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An agent-based framework for supply chain coordination in construction Xiaolong Xue a , Xiaodong Li a , Qiping Shen b, * , Yaowu Wang a a School of Management, Harbin Institute of Technology, PR China b Department of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong Received 1 February 2004; received in revised form 1 July 2004; accepted 1 August 2004 Abstract Supply chain coordination has become a critical success factor for supply chain management (SCM) and effectively improving the performance of organizations in various industries. Coordination refers to the integration of different parts of an organization or different organizations in supply chain to accomplish a collective set of tasks and to achieve mutual benefits. This paper defines the concepts of construction supply chain (CSC) and construction supply chain management, especially regards construction supply chain management as the coordination of interorganizations decision making in construction supply chain and the integration of key construction business processes and key members involved in construction supply chain. Much research and practice indicate that there still are many problems in construction, most of which are supply chain problems. The research analyzes the problems in construction supply chain. In order to resolve these problems and improving the performance of construction, an agent-based framework for construction supply chain coordination is designed based on the agent technology and multiattribute negotiation and multiattribute utility theory (MAUT). The framework, which integrates the construction organizations in construction supply chain and multiattribute negotiation model into a multiagent system (MAS), provides a solution for supply chain coordination in construction through multiattribute negotiation mechanism on the Internet. Finally, the prototype of the framework is developed and tentatively run based on an imaginary construction project. The trial run reveals the feasibility to implement the agent-based framework for coordination in construction. D 2004 Elsevier B.V. All rights reserved. Keywords: Construction; Coordination and management; Intelligent agent; Multiattribute negotiation; Multiagent systems; Supply chain 1. Introduction In recent years, the application of supply chain management (SCM) philosophy to the construction industry has been widely investigated as an effective and efficient management measure and strategy to improving the performance of construction, which has suffered from high fragmentation, large waste, poor productivity, cost and time overruns, and conflicts and disputes for a long time [1–3], and to address adversarial interorganizational relationship of organ- 0926-5805/$ - see front matter D 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.autcon.2004.08.010 * Corresponding author. E-mail address: [email protected] (Q. Shen). Automation in Construction 14 (2005) 413 – 430 www.elsevier.com/locate/autcon

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Page 1: An agent-based framework for supply chain coordination in construction

www.elsevier.com/locate/autcon

Automation in Constructio

An agent-based framework for supply chain

coordination in construction

Xiaolong Xuea, Xiaodong Lia, Qiping Shenb,*, Yaowu Wanga

aSchool of Management, Harbin Institute of Technology, PR ChinabDepartment of Building and Real Estate, The Hong Kong Polytechnic University, Hong Kong

Received 1 February 2004; received in revised form 1 July 2004; accepted 1 August 2004

Abstract

Supply chain coordination has become a critical success factor for supply chain management (SCM) and effectively

improving the performance of organizations in various industries. Coordination refers to the integration of different parts of an

organization or different organizations in supply chain to accomplish a collective set of tasks and to achieve mutual benefits.

This paper defines the concepts of construction supply chain (CSC) and construction supply chain management, especially

regards construction supply chain management as the coordination of interorganizations decision making in construction supply

chain and the integration of key construction business processes and key members involved in construction supply chain. Much

research and practice indicate that there still are many problems in construction, most of which are supply chain problems. The

research analyzes the problems in construction supply chain. In order to resolve these problems and improving the performance

of construction, an agent-based framework for construction supply chain coordination is designed based on the agent

technology and multiattribute negotiation and multiattribute utility theory (MAUT). The framework, which integrates the

construction organizations in construction supply chain and multiattribute negotiation model into a multiagent system (MAS),

provides a solution for supply chain coordination in construction through multiattribute negotiation mechanism on the Internet.

Finally, the prototype of the framework is developed and tentatively run based on an imaginary construction project. The trial

run reveals the feasibility to implement the agent-based framework for coordination in construction.

D 2004 Elsevier B.V. All rights reserved.

Keywords: Construction; Coordination and management; Intelligent agent; Multiattribute negotiation; Multiagent systems; Supply chain

1. Introduction

In recent years, the application of supply chain

management (SCM) philosophy to the construction

0926-5805/$ - see front matter D 2004 Elsevier B.V. All rights reserved.

doi:10.1016/j.autcon.2004.08.010

* Corresponding author.

E-mail address: [email protected] (Q. Shen).

industry has been widely investigated as an effective

and efficient management measure and strategy to

improving the performance of construction, which has

suffered from high fragmentation, large waste, poor

productivity, cost and time overruns, and conflicts and

disputes for a long time [1–3], and to address

adversarial interorganizational relationship of organ-

n 14 (2005) 413–430

Page 2: An agent-based framework for supply chain coordination in construction

X. Xue et al. / Automation in Construction 14 (2005) 413–430414

ization by increasing number of construction organ-

izations and researchers [1,4–12]. SCM can be

considered as the coordination of distributed decision

making of organizations or participants on material

flow, information flow, human flow, and cash flow in

supply chain from systems perspective.

