Research on the Enterprise Raw Material Ordering and

Transshipment Problems based on the Dual-objective Planning

Model: Taking a Building Materials Enterprise as an Example

Linfei Zhang

, Yuan Gao

and Mingxuan Zhou

School of Economics, Henan University, Kaifeng, China

School of Eurasia International, Henan University, Kaifeng, China

Keywords: Enterprise Cost Reduction Plan; Comprehensive Evaluation System Construction; Dual-Objective Planning

Model; Price Correction Factor.

Abstract: Based on hierarchical clustering, technique for order preference by similarity to an ideal solution and dual-

objective planning models, we give manufacturers a raw material ordering and transshipment solution

involving multiple supply chain participants, including supplier evaluation, cost control and loss control.

Compared with the traditional increase of constraint conditions to optimize the model, we introduced the price

correction factor containing multiple components from the perspective of system analysis to simulate the

dynamic characteristics of the supply chain system. By constructing the "relative ordering price", the form of

the objective function is affected in real time, thereby increasing the understanding space, so that the objective

function in the process of solving can be more close to the global optimal solution in a limited number of

iterations .The empirical results pointed out that after the introduction of the price correction factor, the

ordering plan formulated has obvious selectivity to the production materials, which significantly improves the

production enterprises’ anti-risk ability and capacity improvement potential, thereby promoting the

transformation and upgrading of the enterprises and also improving its operational management capabilities

and supply chain management capabilities.

1 INTRODUCTION

1.1 Research Background

In recent years, as the global value chain layout has

been further adjusted due to the impact of the covid-

19, the degree of uncertainty in the economic market

has deepened and the government's macro-control

role in the “horizontal integrated” supply chain

management model has become extremely important.

From China’s 2017 “Guiding Opinions of the

General Office of the State Council on Actively

Promoting Supply Chain Innovation and

Application”, which promoting innovation of

industrial organization and government governance

methods, to promote management modernization and

upgrading of industrial structure in the “14th Five-

https://orcid.org/0000-0003-3746-408X

https://orcid.org/0000-0003-4379-1060

https://orcid.org/0000-0002-6071-4334

Year Plan and 2035 Vision Goals Outline” ,from the

second plenary session to the sixth plenary session of

the 19th Central Committee, in the spirit of dealing

with excess capacity and promoting the optimization

and upgrading of the supply chain and industry chain,

enterprises are not only the main body pursuing

individual economic interests, but also need to

perform corresponding social responsibility.

Enterprises need the assistance of government

governance to maximize their profits, which in turn

promotes social and economic development. The

production and operation transactions between the

upstream and downstream of the supply chain are

continuously optimized and upgraded under the

guidance of policies.

Under the influence of the pandemic, China's

building materials industry has been greatly affected

but still maintains a steady upward development

Zhang, L., Gao, Y. and Zhou, M.

Research on the Enterprise Raw Material Ordering and Transshipment Problems based on the Dual-objective Planning Model Taking a Building Materials Enterprise as an Example.

DOI: 10.5220/0011150200003437

In Proceedings of the 1st International Conference on Public Management and Big Data Analysis (PMBDA 2021), pages 22-31

ISBN: 978-989-758-589-0

trend. According to the “Building Materials Industry

and Economic Operation in 2020”, China’s building

materials industry has grown by 2.8% year-on-year

with generally stable prices and rising profits. We

take a small and medium-sized building materials

enterprise as our research object and analyze its order

volume, supply volume and transshipment data of

various participants in the past ten years to formulated

the optimal raw material ordering plan and

transshipment plan. Based on this, production

enterprises can adjust their development strategy and

accelerate the process of transforming and upgrading.

1.2 Research Purpose and Significance

China’s “14th Five-Year Plan” outline a procedure to

transform and upgrade traditional industries,

especially promoting the layout optimization and

structural adjustment of the raw material industries

such as petrochemicals, steel, nonferrous metals and

building materials to promote green building

materials. The development of the building materials

industry will shift from “incremental expansion” to

“improving quality and efficiency”. We take a certain

building materials enterprise as the research object

and introduce the price correction factor to simulate

the dynamic characteristics of the supply chain. Our

primary method is to build a dual-objective planning

model to analyze the enterprise's optimal ordering

plan and transshipment plan for raw materials from

the perspective of supply chain management.

