths paper we buds a LAP optmzaton mode based on ths consderaton, and the mode s cose to the reaty of the cassfcaton n the poston of E-ISSN: 2224-2872 699 Voume 14, 2015
Zpeng Zhang that the canddate ogstcs and ogstcs demand nodes s known, of course t s under the stuaton of not consderng the transportaton storage fee, management fee and transport cost and freght traffc s proportona to the dstance. The mode s as foows: MnC = [ C X D Q + G Lk N G j G j j jk j j C X D Q ] + H (F S + W Q ) (1) j jk j j j jc j jc j j Gj Symbo Tabe1 Symbo and Defnton of 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 } Expressway servce areas j N = {k/ k = 1, 2 } The set of vehces L= { / = 1, 2, 3 } Dfferent types of goods F jc W jc j Budng cost of the ogstcs Operatng costs of the ogstcs 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 D j ogstc node j to fnshng node Transportaton dstance of goods from ogstc node j to fnshng node Q Transportaton of goods from ogstc node j S j Q j 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 ogstc. And t s not been provded 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 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 to fnshng node ess than transt goods from the orgnatng staton to the termna. Capacty constrants constrant. Q Q (5) j j Gj j Gj The formua (5) means that the capacty of ogstcs can meet the demand of passenger transt freght ogstcs network. Cost reatons constrants COST= F + C D (6) jc Formua (6) shows the reaton about the cost and the transportaton cost, whch shoud consttute ke the formua (6). 3 Reazaton of the Agorthm As for sovng the ogstcs LAP probem, we seect some reasonabe methods, such as that we not ony can save cost, but aso speed up crcuaton effcency, ncrease soca benefts. About the LAP probem of expressway ogstcs network[12], we w determne correct ogstcs centres from the canddate servce areas, n the process, varous nfuencng reasons behnd the stuaton are assocated together wth the LAP probem (ncudng the subjectve factors and objectve factors ) [13]. Accordng to the actua stuaton and ogstcs theory, fnay we 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. j j E-ISSN: 2224-2872 700 Voume 14, 2015
Zpeng Zhang By the way, we aso use the anaytc herarchy process to consodate decson-makers assessments. Through prncpa component anayss method usng SPSS soft, ths paper anayzes on 13 ndcators shown n Tabe 2. Through standardzed treatment of orgna matrx such as the economy, potcs, popuaton, resources, envronment and so on, the correaton matrx w be shown n tabe 3. A knds of statstca data requred for ths paper many ncude the foowng aspects: We gan the dates whch contan the number of popuaton, the state of the economy scae, the ogstcs demand of each canddate centre from the statstca yearbook. They can be used by dfferent theoretc backgrounds and reate dfferenty to the dscpne of mut-attrbute group decson-makng. We can gan the expressway servce nformaton of Shandong provnce, whch ncude nfrastructure, convenent transportaton, to staton spacng, servce area scae, functon, coverage and the dstance nformaton. We can aso know the pocy envronment of canddate ogstcs centre, the degree of pubc approva, the government support and other nformaton from the government work report. The conventona approaches to ogstcs ocaton probem tend to be ess effectve n deang wth the mprecse or vagueness nature of the ngustc assessment. In order to ensure data reabty and tmeness, ths paper obtans nformaton from the authortatve statstca yearbook. Ths paper aso can make the exstng statstca data anaysed and processed. It makes the date cose to the LAP probem of ogstcs centre, and the resut s 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. Accordng to the shortest path nformaton between each par of the ctes, we need the ndex ony be obtaned by statstca anayss of the route nformaton. To sum up, ths paper seects 24 nformaton of expressway servce area as the evauaton ndex from the Shandong Expressway Group, whch s shown n the foowng tabe 2: ogstcs centre ocaton decson factors of Shandong Expressway Group: Tabe 2 Logstcs Centre ocaton Decson Factors of Shandong Expressway Group Num Canddate W 1 W 2 W 3 W 4 W 5 W 6 W 7 W 8 W 9 W 10 W 11 W 12 W 13 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 G 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 Q 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 Nn Yang 2475 30 3 40 7018 549 150 6170 5000 1 50 5.00 8.00 9 Q Fu 2820 30 3 30 6431 808 350 9639 6350 2 50 5.00 8.00 10 Z Cheng 2820 16 1 20 6431 808 80 5420 4168 1 50 4.00 7.00 11 Ten Zhou 1560 16 1 10 4243 373 100 6000 6000 1 60 4.00 8.00 12 Z Zhang 1560 16 3 0 4243 373 130 11000 8000 1 80 5.00 8.00 13 X 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 Z 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 Go 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 In the above tabe of the decson factors shows that: W 1 represents the economy condton of the E-ISSN: 2224-2872 701 Voume 14, 2015