According to Lau et al. (2004) [48], SCM is

defined as bcoordination of independent enterprises in

order to improve the performance of the whole supply

chain by considering their individual needsQ. This

definition describes the main function and principle of

SCM, i.e., coordination. Swaminathan and Tayur [13]

classify SCM issues into two broad categories:

configuration (design-oriented) issue that relates to

the basic infrastructure on which the supply chain

executes, and coordination (execution-oriented) issues

that relate to the actual execution of supply chain.

Schneeweiss and Zimmer [14] also regard SCM as a

management activity that has to do with the coordi-

nation of logistic process being locally controlled by

various independent organizations (decision-making

units) in the environment of internationalization and

globalization of markets together with an increased

focus on organizations’ core competence. Up to now,

supply chain coordination has become a very popular

research topic among the research community in SCM

and a vital management issue of organizations in the

collaborative–competitive business environment.

Coordination is bmanaging the dependencies

between activitiesQ [15]. It is defined as a mutually

beneficial and well-defined relationship entered into

by two or more organizations to achieve common

goals. It also refers to the integration of different parts

of an organization or different organizations in supply

chain to accomplish a collective set of tasks and to

achieve mutual benefits. It involves more formal

relationships, objectives and actions which are

mutual, compatible and common, not necessary a

centralized authority [16].

Multiagent systems (MAS) technology offers new

means and tools for supply chain coordination [17,18].

According to Wooldridge and Jennings (1995) [49], an

agent is a self-contained program capable of control-

ling its own decision making and acting based on its

perception of its environment, in order to one or more

goals. An agent must possess any two of the following

three behavioral attributes: autonomy, cooperation, and

learning [19]. MAS comprises a number of intelligent

agents, which represents the real world parties and co-

operate to reach the desired objectives. In MAS, each

agent attempts to maximize its own utility while

cooperating with other agents to achieve their goals

[20]. The main advantage of MAS is its responsibilities

for acting various components of the engineering

process or participants of the business process which

is delegated to a number of agents. MAS is suitable for

domains that involve interactions between different

organizations with different objectives and proprietary

information [21]. Based on these discussions, we can

clearly see that SCM system is a typical MAS, where

the participants are delegated to different agents.

Furthermore, agent-based supply chain coordination

has been proved to be an effective mechanism to

improve the performance of SCM [22–24].

The core principles of SCM and agent technology

provide new perspectives for construction supply chain

(CSC) management. However, little research has been

conducted to investigate the application of intelligent

agent to coordination problems in CSC. The Centre for

Integrated Facility Engineering of Stanford University

established a distribution cooperative CAD environ-

ment entitled AgentCAD, which presented a frame-

work for collaborative distributed facility engineering

[25]. Anumba et al. [26] presented the key features of

an agent-based system for collaborative design of

portal frame structures and made a significant contri-

bution by allowing for peer to peer negotiation between

design agents. Pena-Mora and Wang [27] proposed a

collaborative negotiation methodology and a computer

agent named CONVINCER, which incorporates that

methodology to facilitate or mediate the negotiation of

conflicts in large-scale civil engineering projects. Min

and Bjornsson [28] presented a conceptual model of

agent-based supply chain automation, in which a

project agent gathers actual construction progress

information and sends to subcontractor agents and

supplier agents, respectively, over the Internet. They

evaluated an agent-based SCM model compared with

traditional SCM practice through simulation. Ren et al.

[29] developed multiagent system for construction

claims negotiation (MASCOT) to resolve inefficiency

problems. Kim and Paulson [30] presented an agent-

based compensatory negotiation methodology to facil-

itate the distributed coordination of project schedule

changes wherein a project can be rescheduled dynam-

ically through negotiation by all of the concerned

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X. Xue et al. / Automation in Construction 14 (2005) 413–430 415

subcontractors. The methodology consists of a com-

pensatory negotiation strategy based on utility of

timing, multilinked negotiation protocols, and mes-

sage-handling mechanisms.

Whereas these researchers have addressed a

number of key issues in the field of CSC, very few

researchers have considered CSC management as the

coordination of interorganizations’ decision making in

CSC and apply the agent technology into the whole

CSC management from a systemic perspective. Most

research projects tend to look into agent-based

internal coordination of an organization, which

focuses on one of the stages in CSC, such as design

coordination, project schedule changes coordination,

and construction claims and conflicts resolving. These

approaches may lead to the optimization of a number

of subsystems; however, the sum of the optimized

subsystems may present a problem to the optimization

of the system as a whole.

This paper presents an agent-based framework for

supply chain coordination in construction (ABS3C)

based on multiattribute negotiation and utility theory,

which integrates design and construction, and organ-

izations (or participants), including owner, designer,

general contractor (GC), subcontractors, and suppliers

in CSC. This integrated coordination framework

extends the internal supply chain of general contractor

to external supply chain of designer, subcontractors,

and suppliers. In the decision-making process of

participants in CSC, the factors: cost, time, quality,

safety, and environment normally are considered as

the main decision-making variables. Here, we regard

the five factors as five attributes of decision making in

the construction process. An agent-based multiattri-

bute negotiation mechanism is designed for support-

ing the realization of the framework. The research

methodology is described. The concepts and problems

of CSC are defined and discussed. A prototype system

of the agent-based framework for supply chain

coordination in construction has been evaluated

through the use of a hypothetical construction project

and a toolkit called ZEUS.