China’s supply chain industry has developed

rapidly in recent years. Existing researches often

focus on a single manufacturing enterprise as research

object and generally do not consider the dynamic

characteristics of the supply chain system in the

modeling process. With the idea of system analysis,

we creatively introduce the price correction factor,

which can simulate the dynamic characteristics of the

supply chain system to a certain extent. Our planning

model under this correction can not only reduce the

cost of the production enterprise, but also

significantly improve the enterprise's ability to resist

risks and increase the potential for productivity.

Further we take the price correction factor as the

coefficient of the decision variable to affect the form

of the objective function. As a result, in the process of

optimizing the solution result of the model, there are

only a few and concise constraints, which simplifies

the complex processing methods of the traditional

model.

While promoting enterprises to reduce costs,

improve their ability to resist risks and increase

production potential, our results also have certain

reference significance for the transformation and

upgrading of China’s material industry, operation

management and supply chain management in the

future development and contribute to social and

economic construction and development.

1.3 Existing Research

With regard to the research on supply chain ordering

and transshipment, relevant conclusions have been

discussed since the 1970s. A.A. (Gaballa 1974) first

used the mathematical programming model to study

the problem of supplier selection. K. and S. (Skouri

and Papachristos 2002) proposed damage cost,

holding cost and so on.

C.O. (Brien 2006) adopted the

ownership goal method to evolve the single-objective

cost planning model into a multi-objective cost

planning model, marking the formal formation of the

supply chain logistics integration mode. Michael

(Stuart 2010) starts from the three aspects of order

acquisition, transportation, and inventory to minimize

the sum of order processing costs, transportation

costs, and inventory costs. Some scholars (Liu et al.

2017) used AHP and TOPSIS evaluation models to

reconstruct a scientific and effective supplier

evaluation system. Some also (Zhang et al. 2010)

mentioned the importance of realizing the mutual

sharing of information between enterprises at various

nodes during cost control based on the perspective of

supply chain

. Other schoalrs (Liu 2019) think that

enterprises should not only regard the supply chain as

a product supply ,transshipment and storage, but

should recognize the relationship between supply

chain changes and product development and

marketing.

In summary, we can find that there are many

research results on supply chain research methods and

supplier selection at home and abroad, but most of

them stay on the level of cost control and do not

consider the dynamic characteristics of the supply

chain system, thus the solution results are limited to

the improvement of supply chain stability and

capacity potential. Therefore, with the rapid

development of information economy and support of

government policies, it is imperative to formulate

effective methods on the optimal ordering plan and

transshipment plan from the perspective of system

analysis.

1.4 Research Content and Methods

For production enterprises, ensuring their normal

production, operation and maintenance is the most

basic and important thing. Some enterprises may have

Research on the Enterprise Raw Material Ordering and Transshipment Problems based on the Dual-objective Planning Model Taking a

Building Materials Enterprise as an Example

seasonal changes in supply, they will get a very low

score in the comprehensive evaluation, or even be

directly excluded. This is obviously unreasonable.

Therefore, prior to the comprehensive evaluation,

first perform hierarchical clustering based on the

supply capacity of the manufacturer and rank the

suppliers with reference to the clustering results and

actual conditions. Next, we carry out the “protection”

operation for the higher-rated suppliers.

Based on that, we conduct a comprehensive

evaluation of suppliers based on TOPSIS and

quantitatively analyze their value to the production

enterprise through the index system of “supply

capacity, supply stability, supply satisfaction rate,

average response time”. In the formulation of

ordering and transshipment plans for production

enterprises, we take the most economical ordering

and the smallest transshipment loss as the objective

function, set the constraint conditions with the lowest

storage threshold and other actual conditions and

creatively introduce the “price correction factor”.

The price correction factor comprehensively

considers the characteristics of the production

materials, the comprehensive ratings of suppliers and

the supply and demand status of the production

materials, which includes “linear components”,

“composite components” and “time-varying

components”, which are combined based on the

coefficient of variation method. Similar to the

adjustment of prices by macro market conditions, the

price correction factor also simulates the

characteristics of the supply chain system to a certain

extent. “Relative order price” also has dynamic

characteristics.

