AN ORDER ALLOCATION MODEL IN VIRTUAL ENTERPRISES

BASED ON INDUSTRIAL CLUSTERS

Fangqi Cheng, Feifan Ye

School of Mechanical Engineering, Shanghai JiaoTong University, 800 Dongchuan Street, Shanghai, P. R. China

Faculty of Engineering, Ningbo University, 818 Fenghua Street, NingBo, P.R. China

Jianguo Yang

School of Mechanical Engineering, Shanghai JiaoTong University, 800 Dongchuan Street, Shanghai, P. R. China

Keywords: Industrial Cluster, Virtual Enterprise, Order Allocation, Evolutionary Programming, Partner Selection.

Abstract: To build virtual enterprise based on an industrial cluster is one of the most important ways to improve the

agility and competitiveness of manufacturing enterprises. One of the key factors towards the success of

virtual enterprises is the correct selection of cooperative partners. The approach proposed for order

allocation and partner selection is composed of two stages: task-resource matching and quantitative

evaluation. In the first stage the potential candidates are identified and in the second stage evolutionary

programming is applied. The architecture for information evaluation and order allocation is studied for the

proposed approach. The target function, in which the load rate of candidate enterprise is taken as the main

variable, is developed, and a simplified example is used to verify the feasibility of the proposed approach.

The result suggests that the proposed model and the algorithm obtain satisfactory solutions.

1 INTRODUCTIOIN

The manufacturing industry in the 21st century is

faced with a rapidly changing market demands and

global competition. Customers require that

manufacturing companies make more products with

high quality, low cost, quick delivery, short lead

time, perfect service, as well as the personalization

of the products. From the early 90s of last century,

agile manufacturing has become a popular

philosophy and an important enabling tool.

Partner selection and order allocation are very

important problems in virtual enterprise. Early

literatures on partner selection have been mainly

qualitative and focused on methodological aspects.

Then quantitative methods are employed to study

partner selection problem. Tulluri and Baker (Talluri

S and Baker R.C, 1996) proposed a two-phase

mathematical programming approach for partner

selection by designing a virtual enterprise where the

factors of cost, time and distance were considered.

Zhao Fuqing (Zhao Fuqing et al, 2005) proposed a

multi-objective optimization model and took into

account the factors of cost, due date, and the risk of

failure at the same time. Order allocation problem in

virtual enterprise is attracting increasing attention in

recent years. A. Hammami proposed a method to

calculate satisfying routes for customers’ orders

within manufacturing networks of small-medium

enterprises (A. Hammami et al, 2003). Ruengsak

Kawtummachai et al constructed an algorithm to

deal with order allocation and minimize the total

purchasing cost in a supply chain (Ruengsak

Kawtummachai et al, 2005).

The above literatures studied partner selection and

order allocation separately. However, order

allocation is sometime coupled with partner

selection in a virtual enterprise. Moreover,

production load rate of each entity in virtual

enterprise is one of most important factors for

partner selection and order allocation problems.

Industrial clusters are generally defined

as geographic concentration of interconnected

companies and institutions in particular business

field according to the relationship of specialization

445

Cheng F., Ye F. and Yang J. (2007).

AN ORDER ALLOCATION MODEL IN VIRTUAL ENTERPRISES BASED ON INDUSTRIAL CLUSTERS.

In Proceedings of the Ninth International Conference on Enterprise Information Systems - DISI, pages 445-448

DOI: 10.5220/0002350304450448

 SciTePress

and collaboration. In China, there are many typical

industrial clusters, such as plastics injection machine

industry in Ningbo, shoe making companies in

Wenzhou, Zhejiang province, and so on.

2 ORDER ALLOCATION

2.1 Problem Description

When one of the companies in an industrial cluster

gains an order, and if it is too large for its own

capacity, a good way for this company (dominant

company) is to allocate order among a group of

manufacturing companies in the cluster. The

dominant company needs to review the core

competencies and the production load of the

candidates in the cluster and decide how to allocate

the order. An order allocation oriented to the

horizontal virtual enterprise model based on

evolutionary programming is proposed according to

the above two aspects of information in this paper.

This approach consists of two stages:

task-resource matching and quantitative evaluation.

In task-resource matching stage the dominant

company search for the candidates according to the

product’s manufacturing features and the candidates’

resources information. In the light of the parameters

in the model, evolutionary programming is applied

to optimize partner selection and order allocation in

the second stage. Figure 1 illustrates the relationship

of two stages.

2.2 Task-Resource Matching

Task-resource matching means that products’

features should match companies’ manufacturing

capacities. By using definite search algorithm, all the

potential companies that are qualified for the

manufacturing tasks will be found. For achieving the

above procedure, companies’ manufacturing

resources database should be built to improve the

efficiency and effectiveness of the search and match.