2. Theoretical foundation

According to Pena-Mora and Wang [27], negotia-

tion theory is the study of exchanges between

participants to reconcile their differences and produce

a settlement. Negotiation is defined as one kind of

decision-making process where two or more partic-

ipants jointly search for a space of solution with the

goal of achieving consensus [31]. From this defini-

tion, it can be found that negotiation is an effective

mechanism for supply chain coordination in con-

struction. A successful negotiation needs a number of

elements in the negotiation process. For example, the

participants have a commitment to settle the matter at

hand; good communication skills are required; there is

an agreement between participants on what is actually

the matter under negotiation. There are three out-

comes of negotiation: win/win, win/lose, and lose/

lose. The win/win outcome meets the needs of both

participants [32]. One of the objectives of our research

is to achieve the win/win outcome.

Since a number of factors, such as cost, time,

quality, safety, and environment, must be considered

in the decision-making process of CSC management,

our research adopts the multiattribute negotiation

technology to coordinate the CSC. These factors are

regarded as the attributes involved in CSC decision

making. The multiattribute negotiation technology is

developed based on the multiattribute utility theory

(MAUT), which is an analytical tool for making

decisions involving multiple interdependent objec-

tives based on uncertainty and utility analyses [33]

and an evaluation scheme for estimating various

products and performance [34,35]. This research

adopts MAUT to evaluate CSC decision-making

problems. The procedure of MAUT application to

supply chain coordination in construction is shown in

Fig. 1 (adapted from Ref. [36]), where a decision is

one of a set of possible solutions to the supply chain

coordination problems in construction and an attribute

is one of a set of factors that will influence the CSC

decision-making process. The application of MAUT

will be discussed in the subsequent sections in detail.

According to Gupta et al. [17], agent technology

provides a convenient tool to enhance communication

and coordination in a distributed system to ensure

efficient decision making. Kwon and Lee [22] also

believe that MAS, where multiple agents work

collectively to solve specific interorganization prob-

lems, provides an effective platform for coordination

across organizations in the supply chain. This research

adopts multiagent technology to improve the coordi-

Page 4: An agent-based framework for supply chain coordination in construction

Fig. 1. Procedure of using MAUT in supply chain coordination in construction.

X. Xue et al. / Automation in Construction 14 (2005) 413–430416

nation of CSC decision making. According to the

definition of MAS, CSC management system is a

typical MAS. In this kind of MAS, the partners, i.e.,

general contractor (GC), owner, designer, subcontrac-

tors, and suppliers are delegated to corresponding

agents, i.e., GC agent, owner agent, designer agent,

subcontractor agents, and supplier agents. These agents

are designed and realized in ZEUS, an advanced

developing environment for distributed multiagent

systems and has been used to develop agent-based

coordination systems in construction [26,29,37,38].

Multiagent technology is integrated to negotiation and

multiattribute utility theory in the research. Each agent

possessing own preferences and utilities autonomously

negotiates to other agents. Thus, agents can act

collectively and efficiently as a society and cooperate

to achieve their own goals as well as the common goals

of the whole CSC management.

3. Concepts and problems of CSC

3.1. The concepts of CSC

CSC consists of all construction processes, from

the initial demands by the client/owner, through

design and construction, to maintenance, replacement

and eventual demolition of the projects. It also

consists of organizations involved in the construction

process, such as client/owner, designer, GC, subcon-

tractor, and suppliers. CSC is not only a chain of

construction businesses with business-to-business

relationships but also a network of multiple organ-

izations and relationships, which includes the flow of

information, the flow of materials, services or

products, and the flow of funds between owner,

designer, GC, subcontractors, and suppliers [8,9,39–

41]. According to Muya et al. [42], there are three

types of CSC: the primary supply chain, which

delivers the materials that are incorporated into the

final construction products; the support chain, which

provides equipment and materials that facilitate

construction, and the human resource supply chain

which involves the supply of labor. In this paper, CSC

is considered from the stage of owner demands to the

stages of design, construction, and handover. A

typical model of CSC is shown in Fig. 2. In the

model of CSC, GC is the core of the CSC. And the

owner and designer are the other two main partners in

CSC. Excepting the direct suppliers of GC, subcon-

tractors are also regarded as the suppliers of GC;

meanwhile, subcontractors have their own suppliers.

Although a number of researchers have provided

definitions for CSC management (e.g., Refs. [12,43]),

for consistency, this research defines CSC manage-

ment as follows: CSC management is the coordination

of interorganizations’ decision making in CSC and the

integration of key construction business processes and

key members involved in CSC, including client/

owner, designer, GC, subcontractors, suppliers, etc.

