都市圈物流规划中的预测研究

上海交通大学硕士学位论文都市圈物流规划中的预测研究姓名：李璐申请学位级别：硕士专业：企业管理指导教师：季建华20070124BPRBFASTUDYONDEMANDFORECASTINGINMETROPOLITANAREA’SLOGISTICSPLANNINGABSTRACTAlongwiththeaccelerationofcivilizationinChina,Metropolitanareahasbeenpaidmoreandmoreattentionbythegovernmentandexperts.Alotofresearchesonmetropolitanarea’sconceptionanditsspacescalehavebeendone,butfewarefocusedonthelogisticsaspect.Theproblemswhichconcerntothelogisticsdemandandlogisticsplanninginmetropolitanareaneedstilltobefurtherstudied.Thepaperrealizedastudyonlogisticsdemandforecastinginmetropolitanarea’slogisticsplanning.Therearemanydefinitionsofmetropolitanarea.ThispapertakesthedefinitiongivenbyProfessorGAORuxi.Firstofall,thispaperanalyzedmetropolitanarea’seconomicalandlogisticscharacteristics.Thenthepaperstudiedthemethodfordefiningthescaleofmetropolitanarea.Basingonthisanalysis,thelogisticsplanninginmetropolitanareaherefocusesonmacroplanningofthelogisticsnetworkwhichiscomposedofnodes(cities)andlines(thetransportationwaysbetweenthecities).Paperproposedaframeoflogisticsplanninginmetropolitanarea.Andthecontentoftheplanninginclude:constructingtheexecutingplatform,analyzingthelogisticsbackground,differentiatingthelogisticsnodes,forecastingthelogisticsdemand,logisticsnetworkplanning,constructingthelogisticsinformationplatform,evaluatingthelogisticssystem.Thispaperfocusesonthestudiesonforecastingthelogisticsdemand,whichisthebasisoflogisticsplanning.Thepapermadesomepropositionstotheexecutivemodeoflogisticsplanninginmetropolitanarea,pointedthatfirstlyitneedstoconstructanexecutingplatform.Thenpaperintroducedsomemethodsforanalysingthelogisticsbackgroundinmetropolitanarea.Next,thepaperprovidedthecriteriafordifferentiatingthecitynodes,andpointedthatthelogisticsplanningofmetropolitanarea’slogisticsnetworkshouldfocusonplanningthehubs.Asthebasisofthelogisticsplanning,thestudyonlogisticsdemandforecastingisthefocusinthispaper.Inthestudiesonlogisticsdemandforecasting,thispaperstudiedfirstlythemetropolitanarea’slogisticsdemanding,gotthelogisticsdemandingfunction.Thenpapercomparedthemethodsforforecastingthelogisticsdemand,andpointedoutthatitneedstocombinetheforecastingresultofthewholemetropolitanarea’slogisticsdemandandalsothatoftheindividualcitiesinthemetropolitanarea.ThenthepaperintroducedtheBPandRBPNerveNetworkmethodforforecastingthelogisticsdemand.Atlast,thepaperpointedoutthatthelogisticsplanningcanbeconductedbasedontheresultsoftheforecasting.KEYWORDS:metropolitanarea,metropolitanlogistics,planning,forecastinglogisticsdemand,nervenetwork200712420071242007124-1-1.11.1.1[1]2060132050198531.7%63.3%GDP68.5%-2-201.1.221-3-1.21.2.120[2][3]SWOT[4]-4-1.2.219Kohl1850191978Hill1982Buxton1982WilsonSandor1997JackFaucettAssociatesJFA/CullinanceBox-JenkinsBox-JenkinsBFIRimmerBlack1981Watson1975[5][6]LODLogistics-Origin-Destination[7]-5-1.2.31.Lowry2.3.[8][9]70TheAnalyticHierarchyProcess,AHP1.31.2.3.-6-2.12.1.1[10]20MetropolitanArea,1910MetropolitanDistrictSMSA54150/km225%5%10PrimaryMetropolitanStatisticalAreasPMSAPMSA50SMSAPMSAConsolidatedMetropolitanStatisticalAreasCMSACMSA100261SMSA17CMSA1957JeanGottmannHillsboroughFairfax6003000Megalopolis1961MegalopolisMegalopolisMegalopolis2500SMSAPMSA-7-CMSAMegalopolis1102319319501960505015%StandardMetropolitanLaborAreaSMLACensusMetropolitanAreasCMA2.1.21989[11]1990[12]-8-151992[13]1992[14]1993[15]1993[16]1995[17]2001[18]2.21.-9-23.2.3-10-2.3.1[19][20][21][22][23]1.2.3.-11-2-1Figure2-1logisticsnetworkstructure2.3.2[24][25]-12-1..2.3.4.-13-2.42.4.12-12-1MA575%150/15%20%SMLA4/115%7CMA1040%15%3010%-14-2-11075%5%575%10%2000P432-1121992[14]1500~300040%1995[26]Urbanizedarea61500/km275%3000/km265%2000~6100/km265%2000[27]12203AGDP75%B60%C4-15-5[10]GDPrDTEEr[28]GDP2ijjjiiijdEPEPWJaY××××=2-12iBBBdEPWJaC×××=2-2YijijCPEGDPdWJaBi[29]-16-2ijjjiiijEGPGPY×=2-3EYC=2-4YCPGGDP()TTBBCYCY′′′′,,,2.4.22.4.2.11.150270%1GDPGDPGDPGDP2-17-32.DE⋅⋅=βα2-5EDαβαβGDP2-22-2αα11.21.50.70.81.10.5βGDP/GDP70%70%≥≥45%45%β0.81.01.2[M]1998E′3.[28]-18-2iCCCiiiCEGPGPY×=2-62iCCCiCEGPC=2-7YCPGGDPicY′C′4.()CYE′′′,,E′Y′C′42.4.2.2300192004GDP2-32-320GDPGDPGDPGDP17450.323450.032515.042350.052158.061910.071313.981226.191173.8101100.6111050.612978.413788.114781.215705.216590.717589.718212.019169.120132.02005200520052005-19-2-3GDP1097.61352.3981%2-42-4GDP820.7104.85%0.845.921630.770.26%0.891.281190.795.51%0.866.641450.570.83%0.8582700.559.76%11351630.754.70%1114.11070.528.58%1.264.22841.538.29%1.2511.21580.757.25%1110.6860.756.97%160.22940.739.24%1.2246.962360.531.39%1.2141.62200.552.86%11102051.225.34%1.2295.21391.241.52%1.2200.162961.247.48%1355.21461.539.52%1.2262.82870.724.58%1.2241.082631.28.74%1.2378.722005200520052005E′2-5-20-12340E≤100100E≤200200E≤300300E2-62-6GDP1352.47450.3598.93450.02163.71.505552.72158.01030.50.944333.91050.6518.80.876773.81226.1750.10.770447.22350.0732.70.715651.72515.0487.70.381267.2781.2119.90.262349.01100.6160.80.259434.71313.9184.30.244583.61910.0183.90.174454.3788.194.70.158257.2590.730.90.079124.4169.17.90.055451.7978.434.60.05296.9212.06.60.046502.