The requirements of search and match are that the

manufacturing capacities of companies’ combination

cover the requirements of collaboration tasks.

2.3 Quantitative Evaluation

To identify the proper partners and assign

corresponding volume of orders is the key task of

the second stage. An evolutionary programming

method is used to gain the results.

The problem can be described as follows. There are

n candidates in an industrial cluster, and the target

function and the constraints are modeled as follows:

infM max{

n21

T,,T,T " } •

Cnum

∑

(1)

.t.s =

∑

xnum 100

(2)

))1(CP/(numT

iii

ρ−•=

(3)

Where:

n,,2,1i "

;

T is the lead time of candidate

i ;

num is the volume of products corresponding

candidate

i ;

C is the cost of each product in

candidate

i ;

x is the candidate which is selected to

combine into the virtual enterprise;

CP is the

competences of candidate

i ;

ρ is the production

load rate of candidate

i .

For the evolutionary programming, the natural

Figure 1: An outline of the approach for optimal model.

ICEIS 2007 - International Conference on Enterprise Information Systems

446

number string is selected as the gene description.

Let

}w,,w,w{w

n21

"= , where

w is a natural

number between 0 and

m . That stands for the

number of volume of orders corresponding

candidate

i .

2.4 System Architecture

Based on the order allocation oriented to the

horizontal virtual enterprise model mentioned above,

the system architecture was proposed for

information evaluation and order allocation system

(IEOAS) as shown in Figure 2, which contains five

function modules, and a knowledge base and

partners’ databases.

Figure 2: Architecture of IEOAS.

3 NUMERICAL EXAMPLE

To illustrate the approach presented in this paper, a

simplified example by using evolutionary

programming is described below.

There are the parameters of the numerical example.

Six candidates and the corresponding parameters

were given shown in Table 1.

Table 1: Performance index of each candidate.

CCN

Items

1 2 3 4 5 6

0.75 0.35 0.98 1.05 2.00 1.56

10 30 24 37 9 45

0.30 0.80 0.75 0.65 0.12 0.97

Note: CCN: candidates code number in Table 1.

The total number of orders is 100 units;

The population size is 20;

The condition to end iterative process is 10000.

The evolutionary programming can obtain the

second-optimal solution and cost less time than

many other programming methods such as integer or

mixed integer programming if the size of problem is

very large. The target function converges 0.8981

when the iterative number is 1930, and the quantum

of each candidate is allocated. The result is shown in

Figure 3.

Figure 3: Target function value change curve.

Since our optimal target is the lead time and cost, the

lead time of each enterprise is almost equal shown as

Table 2. From comparing the lead time of each

candidate, it is evident that the deviation among the

candidates is very small. The biggest value is 2.57

and the smallest value is 2.02.

Table 2: The results of order & Time.

Items 1 2 3 4 5 6

0.30 0.80 0.75 0.65 0.12 0.97

num

18 15 15 33 16 3

2.57 2.50 2.50 2.55 2.02 2.22

AN ORDER ALLOCATION MODEL IN VIRTUAL ENTERPRISES BASED ON INDUSTRIAL CLUSTERS

447

4 CONCLUSIONS

In order to improve the agility and competitiveness

of manufacturing companies with similar products in

industrial clusters, horizontal virtual enterprises are

suggested to be an effective enabler. During the

development and operation of the virtual enterprises

based on industrial clusters, a two-stage approach is

employed, in which there are a task-resources

matching and a quantitative evaluation stages. In the

second stage, evolutionary programming is applied.

Meanwhile, the architecture of network system is

studied in relation with the algorithm. The study

shows that production load rate should be considered

as a very important factor in partner selection and

order allocation to achieve the equilibrium of

production load in all the involved enterprises. Then

the overall improvement of agility and

competitiveness can be obtained within the

enterprises.

ACKNOWLEDGEMENTS

This paper is based upon work funded by Zhejiang

Provincial Natural Foundation of China under Grant

No. Z604342

REFERENCES

A. Hammami, P. Burlat, J.P. Campagne, 2003. Evaluating

orders allocation within networks of firms.

International Journal of Production Economics. vol. 86,

pp. 233-249.

Ruengsak Kawtummachai, Nguyen Van Hop, 2005. Order

allocation in a multiple-supplier environment.

International Journal of Production Economics. 93-94,

pp. 231–238.

Talluri S, Baker R.C., 1996. Quantitative framework for

designing efficient business process alliances. In:

Proceedings of the 1996 International Conference on

Engineering and Technology Management.

Piscataway. pp. 656–661.

Zhao Fuqing, Hong Yi, Yu Dongmei, 2005. A

multi-objective optimization model of the partner

selection problem in a virtual enterprise and its

solution with genetic algorithms. International Journal

of Advanced Manufacturing Technology.

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