CSC management focuses on how firms utilize their

suppliers’ processes, technology and capability to

enhance competitive advantage. It is a management

philosophy that extends traditional intra-enterprise

activities by bringing partners together with the

common goal of optimization and efficiency. CSC

management emphasizes on long-term, win/win, and

cooperative relationships between stakeholders in

systemic perspective. Its ultimate goal is to improve

construction performance and add client value at less

cost.

We have identified eight key construction busi-

ness processes that are implemented within the CSC

across organizational boundaries. They are: project

management, client service management, supplier

relationship management, demand management,

order fulfillment, construction flow management,

environment management, and research and develop-

ment. Fig. 3 presents a schematic view of CSC

management.

Page 5: An agent-based framework for supply chain coordination in construction

Fig. 2. Model of construction supply chain.

X. Xue et al. / Automation in Construction 14 (2005) 413–430 417

3.2. Problems in CSC

Although there have been many changes in the

construction industry as a result of the development of

technology and culture over the last decades, CSCs do

not seem to have changed much. Many problems still

exist in CSC. According to Ref. [40], the major

Fig. 3. Problems in CSC (ad

problems originate at the interfaces of different

participants or stages involved in the CSC, as shown

in Fig. 3. The problems are caused by myopic and

independent control of the CSC.

Love et al. [12] and Mohamed [44] noted the

highly fragmented characteristics of the construction

industry. For example, the separation of design and

apted from Ref. [40]).

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X. Xue et al. / Automation in Construction 14 (2005) 413–430418

construction, lack of coordination and integration

between various functional disciplines, poor commu-

nication, etc., are the important impact factors causing

performance-related problems, such as low produc-

tivity, cost and time overrun, conflicts, and disputes.

Palaneeswaran et al. [11] revealed the weak links in

CSC as follows:

! Adversarial relationships between clients and

contractors;

! Inadequate recognition of the sharing of risks and

benefits;

! Fragmented approaches;

! Narrow minded bwin/loseQ attitudes and short-termfocus;

! Power domination and frequent contractual non-

commitments resulting in adverse performance

track records with poor quality, conflicts, disputes,

and claims;

! Prime focus on bid prices (with inadequate focus

on life-cycle costs and ultimate value);

! Less transparency coupled with inadequate infor-

mation exchanges and limited communications;

! Minimal or no direct interactions that foster

sustainable long-term relationships.

In order to overcome the shortcomings (weak

links) of CSC and resolve the problems in CSC, and

to further improve the performance of the whole CSC,

this research presents a solution which integrates the

agent technology and multiattribute negotiation tech-

nology. This solution will be explained in details in

the following sections.

4. Design of agent-based framework for supply

chain coordination

4.1. Integrated design

CSC involves multiple agents that delegate

organizations to autonomously perform tasks

through exchanging information. It has increasing

number of participants due to the increasing scale

and complexity of construction projects. As a

result, the coordination among the participants

becomes a challenge that is vital to the perform-

ance of CSC and the value to the client. These

require an agent-based framework which has an

appropriate structure and effective coordination

mechanism to promote efficient communication

among all parties. The framework should also be

stable, flexible, and user-friendly. Based on these

considerations, we have designed the framework as

shown in Fig. 4.

In this framework, the domain agents include both

dserviceT agents (i.e., coordinator agent, monitor

agent, and name server agent) and dspecialtyT agents(i.e., owner agent, design agent, GC agent, subcon-

tractor agents, and supplier agents). The suppliers

include both GC’s suppliers and subcontractors’

suppliers. Here, we assume that all materials and

human resources are arranged by GC or subcontrac-

tors and there are no owner’s suppliers in this

framework. All agents communicate and cooperate

through the Internet.

All specialty agents advertise their abilities, knowl-

edge, and preferences in the acquaintance database

maintained by of the coordinator agent, and their

addresses in the address book maintained by the name

server agent. The specialty agents use the coordinator

agent to identify agents with the required abilities,

knowledge, and preferences. They also use the name

server agent to determine the addresses of the

identified agents.

The coordinator agent has a mailbox and message

handler for receiving and responding to queries from

agents about the abilities and preferences of other

agents, and an acquaintance database for storing the

abilities and prefernces of the agents. It functions by

periodically querying all the agents in the society

about their abilities and preferences, and storing the

returned information in its acquaintance database. The

monitor agent is used to view, analyse societies of

agents. It functions by querying other agents about its

states and processes, and then collating and interpret-

ing the replies to create an up-to-date model of the

agents’ collective behavior. This model can be viewed

from different perspectives through visualization

tools. The name server agent provides a look-up

service for agents’ addresses and has a mailbox and

message handler, the component needed for receiving

and responding to agents’ requests for the addresses of

other agents.

The specialty agents, which are independent and

autonomous in making decisions, are designed to

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Fig. 4. An agent-based framework for supply chain coordination in construction.

