《系统工程》课程教学资源（英文文献）Area Logistics System Based on System

AreaLogisticsSystemBasedonSystemDynamics ModelGUIShouping（桂寿平）,ZHUQiang（朱强）,LULifang（陆丽芳）College of Tra ffic and Communications, South China University of Technology, Gua ngzhou510640,ChinaAbstract: At present, there are few effective ways to analyze area logistics systems. This paper usessystem dynamics to analyze the area logistics system and establishes a systemdynamics model for thearea logistics system based on the characteristics of the area logistics system and system dynamicsNumerical simulations with the system dynamic model were used to analyze a logistic system.Analysis of the Guangzhou economy shows that the model can reflect the actual state ofthe systemobjectively and can be used to make policyand harmonize environment.Key words: system dynamics; area logistics system; simulationIntroductionWithChina's entry into WTO, modern logistics systems are indispensable for fast economic growthand increased market opening.Rational area logistics systems are needed to realize efficient logisticsin China. So, theoretical research that can effectively construct area logistic rationalization becomesmoreimportant.However,thereislittleeffectiveresearchonarealogisticsproblemswithfewresearchmethodsavailableLarge complicated systems, such as social and economic systems, can be more effectively analyzedusing the system dynamics method. The system dynamics model should include nonlinear dynamicmodels with multiple feedback and long-time delays in terms of consequence of social systems andobvious structure ofwhite box" Moreover, the motion of the dynamic systems is solved in computersimulations to analyze the effect ofdecision-making on the system motion. The key part ofthe systemdynamics model is not the data but the model design. The advantage of system dynamics models isthat they include decision trees with consequence and structure, which is the reason why othermethods (ie., econometrics, operational research and analysis of input and output) are not as effectiveas the system dynamics model in analyzing social and economic systems [1-5]This paper describes an area logistics model by us ing qualitative and quantitative system dynamics

Area Logistics System Based on System Dynamics Model GUI Shouping (桂寿平), ZHU Qiang (朱 强), LU Lifang (陆丽芳) College of Traffic and Communications, South China University of Technology, Guangzhou 510640, China Abstract: At present, there are few effective ways to analyze area logistics systems. This paper uses system dynamics to analyze the area logistics system and establishes a system dynamics model for the area logistics system based on the characteristics of the area logistics system and system dynamics. Numerical simulations with the system dynamic model were used to analyze a logistic system. Analysis of the Guangzhou economy shows that the model can reflect the actual state of the system objectively and can be used to make policy and harmonize environment. Key words: system dynamics; area logistics system; simulation Introduction With China’s entry into WTO, modern logistics systems are indispensable for fast economic growth and increased market opening. Rational area logistics systems are needed to realize efficient logistics in China. So, theoretical research that can effectively construct area logistic rationalization becomes more important. However, there is little effective research on area logistics problems with few research methods available. Large complicated systems, such as social and economic systems, can be more effectively analyzed using the system dynamics method. The system dynamics model should include nonlinear dynamic models with multiple feedback and long-time delays in terms of consequence of social systems and obvious structure of “white box”. Moreover, the motion of the dynamic systems is solved in computer simulations to analyze the effect of decision-making on the system motion. The key part of the system dynamics model is not the data but the model design. The advantage of system dynamics models is that they include decision trees with consequence and structure, which is the reason why other methods (i.e., econometrics, operational research and analysis of input and output) are not as effective as the system dynamics model in analyzing social and economic systems [1-5] . This paper describes an area logistics model by using qualitative and quantitative system dynamics

analyses. The information gathered was used to analyze the structure and behavior ofsystems toprovide a scientific basis for decision-making1SystemDynamicsModelforArea LogisticsSystemThe flow chart for the system dynamics model of the area logistics system is given in Fig. 1.The system flow chart illustrates the key steps in the system dynamics model:1)Specify goals and limits: including system boundaries, the system dynamics model researchobject, forecasting ofthe expected system state, observing system features, identifying problems andsystem states related to the problems, limiting the ranges of problems, and choosing appropriatesystem variables [6]2)Analyze the consequences ofsystem decisions: describing factors related to problems,explaining the inside relations among factors, creating a consequence chart, and separating andanalyzing feedback loops and their effects.3)Specify the level and rate variables in the feedback loops and their designs4)Establish the system dynamics model with the DYNAMO equation5)Use computer simulation for solving the DYNAMO equation with the original data and therelated variables to simulate different schemes. The results lead to result graphs and conclusionswhich are used to modify the procedures (equations) and the variables.6)Analyze the results to identify structural errors and the causes ofthese errors. The model maythen be modified with additional simulations until a satisfactory result is achieved