Figure 1: Analogy diagram of price correction factors.

Analog communication system may be easier to

understand the role of price correction factor (see

figure 1): if the order price before correction is

analogous to the input signal, then the price correction

factor can be compared to a system function of this

nonlinear time-varying system, and the price

correction factor can be used to represent an

approximately equivalent supply chain (AESC)

system, and the revised “relative price” also has the

characteristics of this system.

We conducted an empirical analysis based on the

ordering and transshipment data of a building

materials enterprise with various suppliers and the

transshipment data of various forwarders in the past

ten years (Data from the second author’s family

business which is authorized to use).The analysis

results pointed out that after the introduction of price

correction ,the subsequent model solution results

shows a clear preference towards the raw materials,

and even some ordering nodes completely abandon

certain types of raw materials. In fact, C raw material

is a kind of high pollution and low pollution that have

been gradually eliminated in recent years. But in the

absence of price correction factors, this tendency is

not reflected in the solution results of the model.

From the perspective of the storage change curve

before and after the price correction factor is

introduced, the solution result before the introduction

of this factor makes the production enterprise often in

the two extreme situations of raw material backlog

and raw material shortage. And according to the

quantitative analysis of the three parameters "average

replenishment response cycle", "storage vigilance

level" and "peak storage volume", the solution result

after introducing the price correction factor enables

the production enterprise to have stronger capacity

improvement potential and anti-risk ability.

2 SYMBOL DESCRIPTION

Table 1: Symbol Description.

Symbol Description Unit

Supply volume of the i-th

supplier in week t

Order amount of the i-th supplier

in week t

J-th raw material

J-th raw material price

The number of j-th raw materials

consumed per cubic meter of

product

M-th forwarder

Loss rate of the m-th forwarder

()

The price correction factor for

the j-th raw material in week t

()

ij j

ijjj

()X

()

() () ()YXH

ωωω

NLTV

ESC

PMBDA 2021 - International Conference on Public Management and Big Data Analysis

3 ASSUMPTIONS

 The enterprise has continuous production for

48 weeks a year.It has a weekly production

capacity of H million cubic meters and raw

material inventory should meet the production

capacity requirements for the next two weeks;

 The production enterprise has 2H million

storage capacity in initial week ;

 There are n suppliers, and one supplier only

produces one kind of raw material;

 The supplier only corresponds to the order

quantity sent once each time, and the supplier

responds to the order each time;

 All transshipment enterprises’ maximum

weekly transshipment capacity is 60 million

cubic meters;

 The weekly supply of a supplier is transshipped

by a forwarder.

 There are s kinds of raw materials, the price of

each raw material remains constant at every

week.

4 MODEL

4.1 Supplier Evaluation

4.1.1 Supplier Rating System based on

Hierarchical Clustering Algorithm

Based on the total supply volume of each supplier in

the past Y years, we use a systematic clustering

method based on supply capacity and the elbow rule

to comprehensively classify suppliers with actual

situation considered. Specific classification methods

are as follows.

First priority supplier: They can stably provide a

large amount of production materials in actual

situation and often has a strategic cooperative

relationship with the manufacturer.

Second-priority suppliers: Their supply

characteristics are also stable, medium-range or have

periodic large-scale supply.

The third priority supplier: They are used as an

alternative to the production enterprise

Fourth priority supplier: They are generally

excluded and does not participate in the

comprehensive evaluation.

4.1.2 Supplier Selection Evaluation Index

System

In order to select a certain number of qualified

suppliers from n suppliers, we establish an index

system to evaluate the supplier's supply

characteristics. The evaluation system is as follows.

Table 2: Evaluation System of Suppliers.

Symbol Description Unit

The supply capacity of the i-

th supplier

Supply stability of the i-th

supplier

Supply satisfaction rate of the

i-th supplier

The average response time of

the i-th supplier

week

Here are the definitions of four evaluation

indicators.

a)Supply capacity

(1)

b)Supply stability

(2)

This indicator is an extremely small type

indicator, i.e. the smaller the value, the more stable of

the supply.

c)Supply satisfaction

(3)

Define this indicator to measure the matching

degree of supply and demand.

d)Average response time

Considering that there may be many unordered or

unshipped situations in the actual ordering and

shipping data, here is the definition of the annual

effective order quantity and the annual effective

shipment quantity.