X. Xue et al. / Automation in Construction 14 (2005) 413–430 419

delegate corresponding behaviors of CSC partici-

pants. The GC agent stands for GC to perform its

responsibilities, including cost control, schedule,

quality, safety, and environment performance of the

project through coordination with subcontrctors,

suppliers, owner, and designers. The subcontractor

agents simulate the decision-making process of

subcontractors to negotiate with GC and their

suppliers. The owner agent tracks the real schedule,

cost, and quality of the project and duly send the

information of design changes (new demands) to the

designer agents and the GC agent. The designer

agents will respond to the demands and send

information and design change drawings to the

owner and the GC. The GC agent reschedules the

project, makes new decisions, and sends the infor-

mation to subcontractor agents, supplier agents, and

owner agent. In the framework, all coordination

processes are based on multiattribute negotiation

(bM-NegotiationQ as shown in Fig. 4) between

sepcialty agents. According to Andreas [45], agent-

based automated negotiation provides an effective

mechanism to coordinate the decision-making activ-

ities in supply chain. The multiattribute negotiation

mechanism for coordinating the interorganizations’

decsion making in CSC is the significant compo-

nents of ABS3C. The following section describes the

design process of the multiattribute negotiation

mechanism in detail.

4.2. Multiattribute negotiation model

Although Kim and Paulson [30] present an agent-

based compensatory methodology to improve the

coordination of project schedule changes, it only

considers the utility of timing and cost and is not

suitable for the coordination of decision making in

CSC. We present an agent-based multiattribute

negotiation model for supply chain coordination in

construction, which creatively extends the general

negotiation model for A/E/C [27] from SCM and

utility theory perspectives and integrates the composi-

tional multiattribute negotiation model [46], as shown

in Fig. 5. The model consists of three elements: CSC

participants, multiattribute negotiation process, and

the outcome.

Each of these elements plays a role in a generic

coordination problem within the domain. All partic-

ipants reveal their attributes and preferences in the

negotiation process with due regard to their parent

organizations and relationships to the issues. Here,

each of the CSC participants is represented by a

corresponding agent in the framework. The outcome

will be the negotiation results determined by the

Page 8: An agent-based framework for supply chain coordination in construction

Fig. 5. Multiattribute negotiation model for supply chain coordination in construction.

X. Xue et al. / Automation in Construction 14 (2005) 413–430420

interactive process between participants in CSC. The

multiattribute negotiation process is the interactive

exchange and elicitation of participants’ preferences.

The following section will focus on the negotiation

process.

4.3. Multiattribute negotiation process

According to Jonker and Treur [46], the multi-

attribute negotiation process includes the following

five steps:

(1) Evaluation of the attributes of the initial sol-

utions made by the participants;

(2) These evaluations are aggregated into overall

utilities of these initial solutions;

(3) Provision of the target utility;

(4) Based on the target utility and the distribution of

attributes, the values of the target attributes are

determined, which lead to a new round of

decision making;

(5) For each of the target attributes, an attribute

value is chosen that has an evaluation value as

close as possible to the target evaluation value

for the attribute.

In the agent-based multiattribute negotiation

model, these five steps are simplified to three

processes: attributes evaluation, utility determination,

and attribute planning.

4.3.1. Attributes evaluation

As shown in Fig. 5, many attributes are involved in

the process of supply chain coordination in construc-

tion. Attributes evaluation is a process whereby the

value of the attributes are evaluated, based on the

preferences of the participants in the CSC. All

attributes are classified into two categories: quantita-

tive and qualitative. For the quantitative attributes,

such as cost and time, the value of these attributes can

be directly calculated according to certain principles.

For the qualitative attributes, such as quality, safety,

Page 9: An agent-based framework for supply chain coordination in construction

Fig. 6. Multiattribute negotiation protocol.

X. Xue et al. / Automation in Construction 14 (2005) 413–430 421

and environment, it is necessary to construct a scale to

measure the levels of these attributes. In this paper, a

scale, from 0 (worst) to 10 (best), serves as the

measure of the evaluation. The most frequently used

five levels are: 1—worse, 3—bad, 5—normal, 7—

good, and 9—better. When a compromise is needed,

0, 2, 4, 6, 8, 10 are intermediate values between the

two adjacent judgments.

After evaluating the attributes in a table form, an

evaluation matrix that includes m participants and n

attributes is obtained:

X ¼ xij� �

m�n

where xij is the value of the jth attribute evaluated by

the ith participant in a decision-making process.

Next, the matrix X is translated into a unitary

normalization matrix of vector:

Y ¼ yij� �

m�n

where 0VyijV1, and

yij ¼ xij

, ffiffiffiffiffiffiffiffiffiffiffiffiffiXmi¼1

x2ij

s

4.3.2. Utility determination

In this process, the target utility is determined. The

utility of the ith participant’s decision making (Ui) is

given by

Ui ¼Xnj¼1

wjyij

Where the wj is the weighting of the jth attributes:

wj ¼Xnj¼1

akj

� Xnk¼1

Xnj¼1

akj

WherePn

j¼1 wj ¼ 1 and akj is the value of relative

weightiness between kth attribute and jth attribute.