analyses. The information gathered was used to analyze the structure and behavior of systems to provide a scientific basis for decision-making. 1 System Dynamics Model for Area Logistics System The flow chart for the system dynamics model of the area logistics system is given in Fig. 1. The system flow chart illustrates the key steps in the system dynamics model: 1) Specify goals and limits: including system boundaries, the system dynamics model research object, forecasting of the expected system state, observing system features, identifying problems and system states related to the problems, limiting the ranges of problems, and choosing appropriate system variables [6] . 2) Analyze the consequences of system decisions: describing factors related to problems, explaining the inside relations among factors, creating a consequence chart, and separating and analyzing feedback loops and their effects. 3) Specify the level and rate variables in the feedback loops and their designs. 4) Establish the system dynamics model with the DYNAMO equation. 5) Use computer simulation for solving the DYNAMO equation with the original data and the related variables to simulate different schemes. The results lead to result graphs and conclusions which are used to modify the procedures (equations) and the variables. 6) Analyze the results to identify structural errors and the causes of these errors. The model may then be modified with additional simulations until a satisfactory result is achieved

Specify system controlmodel target1Analyze causalityEstablishsystemdynamicmodel flow chartSpecifycontradictoryrelationoffeedbackloopvariableSpecify all variables andparameterdimensions-Specify levels,rates.Specify initial valuesaffiliations,andconstantsand variable step sizesSpecify type of functionsEstimate the sizes ofNboth sides offunctionsTYCompile theDYNAMOfunctionsComputer simulation1Analyze the resultsFig.1 Systemdynamics flowdiagram2 MathematicalModel forArea Logistics SystemThe basic relations between the different parts of the system are given in the consequence chart inFig. 2, which gives an initial layout of the system designInFig.2,the arrows indicate consequence links between twofactors.The plus and minus signsindicate how the two factors influence each other.The plus indicates that the variable at the arrowtipwill increase as the variable at the arrow base increases[7,8], The minus indicates adverse relations

Fig.1 System dynamics flow diagram 2 Mathematical Model for Area Logistics System The basic relations between the different parts of the system are given in the consequence chart in Fig. 2, which gives an initial layout of the system design In Fig. 2, the arrows indicate consequence links between two factors. The plus and minus signs indicate how the two factors influence each other. The plus indicates that the variable at the arrow tip will increase as the variable at the arrow base increases[7,8] . The minus indicates adverse relations

BFGCGPDABRINGREILCLD4cALC++XBRLDGRLDLR+.DCIFLC+LCCFSGC+DLAGRLA1+++LCRIIFIIE/RIDCL+betweenvariablesFig.2 Cause and effect in an area logistics systemThe consequence chart only describes the basic feedback structure framework, not the differencesbetween the different variables. The system consequence chart can be used to develop the systemdynamicsmodelforthearealogisticssystemshowninFig.3There are 29 variables in this model Among these variables, three are level variables, four are ratevariable, seventeen are descriptive variables, two are constants, and three are self-defined variables.Variable definitions:GDP:gross domestic product, NGRE: natural economic growth rate; BRL: logistics baffle rate;GCGP: gross product growth coefficient; BF: baffle factors; ILC: ideal logistics cost; IC: idealcoefficient;LD:

between variables. Fig. 2 Cause and effect in an area logistics system The consequence chart only describes the basic feedback structure framework, not the differences between the different variables. The system consequence chart can be used to develop the system dynamics model for the area logistics system shown in Fig.3. There are 29 variables in this model. Among these variables, three are level variables, four are rate variable, seventeen are descriptive variables, two are constants, and three are self-defined variables. Variable definitions: GDP: gross domestic product; NGRE: natural economic growth rate; BRL: logistics baffle rate; GCGP: gross product growth coefficient; BF: baffle factors; ILC: ideal logistics cost; IC: ideal coefficient; LD:

GCGFBFGDPNGREBRLICTimeDDILCLDALCDCLRGRLDBRLDIFLCCFSGC.LADLAGRLARIE)DCIIFRIFig. 3 System dynamics model for an area logistics systemlogistics difference; ALC: actual logistics cost; LR: logistics requirement; GRLD: logistics demandgrowthrate; BRLD: logistics demand bafflerate;DC:demand coefficient, LC:logistics cost; IFLC:logistics cost influence factor; CF: cost factor, LA: logistics ability; GRLA: logistic ability growthrate; DLA: logistic ability dissipative rate;IE: investment effect, LI: logistics investment, IFI:investmentinfluencefactor;DCL:logisticsdissipativecoefficient,IED:investmenteffectdelay,CRIinvestment convers ion rate; RI: investment ratio; DD: difference delay; SGC: self growth coeffic ient.The systemequations are (DYNAMO equations run on Vensim_ple32):GDP=INTEG (NGRE - BRL);NGRE= GDPxGCGP;BRL=NGRExBFC (DD/ILC);LD=ALC-ILC:DD=DELAY3 (LD, delay time);ILC=LDxthe chart ofIC;LD = INTEG (GRLD- BRLD);GRLD= GDPxthe form ofDC (LA/ LD);BRLD=GRLDxinfluential factorschartofLC:LA= INTEG (GRLA- DRLA);GRLA=LAxSGC+thedelayofIExCRI

Fig. 3 System dynamics model for an area logistics system logistics difference; ALC: actual logistics cost; LR: logistics requirement; GRLD: logistics demand growthrate; BRLD: logistics demand baffle rate; DC: demand coefficient; LC: logistics cost; IFLC: logistics cost influence factor; CF: cost factor; LA: logistics ability; GRLA: logistic ability growth rate; DLA: logistic ability dissipative rate; IE: investment effect; LI: logistics investment; IFI: investment influence factor; DCL: logistics dissipative coefficient; IED: investment effect delay; CRI: investment conversion rate; RI: investment ratio; DD: difference delay; SGC: self growth coefficient. The system equations are (DYNAMO equations run on Vensim_ple32): GDP=INTEG (NGRE – BRL); NGRE = GDP×GCGP; BRL = NGRE×BFC (DD/ILC); LD=ALC – ILC; DD=DELAY3 (LD, delay time); ILC=LD×the chart of IC; LD = INTEG (GRLD – BRLD); GRLD = GDP×the form of DC (LA / LD); BRLD = GRLD×influential factors chart of LC; LA = INTEG (GRLA – DRLA); GRLA = LA×SGC + the delay of IE×CRI;

DLA= LAxDCL;LI=GDPxtheform ofRI (DD/ILC)The DYNAMO equations for the other variables can be expressed in the same way.3Mathematic DescriptionThe system includes state variables (xl , x2, ..., xm), control variables (ul , u2, ..., ur ), and theoutputvariablesthatarerelatedbyasystemof mfirst-orderdifferentialequations:x,=f(....u.,"..u,;),i=1,2...m.The output characteristics are expressed asj=1,2....,h.y,=g,(,..x.u.u..ust),DefineuYX专X2l,XDI-LyX represents the state vector, U represents the con- trol vector, and Y represents the output vector.Thevectorfunction:f(X, U, t) is the state equation, XERm, UER'g(X, U, t) is the output equation, YE RhThe area logistics system is a feedback system with three level variables. The dynamics equationsfor the vectors can be formulated as:L=AL,0000A=(C22 - 1)C2I00(C34 -1)C1where LERm,A is the transfer matrix Ci2represents the baffle factor, C13 represents thedifference de- lay, C14 represents the ideal logistics cost, C21 represents the demand coefficient, C22represents the logistics cost influential factor, C31 represents the natural growth coefficient, and C34represents the dissipative coefficient

DLA = LA×DCL; LI = GDP×the form of RI (DD / ILC). The DYNAMO equations for the other variables can be expressed in the same way. 3 Mathematic Description The system includes state variables (x1 , x2 , ., xm ), control variables (u1 , u2 , ., ur ), and the output variables that are related by a system of m first-order differential equations: The output characteristics are expressed as Define X represents the state vector, U represents the con- trol vector, and Y represents the output vector. The vector function: f(X, U, t) is the state equation, X∈R m，U∈R r ; g(X, U, t) is the output equation, Y∈R h The area logistics system is a feedback system with three level variables. The dynamics equations for the vectors can be formulated as: L =AL, where L∈R m ，A is the transfer matrix, C12 represents the baffle factor, C13 represents the difference de- lay, C14 represents the ideal logistics cost, C21 represents the demand coefficient, C22 represents the logistics cost influential factor, C31 represents the natural growth coefficient, and C34 represents the dissipative coefficient

