Study on Task Decomposition in Emergency Logistics based on

System Dynamics

Jun Su

and Li-jun Cao

School of Management, Jinan University, Guangdong, Guangzhou, 510632, P.R.China

School of International Business, Jinan University, Guangdong, Zhuhai, 519070, P.R.China

Keywords: System Dynamics, Dynamic Alliance of Logistics, Task Decomposition, Emergency.

Abstract: It analyzed several key factors by system dynamics that the task decomposition in emergency logistics

impact on dynamic alliance of logistics. These factors included inter-constraints, quality of cooperation,

collaboration time, ability to adapt with each other, core capabilities of logistics. To establish diagram and

system dynamics model, it can forecast and analysis disadvantages of task decomposition in emergency

logistics. It can for the government emergency management to provide strategic adjustment decision

support. On this basis, it simulated the task decomposition of system dynamics model on dynamic alliance

of logistics by EXCEL, tested and verified this way was a feasible approach.

1 INTRODUCTION

When unexpected events occurred, government

organized the dynamic alliance of logistics quickly

for transporting emergency supplies. Their primary

job is to break down the missions into several sub-

tasks, and then look for federates of dynamic

alliance of logistics for each sub-tasks. In the

process, the government should consider which way

is the best of task decomposition.

The extent of task decomposition determines the

number of federates in logistics dynamic alliance

adapt to the emergency incident, the different

running status of logistics dynamic alliance, and the

success or failure to the emergency task ultimately.

But the extent of task decomposition influence by

many indicators, such as the mandate of the total

stipulated completion time, each sub-task stipulated

completion time, the working ability in core part of

task, and ability to adapt to each other. It should be

used to the co-ordinate system for ensuring access to

the optimal task decomposition scheme.

When unexpected event occurred, the

management system of emergency logistics is a non-

stable, non-equilibrium dynamics of the process

system. It should not be used the way as solve stable

systems to resolving such issues. The system

dynamics is to study the behavior of complex

feedback systems in the computer simulation

method, it can start from the system as a whole, find

and study of related factors within the system. It also

can focus on the dynamics of process and causality

in logistics system, and to solve complex problems

in a non-complete non-state analysis of information

(Hu et al., 2006).

Currently, it has a lot of studies in task

decomposition, particularly in large enterprises and

multi-enterprise collaboration between departments

in manufacturing. Pi (2006) studied and explored the

significance and role in task decomposition of

aerospace; Chen (1998) focused on analysis of task

decomposition in the Boeing Commercial Aircraft

Manufacturing Engineering System; Hu et al.

(2005a) proposed the optimization of the virtual

enterprise partner selection model based on the task

decomposition, and the same year, she proposed

process of building a virtual enterprise framework

based on task decomposition (Hu et al., 2005b);

Zhang et al. (2007) addressed a multi-level projects

across the enterprise network planning method based

on task decomposition, to solve multi-level program

consistency problem in cross-enterprise projects.

This article built a dynamic alliance of logistics

simulation model of task decomposition, with the

impact of the relationship between the relevant

indicators based on system dynamics theory .Finally,

it discussed the model simulation results and

applications.

301

Su J. and Cao L..

Study on Task Decomposition in Emergency Logistics based on System Dynamics.

DOI: 10.5220/0003963703010304

In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS-2012), pages 301-304

ISBN: 978-989-8565-10-5

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

2 THE ANALYSIS OF THE

CAUSAL RELATIONSHIP IN

TASK DECOMPOSITION OF

EMERGENCY LOGISTICS

Use of system dynamics to build the flow of causal

relationship diagram, It can effectively express the

relationship of system feedback, and identify the

location of the proper task decomposition. In the

process of task decomposition of logistics dynamic

alliance, the quality of cooperation, collaboration

time and so on, can be affected by many factors,

such as logistics facilities and equipment damage,

road conditions after expected events, government

policies, as well as the impact from different quality

of the federate and so on. According to the causal

relationship between the determinants, and being

combined with other variables in the decomposition

of the project, it used VENSIM to build system flow

diagram, shown in Figure1.

Figure 1: The flow chart of causal elements in emergency

logistics task decomposition.

It is usually that the more federates the more

bindings. To be targeted strategic adjustments

timely, such as replace members of union, can

improve the collaboration efficiency and adapt of

ability, and prevent the growth trend of inherent

constraints in logistics dynamic alliance. But at the

same time, the completion time of task will be

extended, and emergency supplies cannot be

delivered on time. It can bring many dangers to the

people in disaster areas and the losses of economic.

3 CONSTRUCTION OF SYSTEM

DYNAMICS MODEL

Logistics dynamic alliance is an organization which

be needed to maintain close between logistics

enterprises. It requires federates matched hardware

and software resources. With the increasing number

of federates, the demanding of breakpoints have

become increasingly in the supply chain. It

expressed as the increasing of intrinsic constraints.

Based on this consideration, this paper

modified the Pearl curve model, the formula is

expressed as:

(1)

−

(1)

In here, “n” is expressed that the required total

number of enterprises in a particular task of

emergency logistics alliance, and it takes a positive

real number. The “k” is the limit of the “y”, and it

takes “50”. “a” and “b” are the model parameters,

and it takes that “a” is “1”and “b” is “1”.

In addition, it should be noted that the

followings, such as: if it has only one company in

alliance (n = 1), at this time, that means “y” is “25”,

this is the minimum constrains; but with the

increasing number of federates, the increasing “y”

was, and the “50” is assumed maximum constrains

of “y”.