The value of akj is given by

akj ¼1 ; if kth attribute is more important than jth attribute

0:5 ; if kth attribute is important as jth attribute

0 ; if kth attribute is less important than jth attribute

8<:

Jonker and Treur [46] presented the formula of target

utility (TU) of participant’s decision making as

follows:

TU ¼ UBOW þ CS

where UBOW is the utility of the own decision making,

and the concession step (CS) is determined by

CS ¼ b 1� l=UBOWð Þ UBOT=UBOWð Þ

where UBOT is the utility of the other participant’s

decision making. The factor (1�l/UBOW) expresses

CS will decrease to 0 if the UBOW approximates the

minimal utility l and (UBOT�UBOW) expresses the

current utility gap. b stands for the negotiation speed.

4.3.3. Attribute planning

The attribute planning process refers to target

evaluation and configuration determination [46]. The

target evaluation of jth attribute TEj is given by

TEj ¼ 1� sð ÞBTEj þ sEBOT; j

where BTEj is the basic target evaluation of jth

attribute, which is determined in such away that

AwjBTEj=TU. EBOT, j is the jth attribute evaluation

value of other agent. s stands for the configuration

tolerance. BTEj is determined as the below format:

BTEj ¼ EBOW; j þ aj=N� �

TU� UBOWð Þ

where EBOW,j is the jth attribute evaluation value of

decision agent, N is a normalization factor, which is

the weighted sum of a ’s with the relative importance

factors being the weightings: N=P

wjaj,. aj=(1�wj)

(1�EBOW, j).

The configuration determination for the next

decision making includes three steps. Firstly, attribute

values are determined with an evaluation that is as

close as possible to the target evaluation value.

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Fig. 7. Roles represented in ABS3C.

X. Xue et al. / Automation in Construction 14 (2005) 413–430422

Secondly, a partial configuration (excepting the

quantitative attributes, such as cost and time) is

selected from the closest value of attribute. The final

step is to reevaluate the quantitative attributes.

4.4. Multiattribute negotiation protocol

Negotiation protocol controls the interactions

among agents by constraining the way the agents

interact [30]. It also specifies the kinds of deals that

the agents can make, as well as the sequence of offers

and counteroffers that are allowed [29]. In our

framework, multiple attributes are considered in the

process of negotiation between agents. For example,

GC agent needs to negotiate with subcontractor

agents, supplier agents, designer agents, and owner

agent regarding different attributes of solutions, such

as time, cost, safety, and quality, based on the overall

utility. The negotiation protocol is named as multi-

Fig. 8. Interactions of a

attribute negotiation protocol in this paper, which is

shown in Fig. 6.

Each agent offers its current best solution that is

also saved locally. Then, the solution is sent to

other agents. The sender waits for a message from

other agents. If the message is acceptance of the

offer, it indicates that the sent solution is consistent

with the other agents’ solution, and the negotiation

is successful. If the message is NoMoreSolution, it

means that the other agent has run out of solutions.

If the same is true for the initiating agent as well,

then the negotiation ends unsuccessfully; otherwise,

the agent will continue to generate new solutions. If

the message includes a changed solution from the

other agent, this solution is checked for compati-

bility with any of the past solutions generated by

this agent. If an intersection is found, it presents a

mutually acceptable solution, which determines the

negotiation successfully. Otherwise, this indicates

gents in ABS3C.

Page 11: An agent-based framework for supply chain coordination in construction

Table 1

Summary of Interactions

Collaboration Explanation

1 Registration Agents notify the NS of their

presence

2a Resolve query A request for the network

location of a named agent

2b List query A request for all agents of a

particular type

3a Location response The location of the agent

previously in question

3b List response The list of agents previously

in question

4a Answer agent name NS answers the inquiries

about the agent name

4b Inquire agent name Agent inquires from NS

about the other agent name it

needs to communicate

with them

5a Ability and preference

response

Information about an agent’s

current abilities and

preference

5b Ability and preference

request

Ask for information about

recipient’s abilities and

preference

6 Information request Asks all activity be forwarded

7 Activity notification A copy of ant message sent

8 Find request Asks for agents with

particular abilities and

preference

6 Find response A list of agents matching the

desired criteria

10 Inquire A agent inquire from

facilitator about the other

agent’s abilities and

preference

11 Answer The facilitator answers the

inquires from agents

12 Negotiate with

Subcontractors

GC negotiates the relative

construction solutions with

Subcontractors

13 Sent solution (decision

making)

Subcontractors send their

decision making to GC

14 Send demands information GC sends its demands plan to

suppliers

15 Response GC demands Suppliers send their supply

plan to GC

16 Inquire project information

and send change

The owner inquires from GC

about the project information

and sends demands change

17 Report and response change GC reports the project

information to Owner and

response the demands change

18 Require GC requires for relative

problems of the design

drawings

Collaboration Explanation

16 Explain Designer explains the design

drawings

20 Put forward new demands The owner puts forward his

new demands, i.e., design

change

21 Offer design drawings The designer offers drawings

to meet the owner’s demands

Table 1 (continued)

X. Xue et al. / Automation in Construction 14 (2005) 413–430 423

that utility gap remains between the initiating agent

and other agents. This loop can be repeated. The

main advantage of this protocol is that it guarantees

the discovery of the Pareto optimum [47].

