An Agent-based Framework for Supply Chain

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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

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 [13], and to address

adversarial interorganizational relationship of organ-

0926-5805/$ - see front matterD 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) 413430

www.elsevier.com/locate/autcon


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ization by increasing number of construction organ-

izations and researchers [1,412]. SCM can be

considered as the coordination of distributed decision

making of organizations or participants on materialflow, 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 chainexecutes, 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

collaborativecompetitive 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 acentralized 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 toimprove the performance of SCM [2224].

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 andsupplier 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

X. Xue et al. / Automation in Construction 14 (2005) 413430414


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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. Theseapproaches 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 systemof 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 thegoal 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 bothparticipants [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 MAUTwill 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-

X. Xue et al. / Automation in Construction 14 (2005) 413430 415


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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 agentsare designed and realized in ZEUS, an advanced

developing environment for distributed multiagent

systems and has been used to develo p 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 themodel 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 lesscost.

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.

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



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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

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

Fig. 3. Problems in CSC (adapted from Ref. [40]).

Fig. 2. Model of construction supply chain.



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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-term

focus;! 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 bestable, 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 GCs suppliers and subcontractors

suppliers. Here, we assume that all materials and

human resources are arranged by GC or subcontrac-tors and there are no owners 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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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, andowner 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

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



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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 evaluationAs 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,

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



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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 usedfive levels are: 1worse, 3bad, 5normal, 7

good, and 9better. 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 xij

mn

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 yij

mn

where 0VyijV1, and

yij xij

, ffiffiffiffiffiffiffiffiffiffiffiffiffiXmi1

x2ij

s

4.3.2. Utility determination

In this process, the target utility is determined. The

utility of the ith participants decision making (Ui) is

given by

Ui Xnj1

wjyij

Where the wj is the weighting of the jth attributes:

wj Xnj1

akj

0 Xnk1

Xnj1

akj

WherePn

j1 wj 1 and akj is the value of relativeweightiness between kth attribute and jth attribute.

The value of akj is given by

akj 1 ; if kth attribute is more important than jth attribute0:5 ; if kth attribute is important as jth attribute0 ; if kth attribute is less important than jth attribute

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An Agent-based Framework for Supply Chain