Zpeng Zhang 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 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. The data of W 6, W 7, 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, and W 12 are obtaned through nvestgaton and statstcs. 3.2 Data standardzaton and ts formua In the process of anaysng about LAP probem, there s no doubt that, we often encounter a varety of data types, and the dfference between the unts of measure for varous statstca data w ead to the fna evauaton resuts for the convenence of anayss. Under many stuatons, the vaues of the quatatve crtera are often mprecsey defned for the decson-makers. In 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 = (7) Xmax Xmn Negatve ndex (sma s better ndex) processng method: Xmax X X = (8) 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. 3.3 Determne the weght The concept of entropy 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 mode, 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, ths can 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 (9) q q Defnton of weght: 1 H W =, (0 W 1, W = 1) p H (10) 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: R j 1 = 1 q X * * ( X )( ) * * X X j X j q j * = q q X * SS * * ( X ) 2 X (11) (12) q S = q 1 (13) Ths paper got a knds of data whch reated to decson mode, by usng Statstcs descrpton functon that n SPSS soft. It makes a standard to orgna data based on mathematca expressons above. Through standardzed treatment of orgna matrx above, the correaton matrx w be shown n tabe 3, ke the foowng: E-ISSN: 2224-2872 702 Voume 14, 2015
Zpeng Zhang Tabe3 Normazed Locaton Factors n Decson Mode of LAP N canddate X 1 X 2 X 3 X 4 X 5 X 6 X 7 X 8 X 9 X 10 X 11 X 12 X 13 score 1 De Zhou 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 Xa Jn 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 De Nan 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 Go Tang 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 Yu Cheng 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 Tan Q 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 Ta An 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 Nn Yang 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 9 Q Fu 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 Z Cheng 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 Ten Zhou 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 Z Zhang 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 X Cheng 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 Cao Zhou 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 Z Png 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 Qng Zhou 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 Fang Z 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 We Fang 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 Go M 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 Png Du 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 La X 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 Wen Deng 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 Qng Dao 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 In order to meet the target estabshed at the start of the chapter, we frsty use the method of data standardzaton on the normazed sets to estabsh the mode of the decson-makng probem, the resut of correaton anayss w gude us to fnd the correct concuson that whether the servce areas are but nto ogstcs centres. In ths secton, we 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 Infuence 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 other nfuence factors, 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 tabe 3 (red ne) has been removed. 4.2 Custer method n SPSS In the process of anayss of decson factors, we graduay fnd that: because of the smar factors, the decson contents (each ndex of canddate servce area) between each other have smar resuts. The ndex of 24 canddates s dvded nto severa categores, and they have approprated arge-scae system custerng method n SPSS. Ths can not ony reduce the anayss scae, but aso makes the dfferences of the ndex n the categores as sma as possbe and the dfference between categores as arge as possbe. Correspondng anayss of the operaton as foows: Step 1: Cck the "anayss" -- "cassfcaton" -- E-ISSN: 2224-2872 703 Voume 14, 2015