5. Implementation of ABS3C

The prototype is developed by using the ZEUS

agent building toolkit. ZEUS is an advanced devel-

opment toolkit for constructing distributed multi-

agent applications. It defines a MAS approach and

supports it with a visual environment for capturing

user specification of agents that are used to generate

Java source code of the agents. ZEUS toolkit

proposes five main technologies to enable agents to

coordinate at the knowledge level: information

discovery, communication, ontology, coordination,

and legacy software problems. ZEUS is a culmina-

tion of a careful synthesis of established agent to

provide an integrated environment for the rapid

software engineering of collaborative agent applica-

tions [37].

The implementation process consists of the follow-

ing steps: role modelling, application design, and trial

run.

5.1. Role modeling in ABS3C

ZEUS agent building toolkit adopts role model-

ing to address the specification, analysis, design,

implementation, and maintenance of agents. Role

models formalize the definition of an agent role and

provide a comprehensible means of analyzing the

problem in question. The role models are grouped

into domains. The domains provide a context that

enables developers to compare their planned system

with existing applications. Role models, which

Page 12: An agent-based framework for supply chain coordination in construction

Fig. 9. Schedule of HCP building.

Fig. 10. Ontology in ABS3C.

X. Xue et al. / Automation in Construction 14 (2005) 413–430424

describe the dynamic interaction between roles, are

architectural patterns that depict the high-level

similarities between related systems, i.e., the prob-

lems inherent to each domain, but not how they

were solved. The role models of ABS3C are

illustrated in Fig. 7. The interactions between these

roles are shown in Fig. 8. Table 1 summarizes the

collaboration relationships.

5.2. Application design

This process is illustrated based on a hypothetical

construction project: HCP building. The HCP

involves the following participants: owner, designer,

general contractor, groundwork subcontractor, civil

and structure subcontractor, building services subcon-

tractor, finishing works subcontractor, concrete sup-

plier, and finishing materials supplier. The project

schedule is shown in Fig. 9.

5.2.1. Ontology creation

Ontology is a set of declarative knowledge re-

presenting every significant concept within a partic-

ular application domain. The significance of a

concept is easily assessed, if meaningful interaction

cannot occur between agents without both parties

being aware of it, then the concept is significant and

must be modeled. Ontology contains the key con-

cepts within the specific application domain, the

attributes of each concept, the types of each attribute,

and any restrictions on the attributes. In ZEUS, an

individual domain concept is described by using the

term dfactT. ZEUS provides two kinds of fact: abstract

and entity. In ABS3C, all the concepts refer to the

entity. The ontology created in ABS3C is shown in

Fig. 10.

5.2.2. Agent creation

Agent creation includes three steps: agent defi-

nition, agent organization, and agent coordination.

Agent definition determines the planning parameters,

task and initial resources allocation. Agent organ-

ization illustrates the relationship between the own

agent and other agents and acquaintance abilities

from other agents. Agent coordination defines the

coordination protocols and strategies between the

own agent and other agents. Multiattribute negotia-

tion and multiattribute negotiation protocol as

Page 13: An agent-based framework for supply chain coordination in construction

Fig. 11. Relations of agents in ABS3C.

Table 2

Agent created in the ABS3C

Agent name Roles played

Specialty

agents

GC agent CSC head (Negotiation

initiator, Manager and

constructor)

Owner agent Client

Designer agent CSC participant

(Negotiation partner,

supplier, designer)

Groundwork

subcontractor agent

CSC participant

(Negotiation partner,

supplier, constructor)

Civil and structure

subcontractor agent

CSC participant

(Negotiation partner,

supplier, constructor,

consumer)

Building services

subcontractor agent

CSC participant

(Negotiation partner,

supplier, constructor)

Finishing works

subcontractor agent

CSC participant

(Negotiation partner,

supplier, constructor,

consumer)

Concrete supplier

agent

CSC participant

(Negotiation partner,

supplier)

Finishing material

supplier agent

CSC participant

(Negotiation partner,

supplier)

Service

agents

ANS agent Agent name server

Monitor agent Monitor (to view, analyse or

debug societies of agents)

Construction

coordinator agent

Coordinator (receive and

respond to queries from

agents about the abilities

and preferences of other

agents)

X. Xue et al. / Automation in Construction 14 (2005) 413–430 425

previously mentioned are integrated into the proto-

type of ABS3C as the coordination protocols and

strategy in ZEUS. Table 2 lists the agents in the

prototype of ABS3C.

GC agent as the CSC head possesses the core

position comparing with other agents. GC is the

superior of subcontractors and suppliers. The owner

supervises the activities of GC and designer. The

relationships of GC and owner, subcontractor and

subcontractor are cooperative partnering. In HCP case,

the finishing material supplier and concrete supplier

are considered as the direct supplier of corresponding

subcontractors, so the suppliers are considered as the

subordinates of subcontractors. The relations between

agents in ABS3C are shown in Fig. 11.