The effective ordering quantity of the

manufacturing enterprise to the i-th supplier at the n-

th effective ordering node in a certain year can be

defined as

(4)

iit



100%

=×



( ) ( ), 1, 2,3...48

iE io i

On OtkTk=− =

Research on the Enterprise Raw Material Ordering and Transshipment Problems based on the Dual-objective Planning Model Taking a

Building Materials Enterprise as an Example

Among them, the effective ordering node of the i-

th supplier in the k-th sampling

()

can be

defined as

(5)

Similarly, the effective supply volume of the n-th

effective supplying node of the i-th supplier in a

certain year can be defined as

(6)

Among them, the effective supplying node of the

i-th supplier in the k-th sampling can be defined as

(7)

According to the supplier only corresponds to the

order quantity sent once each time, and the supplier

responds to the order each time, there is

(8)

According to the above definition, the average

response time of the i-th enterprise in the past Y years

can be defined as

(9)

The effective ordering sampling sequence of the

i-th supplier can be expressed by the inverse

function of the effective shipping node as

(10)

Similarly, the effective supplying sampling

sequence of the i-th supplier can be expressed as

(11)

Obviously, this indicator should be regarded as an

extremely small type indicator, that is, the smaller its

value, the quicker the supplier can supply the

manufacturer.

4.1.3 Quantitative Evaluation of Suppliers

based on TOPSIS

Use TOPSIS to normalize and standardize the four

indicators of all suppliers, and calculate the scores of

n suppliers and sort the suppliers accordingly.

4.2 The Establishment of

Dual-objective Planning Model

In order to formulate the most economical ordering

plan and transshipment plan with the least loss rate in

the next

weeks, the enterprise needs to evaluate and

select four parts of raw materials, suppliers, ordering

quantities, and forwarders so as to construct two

optimal solutions.

4.2.1 The Introduction of Price Correction

Factors

We comprehensively consider multiple participants in

the supply chain and introduce the price correction

factors, which will affect the ordering and

transshipment strategies of manufacturers by

modifying the unit price of raw materials.

Therefore, the price correction factor for the

raw material in the week can be

decomposed into linear components ，complex

component and time-varying

component

Linear components can be defined as

(12)

is the input-output ratio of the j-th raw

material.

Composite components can be defined as

(13)

ark

is the average score of the supplier that

produces the j-th raw material.

Time-varying components can be defined as

(14)

1, ( ) 0

() ,

0, ( ) 0

1, 2,3...max( )

io i

io io

nOtkT

Ot kT

+−≠





−=



( ) ( ), 1,2,3...48

iE i s i

Sn StkTk=− =

1, ( ) 0

() ,

0, ( ) 0

1, 2, 3......max( )

is i

is is

nStkT

St kT

+−≠





−=



io is i

nnn==

max( )

(() ())

max( )

yisE i y ioE i

knk n

−



ioE

()

() (), () 0

ioE i io io

kn nknk

−

=≠

() (), () 0

isE i is is

kn nknk

−

=≠

()

th−

tth−

()

min

max min

−

min

max min

Mark Mark

ark Mark

−

min

(1) (1)

()

max min

(1) (1)

Wt Wt



−



−−





−



−−



PMBDA 2021 - International Conference on Public Management and Big Data Analysis

is the storage volume of the j-th raw

material in the t-th week.

Since some suppliers may have seasonal and

periodic supply characteristics, considering that the

entropy method is susceptible to the impact of the

jump value, and the coefficient of variation can well

explain the value of the jump value. In this case, the

coefficient of variation method is used to synthesize

the above three components.

Let , are

coefficients of variation of the above three

components.

It should be noted that is also a function of

time, so is called a time-varying component.

In summary, the price correction factor can be

expressed as

(15)

In addition, different from the impact of macro

objectively factors such as the market supply and

demand relationship on the actual order price of raw

materials, the "correction" here is partial, which is a

relative correction to ensure the maximum benefit of

the production enterprise. The revised order prices are

references only for formulating the ordering plan and

the transshipment plan.