Zpeng Zhang "custer ". Step 2: Make the decson nfuence factor "X1", "X2"... "X12" to 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 custer number we shoud seect s not determned before the above operaton, so t requres the cacuaton of a the resuts of the 2 to12 cass, anayss of the date as foows: t can be seen from the chart that t can dspay good group and dfferent group f the canddate are dvded nto 6,7 or 8 categores. 4.3 Comprehensve decson of LAP mode 4.3.1 Correaton factor The method of factor anayss n SPSS software w be used to make the correaton varabes dvded nto a fewer sets of varabes. And t has hgh correaton n the same group, and ow correaton n dfferent groups. Accordng to ths method, we can cacuate the correaton coeffcent matrx to sove the orgna probem. When varance account for 85% of tota varances, then we can seect the prncpa components We can see from the Tabe 5 that fve extracted prncpa components refect 89.62% nformaton of prmtve varabes. It shoud be proved 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 n tabe 3 s not ndependent for each other. In order to anayss of the reatonshp between the orgna varabes, we shoud nvestgate the correaton between each other. Ths paper uses the correaton anayss of SPSS to dscuss the correaton, and gets 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. 4.3.2 Factor 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 ( 1, 2, 3,, ) T F = f f f 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 tabe 5: Tabe 5 Tota Varance Expaned C Inta vaues 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 0.76 6.94 87.91 0.76 6.93 87.91 6 0.43 3.96 91.87 7 0.37 3.44 95.31 8 0.20 2.27 97.58 the prncpa component anayss method Tabe 6 Component Matrx (Method: Prncpa Component Anayss) Component 1 2 3 4 5 X 1 0.781 0.549-0.274 0.123-0.003 X 2 0.648 0.170 0.252-0.589-0.168 X 3 0.891 0.025-0.037-0.232 0.198 X 4-0.515 0.409-0.023 0.672 0.181 X 5 0.759 0.283-0.090 0.202 0.050 X 6 0.490 0.540 0.082 0.298-0.106 X 7 0.253 0.331 0.554 0.301 0.331 X 8 0.610-0.213 0.547 0.401 0.226 X 9 0.426-0.353 0.507 0.341 0.050 X 10 0.642 0.315 0.284-0.694 0.314 X 11 0.640-0.377 0.003 0.151-0.576 X 12 0.922-0.116-0.058-0.191 0.178 E-ISSN: 2224-2872 704 Voume 14, 2015
Zpeng Zhang Tabe 7 Component Score Coeffcent Matrx Component 1 2 3 4 5 X 1-0.088 0.460-0.087-0.047-0.049 X 2 0.109-0.238-0.039 0.620-0.084 X 3 0.310 0.078-0.029-0.131-0.003 X 4-0.269 0.169 0.010-0.102 0.427 X 5-0.122 0.477-0.028-0.107-0.015 X 6-0.293 0.146-0.029 0.593 0.050 X 7 0.111-0.122 0.117 0.009 0.680 X 8-0.076 0.052 0.445-0.205 0.154 X 9-0.130-0.130 0.498 0.018 0.177 X 10 0.500-0.122-0.154-0.101 0.208 X 11-0.129 0.005 0.269 0.215-0.217 X 12 0.275 0.078 0.035-0.139-0.046 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 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 ). 4.3.3 To cacuate the comprehensve scores In 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 Tabe 8 Prncpa Component Anayss of the LRP Mode Num Canddate Z 1 Z 2 Z 3 Z 4 Z 5 Score 1 De Zhou -0.19310-0.23121 0.43171 0.51615 0.35326-0.18 2 Xa Jn -1.70152 0.87836-0.52923 1.77392 0.46605-0.46 3 De Nan -0.53884 0.14081 0.03075-1.06185 0.01839-0.31 4 Go Tang -0.74439-0.39378 0.04199 1.31084 0.39227-0.24 5 Yu Cheng -0.56428-0.89756 1.02267 2.32593 0.63897-0.04 6 Tan Q 1.00307-0.90986-1.10385-0.03176-0.67934 0.12 7 Ta An 0.46523-0.61212 0.17287-1.60620 0.33403-0.01 8 Nn Yang -0.29266 0.21194 0.25602-0.34314-0.34767-0.12 9 Q Fu 0.34766 1.14313 2.17930 0.61989 0.59852 0.68 10 Z Cheng -0.65233 0.05567-0.70039-0.23620-1.45890-0.47 11 Ten Zhou -0.89195-1.11230 0.01205-0.83744-0.33264-0.68 12 Z Zhang -0.16508-1.83325 0.36005-0.52998 0.24491-0.38 13 X Cheng -1.47313-0.55258-0.04661-0.47863-0.27548-0.81 14 Cao Zhou -0.75448 0.63854 0.91603 0.11161-0.28017-0.13 15 Z Png 0.30699-0.37888-0.78478-1.24556 0.30603-0.11 16 Zbo 1.02575-1.02600 0.20725-0.39490 1.11374 0.34 17 Qn Zhou 0.80867 0.74105 0.62835-0.34144-1.64334 0.4 18 Fang z 0.38277 1.01734 0.73070 0.26337-2.34205 0.28 19 We Fang 1.89434 0.09325 1.89190-0.24333-0.16831 1.01 20 Go M -0.56723 0.93155-1.48281 0.16544-1.27883-0.32 21 Png Du 0.92539 1.80661-0.24507-0.31498 2.19353 0.82 22 La X 0.14267 2.09787-1.27411-0.53466 0.97137 0.31 23 We Deng -1.11234-0.07635-0.57442 -.85497 1.28069-0.55 24 Qng Dao 2.34878-0.73223-2.14036 1.96791-0.10504 0.86 E-ISSN: 2224-2872 705 Voume 14, 2015