5.2.3. Service and specialty agents configuration

The service agents, i.e., agent name server, monitor

and coordinator, are named as dutility agentsT in

ZEUS. The monitor agent is given the dvisualizersTtitle, and the coordinator agent is labeled as

dfacilitatorsT. Specialty agents, such as GC agent,

civil and structure subcontractor agent (as seen in

Table 2) are called task agents.

Service and specialty agents configuration (as

shown in Fig. 12) determines their running param-

eters in the prototype of ABS3C, e.g., the name of

agent, the IP address of host, time grain, the name

of address file, external program connected to agent,

etc.

5.2.4. External program

ZEUS allows users to link an external java class

(program) to executing ZEUS agent program. Once

linked to the agent program, external program can

utilize the agent’s public methods to query or modify

the agent’s internal state. The internal event model in

ZEUS provides a mechanism whereby all significant

events occurring in the agent can be monitored, for

example, planning events, resource, acquaintance

database events, message events, execution events,

and coordination engine events. Using the event

model, an external program that is linked to ZEUS

agent can monitor particular events in the agent and

react to them. In the prototype of ABS3C, each

Page 14: An agent-based framework for supply chain coordination in construction

Fig. 12. Configuration of service and specialty agents.

X. Xue et al. / Automation in Construction 14 (2005) 413–430426

specialty agent is linked to an external program as

shown in Fig. 12. Each external program provides a

user interface (negotiation window) through which the

Fig. 13. A screen shot when ru

users, for example, GC, subcontractor, supplier, etc.,

input their preferences on a decision making in the

process of CSC.

ns the prototype system.

Page 15: An agent-based framework for supply chain coordination in construction

Fig. 14. Negotiation window (GCUI).

Fig. 16. Dialogue for new goal of agents.

X. Xue et al. / Automation in Construction 14 (2005) 413–430 427

5.3. Trial run and discussions

The commands of running the prototype of ABS3C

can be generated through the dCode GeneratorT panelin ZEUS. Fig. 13 gives a screen shot of the prototype

system. When running the prototype, the negotiation

Fig. 15. Information exchange of agents in ABS3C.

windows will display on the screen waiting for the

decision makers input the preferences and relative

weightiness of attributes. In the HCP case, we only

consider five negotiation attributes: cost, time, quality,

safety, and environment, which present the decision

maker’s different preferences and utility, as shown in

Fig. 14. The above information will be sent to the

coordinator agent and corresponding negotiation

agent. Fig. 15 displays the structure of HCP CSC

and the process of information exchange and commu-

nication between service agent and specialty agent.

When a new round of negotiation starts, the new goal

of agent can be created through the dEnter Goal

DialogT (Fig. 16).The prototype provides tool (monitor agent) to

dynamically monitor the traffic volume of each agent

and interagent traffic volume, as shown in Figs. 17

and 18. User can analyze and evaluate the perform-

ance and activities of agents. From Figs. 17 and 18, it

can be concluded that the running process is stable.

Although the frequency of communication of con-

struction coordinator is much higher than other

agents, this is not a problem. The phenomenon rightly

reflects the hub role of the coordinator agent in the

framework.

6. Conclusions

Many researches and practices have proved that

supply chain coordination has become the crucial

strategy for successful SCM and effectively improv-

Page 16: An agent-based framework for supply chain coordination in construction

Fig. 17. Traffic volume of each agent.

X. Xue et al. / Automation in Construction 14 (2005) 413–430428

ing the performance of organizations in various

industries. This paper defines that concepts of CSC

and CSC management, especially regards CSC

management as coordination of interorganizations’

decision making in CSC and the integration of key

construction business processes and key members

involved in CSC. The research finds that there still are

many problems in CSC. In order to resolve the

problems and improve the performance of construc-

tion, an agent-based framework for construction

supply chain coordination is designed based on the

agent technology and multiattribute negotiation and

utility theory. The framework, which integrates the

organizations in CSC into an agent system, provides a

solution for the supply chain coordination in con-

struction through the application of multiattribute

negotiation mechanism. The trial run reveals the

Fig. 18. Interagent t

feasibility to implement ABS3C. The development

of prototype system provides an effective platform to

simulate the coordination process in CSC. It is helpful

to reduce the duration of interorganization decision

making and enhance the quick response ability to

meet the change of demands in competitive marketing

environment.

Since this research on agent-based supply chain

coordination in construction is an initial effort, some

issues still need to be addressed. For example, the

multiattribute negotiation mechanism for supply chain

coordination in construction needs to be improved,

especially how agents can elicit user’s preferences and

utility functions relevant to negotiation-related deci-

sion making. Real construction process involves

various thousands of participants and activities, so

how to use agents to efficiently simulate the behaviors

raffic volume.

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X. Xue et al. / Automation in Construction 14 (2005) 413–430 429

of decision making is a real challenge. The developed

prototype system also has some components needed

to be improved. This reveals that relative tools and

approaches needed to be further developed for the

implementation of ABS3C.

Acknowledgement

The work described in this paper was supported by

the Research Grants Council of the Hong Kong

Special Administrative Region, P.R. China.

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