4.2.2 Formulation of the Most Economical

Ordering Plan

Aiming at n suppliers for s kinds of raw materials, we

try to formulate the most economical raw material

ordering plan for the enterprise in the next t weeks.

The economic expenditure includes ordering cost,

transshipment cost and storage cost. the objective

function is that these three types of expenditure are

the smallest.

First, the ordering cost can be defined as

(16)

is a logical variable which takes value one if

i-th supplier produces j-th raw material, otherwise is

zero.

Second, for z forwarders, the transshipment cost

can be defined as

(17)

is a logical variable which takes value one if

m-th forwarder transship the raw material of the i-th

supplier, otherwise is zero.

Third, for the raw materials transferred by z

forwarders, the storage cost can be defined as

(18)

Take the historical average of the same

period in the past Y years

(19)

Considering that the decision variable of the

objective function is , we give a formula to

describe the relationship between and .

(20)

Here, we use the historical average of over

the same period as a measure of the supply

satisfaction rate of the i-th supplier in the next t weeks

(21)

In summary, the objective function with the

lowest total cost is

(22)

4.2.3 The Formulation of the Least Loss

Plan

According to the above definition, the objective

function of the lowest loss rate is

( 2 3 )

4.2.4 Constrains

In order to ensure the normal operation and

maintenance of production enterprises, combined

with the actual conditions of each link of the supply

chain, constraints are as follows.

According to assumption A and B, the weekly

storage volume needs to be able to maintain the

enterprise's production capacity for two weeks,

namely

()

[

]

123

VVV

123

,,VVV

()

12 3

() ()

jljcjvj

=+ +

ij j j it

ij t

CMPO



()

mi it

mi t

CTF TS



((1))

mt it

mi t

CTR S

=−



((48))









()

it i i it

SfnO=

max( )

(max())

()

yiE i i

Sn ny

On ny









1123

min ( )

OCCC=++

min ( )

it mi mt it

mi t

OTS



Research on the Enterprise Raw Material Ordering and Transshipment Problems based on the Dual-objective Planning Model Taking a

Building Materials Enterprise as an Example

(24)

According to assumption C, a supplier only

produces one kind of raw material, so the rank of

is 1 all the time.

(25)

According to assumption E, the weekly

transshipment capacity of each forwarder is 6000

cubic meters at most, that is

( 2 6 )

According to each supplier’s raw materials have

and only one forwarder is responsible for the

transshipment every week, so



(27)

In summary, the dual-objective programming

model can be described as

(28)

5 EMPIRICAL RESULTS

5.1 Problem Introduction

Combined with the above, we are now studying the

ordering and transshipment plan of a building

materials enterprise in the next 24 weeks.

In this example, in addition to satisfying the above

assumptions, a total of 369 suppliers, 8 forwarders, 4

types of raw materials, the prices of them are

and weekly production

capacity of the manufacturer are

. The quantity of four kinds of raw materials required

to produce building material per cubic meter is

，，，,which

means

. Besides, we still

have the supply condition of these 369 suppliers and

weekly transshipment loss rate of forwarders in the

past ten years. According to the above discussion, we

follow the steps below to study the ordering and

transshipment plan of the manufacturer

5.2 Simulation Solution

5.2.1 Hierarchical Clustering Analysis based

on Supplier's Supply Capacity

Combined with the pedigree chart and the elbow rule,

we divide 396 suppliers into 4 categories, and carry

out the “protection” operation for the first and second

types of suppliers, which means even if they fail to

enter the top 80 in the comprehensive evaluation in

the next step, we will replace the original supplier

from back to front.

5.2.2 Comprehensive Evaluation based on

TOPSIS

Based on the evaluation system and protection

operations constructed above, a

comprehensive

evaluation of these 396 suppliers was made and the

top 80 were evaluated.

Table 3: Top Eighty Suppliers.

Rank Supplier Materials Score

1 S229 A 0.067374975

2 S361 D 0.062735875

3 S140 B 0.05772693

4 S108 A 0.04809566

5 S151 C 0.040590773

...... ...... ...... ......