The mathematical model with initial values of the system state variables was solved using thesimulation software Vensim to calculate the dynamic system changes with time4ModelSimulationandAnalysisThe city ofGuangzhou was used as the special example for the logistic model The results wereused to analyze the system characteristics for the time frame from 1998 to 2028Thesimulations alsousedanRAMPfunctionforthelogisticsdemandtosimulateconstantlineargrowth in the system logistics demand [.LD=INTEG(GRLD-BRLD+RAMP(2000,2000,2010),24 443),Unit: 10 000 [ton]Figure 4 illustrates the effect logistics of the RAMP function in the logistics demand. The logisticsdemand increased slowly at first and then more rapidly after 2012 withtheRAMP function27AWithRAMPfunction+WithoutRAMPfunction(uo1,01)a10199820042010201620222028YearFig. 4 Logistics demand for the Guangzhou systemFigure 5 shows that as the logistics demand rapidly increases between 2000 and 2010, and thelogistics supply cannot meet the demand, the logistics cost will increase rapidly until 2008 and thenslowlydeclinebacktobelowthecostwithouttheRAMPmodel2.0rA-WithRAMPfunction-WithoutRAMPfunction(uo/uen1.0199820042010201620222028Year

The mathematical model with initial values of the system state variables was solved using the simulation software Vensim to calculate the dynamic system changes with time. 4 Model Simulation and Analysis The city of Guangzhou was used as the special example for the logistic model. The results were used to analyze the system characteristics for the time frame from 1998 to 2028. The simulations also used an RAMP function for the logistics demand to simulate constant linear growth in the system logistics demand [1]: LD= INTEG(GRLD – BRLD +RAMP(2000, 2000, 2010), 24 443), Unit: 10 000 [ton]. Figure 4 illustrates the effect logistics of the RAMP function in the logistics demand. The logistics demand increased slowly at first and then more rapidly after 2012 with the RAMP function. Fig. 4 Logistics demand for the Guangzhou system Figure 5 shows that as the logistics demand rapidly increases between 2000 and 2010, and the logistics supply cannot meet the demand, the logistics cost will increase rapidly until 2008 and then slowly decline back to below the cost without the RAMP model

Fig.5Logistics costfortheGuangzhousystemFigure 6 shows how the logistics supply ability increases as the logistics demand increases.Without suchincreases,thelogistics difference would increase which would enlargethebaffling effectontheeconomicdevelopmentcausedbythelogisticsmismatchFigure 7 illustrates how the logistics investment increases with and without the RAMP model toimprove the logistics supply. The investment until 2015 creates sufficient supply so that theinvestment may be reduced after 2016 to levels below the system without the RAMP model100WithRAMPfunctionWithoutRAMPfunction(uO1,OD)VT50199820042010201620222028YearFig.6Logistics supply ability forthe Guangzhou systemWithRAMPfunctionWithoutRAMPfunction(uenAoro1)IT0199820042010201620222028YearFig. 7 Logistics investment for the Guangzhou system Figure 8 shows that because the growth inthe logistics demandwiththeRAMPmodel is larger thanthe natural growthbetween2000 and 2010,the demand exceeds the supply and, as a result, the GDP growth will increase

Fig. 5 Logistics cost for the Guangzhou system Figure 6 shows how the logistics supply ability increases as the logistics demand increases. Without such increases, the logistics difference would increase which would enlarge the baffling effect on the economic development caused by the logistics mismatch. Figure 7 illustrates how the logistics investment increases with and without the RAMP model to improve the logistics supply. The investment until 2015 creates sufficient supply so that the investment may be reduced after 2016 to levels below the system without the RAMP model. Fig. 6 Logistics supply ability for the Guangzhou system Fig. 7 Logistics investment for the Guangzhou system Figure 8 shows that because the growth in the logistics demand with the RAMP model is larger than the natural growth between 2000 and 2010, the demand exceeds the supply and, as a result, the GDP growth will increase

4WithRAMPfunctionWithoutRAMPfunction3(uenAe,01)dao21OT199820042010201620222028YearFig.8 GDPgrowth for the Guangzhou system5ConclusionsThe system dynamics model for the area logistics provides a tool for studying area logistics. Themethod fully utilizes the advantages of systemdynamics bycombiningpolicy decisions withpracticaloperations to deepen understanding of the system mechanisms. This method is more intuitive andunderstandablethanother systems.TheanalysisoftheGuangzhoulogisticssystem showsthatthemodel provides better results that more objectively reflect the actual state of the system

Fig. 8 GDP growth for the Guangzhou system 5 Conclusions The system dynamics model for the area logistics provides a tool for studying area logistics. The method fully utilizes the advantages of system dynamics by combining policy decisions with practical operations to deepen understanding of the system mechanisms. This method is more intuitive and understandable than other systems. The analysis of the Guangzhou logistics system shows that the model provides better results that more objectively reflect the actual state of the system