It uses DYNAMO language to the identity

(Zhao, 2010). “K” is the current moment and “J” is

the last moment. “DT” said that the steps between

“K” and “J”. It makes “DT” is “1” initially, and you

can adjust it in the actual simulation process.

Conveniently, it will use the letters to replace

each variable, as shown in Table 1:

Table 1: The alphabet of variable corresponding.

Letters Variable

A Inherent constraints

B The quality of collaboration

C Collaboration time

D Ability to adapt to each other

E The core of logistics capability

M Strategic adjustment

N The ability of members

SF The factor of sudden impact

GF The factor of government impact

TF The factor of technology matching

With the causal relationship in Figure 1, the

alliance model is expressed as:

ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems

302

(1)

()

pearl n

−−

(2)

With the increasing number of federates, the

inherent constraints is growth. To reduce the

inherent constraints, government can make strategic

adjustments to the members of logistics dynamic

alliance. Shown as:

A.K=A.J+PEARL

（

）

-M

(

)

The growth of the quality of collaboration will be

affected by ability to adapt to each other, the core of

logistics capability, and the factor of technology

matching. Shown as:

B.K=B.J+DT*

（

D.J+E.J+N+TF

）

(4)

With the increasing of inherent constraints, the

collaboration time is increased. In addition, some

unexpected event will lead to collaboration time

changes. Shown as:

C.K=C.J+DT*

（

A.J+SF

）

(5)

Ability to adapt to each other is mainly affected

by the ability of members and the factor of

technology matching. In addition, it also can be

influenced by the government and inherent

constraints. Shown as:

D.K=D.J+DT*

（

N+TF+M-A.J

）

(6)

The core logistics capabilities can be influenced

by the decision of government. in addition, it can be

also affected by the strategic adjustment, and the

ability of members. Shown as:

E.K=E.J+DT*

（

M+N+GF

）

(7)

4 SIMULATIONS

4.1 Realization of the Simulation

It used EXCEL to achieve the model simulation.

The initial value of variable represents the initial

state of the system. According to the actual input,

these variables will be simulated by iterative

changes to future operating conditions of dynamic

alliance of logistics. The flow diagram of system

dynamics, which used in the characteristic

parameters of the reaction system, should be

depended on specific characteristics of dynamic

alliance of logistics in the simulation.

After the simulation running, firstly, it was input

the initial value of variables. Then, it can be set

parameters to simulate the actual situation according

to the special. By view of output value and table,

future running of the dynamic alliance of logistics

can be mastered. After the model data generated, the

data generated will be out of the curve form of

visual representation.

4.2 Example

It selected the representative data form one

particular Union in the task decomposition stage,

shown in Table 2, and selected the other parameters

for model to do the initial value of iteration. Here,

“n” is “10”.

Table 2: The table of initial value of each variable in

Table.

Variable A B C D E

initial value 0 20 50 10 20

The data listed in Table 2 is designed to verify

the validity of the model of artificial data. In

practice, the representative from government and

dynamic alliance of logistics enterprises provided

the data and input to the program according to the

actual. By the simulation of the data in Table 2, the

output of the model can express the relative value of

each factor trends. It verified that system dynamics

model created whether show the effectiveness of

impact of relationships between the task

decomposition and the evaluation factors or not.

And it also verified whether can achieve the best

solution by application of this model. Put the data in

Table 2 into EXCEL and get changes in each index.

Shown as Figure 2.

Figure 2: The changes map in each index to model.

The simulation results of the analysis of the figure:

(1) Adding a degree of inherent constraints

will bring the improving of the quality of

collaboration. However, when the alliance members

to a certain amount of time, the quality of

collaboration will deteriorate. The ability to adapt to

each other also will be bad.

(2) Internal constraints may not necessarily

bring about the extension of time collaboration. As

StudyonTaskDecompositioninEmergencyLogisticsbasedonSystemDynamics

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members of the co-ordinated, but to a certain extent,

increasing of the number of members will cut down

the collaboration time.

By validated, it can illustrate the feasibility of this

model. This model can provide some reference value

of information to the government in the extent of the

emergency task decomposition.

4.3 Significance

Mainly reflected in two aspects:

(1) Some person from emergency

management department of government and

dynamic alliance of logistics are in charge of

discussing to achieve the initial values of variable.

Next to simulation, it can be given the optimal

extent of decomposition. Then informed of the

operational status of the future trend of alliance, it

can help the government have more in-depth grasp

of the dynamic alliance of logistics.

(2) When the emergency of task

decomposition cannot be changed, it can design a set

of strategies of different intensity adjustment

programs and put it simulate together with the

current of initial value from logistics alliance. This

has been a different result set. From these results,

select a few good according to the logistics alliance

needs and the strategic adjustment of the

corresponding intensity is the best solution. It can

provide a basis for strategic adjustment in

emergency management for government.

5 CONCLUSIONS

This text used the method of system dynamics to

construct modeling and simulation studies in task

decomposition of dynamic alliance of logistics in the

supply chain of emergency. Based on analysis of the

key elements of the task decomposition of causality

in dynamic alliance of logistics, it established a

causal flow diagram and system dynamics model.

And furthermore, it used random data to achieve the

simulation in EXCEL. As can be conclude from the

simulation results, the design of the model can

express the causal relationship between the key

factors of the task decomposition in emergency

logistics effectively. The conclusion is in line with

the operation of conventional dynamic alliance of

logistics.

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