S223

C 0.001923605

S237

A 0.001915819

S324

C 0.001904659

S092

D 0.001896674

Among the top 80 suppliers, the scores of the

suppliers who supply the four raw materials are as

follows.

Table 4: Scores of The Suppliers Who Supply Four

Materials.

Materials

Number of

suppliers

Average

Score

Highest

Score

A 26 0.013341073 0.067374975

B 19 0.014170486 0.05772693

C 14 0.005367099 0.040590773

D 21 0.008707384 0.062735875

111 2

10 ( 1) 2 10

asn t

mt j it

mji t t

SPt P

ση

=== =

−−≥×

 

RM =（

6000

mi it

mi t

TS t

≤



11 11

min ( ) ( ( )

.. ( (1 ) ))

min ( )

ns an

it ij j j it mi it

ij t mi t

mt it

mi t

it mi mt it

mi t

OMPOTFTS

of TR S

fO T S

== ==





+−







 



[

]

1.0,1.2,1.5,1.3

P =

3.12 10Hm=×

0.68m

0.70m

0.77m

0.73m

[

]

0.68,0.70, 0.77, 0.73

PMBDA 2021 - International Conference on Public Management and Big Data Analysis

5.3 Analysis

5.3.1 The Influence of Linear Component

and Composite Component on Raw

Material Ordering Preference

Since materials A and C are the most popular and the

least popular materials respectively, their order ratio

is used as the basis for analysis.

Figure 2: Ordering ratio of A and C raw materials.

It can be concluded that before the introduction of

the price correction factor and the nature of the raw

material C itself, starting from the goal of the least

loss and the lowest cost, there has been

From figure 2, after the introduction of the price

correction factor, the manufacturer’s preference for

raw material A has increased significantly, and even

the situation of has occurred. This is

caused by the fact that the manufacturer did not order

raw material C at some nodes. This shows that the

price correction factor has been strengthened the

profit gap between different raw materials.

From the analysis of the composition of price

correction factors, there are linear components

directly related to the input-output ratio of raw

materials and composite components directly related

to the supplier’s score. Therefore, differences in these

factors are no longer only reflected as constraints, but

directly modifies the weekly ordering prices of

different raw materials, and their values directly

affect the coefficient of the decision variable, which

is quantified in the objective function.

5.3.2 The Influence of Time-varying

Components on the Storage Volume of

Raw Materials

We calculate the weekly storage volume before and

after the price correction factors are introduced.

Figure 3: Storage volume of change curve.

From figure 3, we know that before the

introduction of the price correction factor, the overall

storage volume change curve shows the following

characteristics:

 The peak storage volume is too high, and

excessive storage of raw materials is prone to

occur.

 Low sensitivity of the enterprise to decreasing

in storage volume.

 The average replenishment response cycle is

longer, that is, the storage volume is at a low

level for a longer time.

 Lower storage vigilance level, that is, the

timing of replenishment is often selected when

the storage volume is very close to the

minimum level.

In contrast, after introducing the price correction

factor, the overall storage volume change

curve

shows the following characteristics:

 Peak reserves are relatively moderate, and the

backlog of raw materials is not easy to appear.

 The enterprise is more sensitive to the decline

in storage volume.

 The average replenishment response cycle is

shorter, the time when the storage volume is at

a lower level is shorter.

 Higher storage vigilance level , that is, when

the storage volume reaches a moderate value,

the restocking starts.

The main characteristic value of the storage

volume change curve mentioned above are as

follows.

()

1 [1, 24]

()

MO t

always hold for any t

MO t

≥∈，

()

MO t

→∞

Research on the Enterprise Raw Material Ordering and Transshipment Problems based on the Dual-objective Planning Model Taking a

Building Materials Enterprise as an Example

Table 5: Characteristic Parameters of Storage Volume.

Change Curve

Average

Response

cycle

Peak

Reserves

Restocking

Vigilance

Level

Before

Correction

4.625 38200 31430

After

Correction

2.783 35290 32680

From a global perspective, before and after the

price correction factor is introduced, the solved total

storage cost can be maintained at a low value. For the

former, this is due to its lower storage vigilance level,

higher replenishment response time and higher

storage peaks. It should be noted that, for a

manufacturing enterprise, having these

characteristics at the same time means that the

enterprise is often in a bad closed loop of production

raw material backlog-rapid consumption of

production raw materials-urgent production of raw

materials-large purchase of production raw materials.