Zpeng Zhang expressons, 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* X + 0.117* X + 0.082*X + 0.124*X + 0.123*X + 0.178*X (15) 7 8 9 10 11 12 ogstcs demand. In the condton of ths nformaton, we make use of artfca mmune agorthm to optmze the souton to the LAP probem. The expermenta resut s st by fgure 1, fgure2, fgure3, fgure4, fgure5; and t sgns n to represent the number of ogstcs centre among them. In the secton 4.3.3, we seect 14 aternatve servce areas. By comparng the fgure1-5, when the number of ogstcs centre s 8 or 9, the effcency (such as the optma servce dstance and number) of the LAP mode s the best. Fgure 1 Cacuaton Wndow of Component n SPSS 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 scores of the tems by order n descendng, t s easy been found that the seven servce areas such as We Fang, Qngdao, Png Du, Q Fu, Qng Zhou, Zbo, Fang Z, Tan Q can satsfed the demand constructon between the 24 canddate servce area. We can take ths method extendng to a 97 servce area of Shandong provnce to make the ast resuts, n addton to the above, the servce area of East La Wu, Ta An West, Jn Nan, Jn Nn, Y Su, Ln Y North can aso be seected as the aternatve servce area. Fgure 2 Schematc Dagram of Logstcs Centre(n=8) Fgure 3 Schematc Dagram of Logstcs Centre(n=9) 4.4 Expermenta resuts of LAP mode In order to fnd the optmzaton number of centres n the ogstcs ocaton probem, ths secton performs many expermenta anayses by usng mmune agorthm. We can see that, the probem of whch servce area can be converted nto a ogstcs centre s determned n the prevous secton 4.3, however, t dd not dscuss how to determne the number of ogstcs centre. In ths secton, there exsts much condtona nformaton about such cty s date as ongtude, attude and E-ISSN: 2224-2872 706 Voume 14, 2015
Zpeng Zhang Fgure 4 Schematc Dagram of Logstcs Centre(n=10) Fgure 5 Schematc Dagram of Logstcs Centre(n=12) 5 Concuson Fnay, we have acheved an optmzaton resut by usng the method of SPSS and mmune agorthm. Takng nto account quanttatve and quatatve crtera, these approaches are extended to seect the best ogstcs ocaton aternatve. In ths paper, the rea data whch coected from the statstca yearbook and the offca webstes s made as basc research. We frsty make use of the methods n SPSS, whch ncude entropy weght regresson, custer anayss, factor anayss and prncpa component anayss. Through the optmzaton of mmune agorthm, we fnay choose 9 servce areas from Shandong expressway group as the most sutabe ogstc areas. There exsts a common feature n such varetes of transport as raway, avaton, shppng, hghway and others, that the method above can hep them to carry capacty expanson. The methods of ogstcs ocaton n ths paper have more advantages than others. As the transportaton of passenger and ogstcs have many smar characterstcs, the methods and deas of LRP research n hghway network can aso be apped to other modes of transportaton. The snge mode of transport n ogstcs has many cost and effcency defects, so the future research shoud focus on the mut-object, mut-mode of transport. It s supposed that the optmzed arge-scae system n a comprehensve mut-transport network w be used wdey. References: [1] HU X, ZHANG Y, LI Z. The vehce route schedung ocaton probem of mutpe dstrbuton s 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 s 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 mode for seectng 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 revew of the Memora Soan-Ketterng Cancer 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 s 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 Locaton [J]. Advanced Materas Research, 2014, 834: 1938-1941. [11] L Y, Lu X, Chen Y. Seecton of ogstcs 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 ocaton probems wth edge ength varatons on trees [J]. Dscrete Apped Mathematcs, 2011, 159(8): 706-716. [13] Vt F, Rnad M, Corman F, et a. Assessng parta observabty n network sensor ocaton probems [J]. Transportaton research part B: methodoogca, 2014, 70: 65-89. E-ISSN: 2224-2872 707 Voume 14, 2015