According to the actual situation, combined with the

storage volume change curve of the production

enterprise in such a bad closed loop, the following

conclusions can be obtained:

Affected by the low storage vigilance level,

enterprise in the replenishment period often need to

purchase a large amount of raw materials for

production, and at this time they have just gone

through a period of raw material backlog, so the

expenditure during this period is higher than average.

Under the combined influence of these two factors,

production enterprises are prone to accidents such as

the break of the capital chain.

When the production enterprise needs to increase

production capacity to a certain extent, its long

replenishment response time and poor sensitivity to

the decline in storage volume determine that this goal

is difficult to achieve.

After the introduction of price correction factors,

the above problems have been effectively alleviated,

and the value of each characteristic of the storage

volume change curve is in a relatively reasonable

range, which can effectively improve the risk

resistance of manufacturers and can accept a certain

degree of increase in production capacity. This can be

explained by the composition of the price correction

factor, where there is a time-varying component

directly related to the storage volume, and its value is

inversely proportional to the weekly storage volume

of each raw material. After the price correction factor

is introduced, the weekly storage volume will no

longer only be reflected as a constraint, but as a time-

varying component to directly modify the weekly

order prices of different raw materials. Its value

directly affects the coefficient of the decision variable

and is reflected in the objective function

quantitatively.

5.3.3 Analysis of the Influence of Price

Correction Factors on the

Dual-objective Programming Model

The price correction factor comprehensively

considers the influence of multiple participants in the

supply chain, contains multiple components, and its

value can be used as a coefficient of a decision

variable to affect the form of the objective function in

real time. This directly increases the solution space of

the objective function, making it closer to the global

optimal solution in a limited number of iterations.

Compared with the traditional method of adding more

constraints, it is highly subjective, static, and

significantly increases the complexity of the model. It

is a means of improving the enterprise’s anti-risk

ability and profitability based on the internal factors

of the production enterprise and price modification.

The factor has the characteristics of non-linear and

time-varying, which can simulate the dynamic

characteristics of the supply system, and the

consideration factors are more systematic, more

objective, more flexible and the calculation cost is

also smaller.

6 CONCLUSIONS

After the above discussion, we gave a raw material

ordering and transshipment formulation strategy and

evaluation index based on common mathematical

modeling methods for manufacturers, and introduced

the concept of the price correction factor, and gave the

calculation of each component formula, practical

meaning and synthesis method. It should be noted that

considering the efficiency of model solving, we made

tougher assumptions and constraints. One is that we

assume the order price of raw materials is constant,

and completely ignores the influence of external

factors on the price of raw materials., which has a

certain degree of time-varying characteristics. When

considering the influence of these factors, it is

necessary to define another influence factor, and take

a part of this factor and the time-varying component

of the price correction factor, which will increase the

complexity of the model. Due to the limitation of

data, this article did not do any spatial domain

analysis of the entire model and did not consider the

delay of a series of actions among suppliers,

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forwarders and manufacturing enterprise. To a certain

extent, this also weakens the rigor of a series of

parameters such as average response time,

replenishment response cycle and time-varying

components. Empirical analysis points out that the

model solution results can meet actual operating

needs of the production enterprise.

With the development of social economy and the

change of social thinking, the roles between

enterprises and governments are constantly changing.

Enterprises must have sufficient time and space to

reduce costs, improve their own risk resistance and

capacity improvement potential, so as to achieve

transformation and upgrading and improve their own

operational capabilities. In this process, the

government must provide a policy environment that

is in line with enterprise development so as to

consolidate the results of enterprise development.

Enterprises play a good role in fulfilling individual

economic responsibilities in their development. The

two-way interaction between the government

governance and enterprises forms a social

environment of good governance.

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Research on the Enterprise Raw Material Ordering and Transshipment Problems based on the Dual-objective Planning Model Taking a

Building Materials Enterprise as an Example