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Production Planning & Control: The Management of
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Multi-agent systems in production planning and
control: an overview
Maria Caridi
a
& Sergio Cavalieri
b
a
Department of Management , Economics and Industrial Engineering , Milano, Italy E-mail:
b
Università degli Studi di Bergamo, Department of Industrial Engineering , Italy
Published online: 21 Feb 2007.
To cite this article: Maria Caridi & Sergio Cavalieri (2004) Multi-agent systems in production planning and
control: an overview, Production Planning & Control: The Management of Operations, 15:2, 106-118, DOI:
10.1080/09537280410001662556
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2. Production Planning & Control,
Vol. 15, No. 2, March 2004, 106–118
Multi-agent systems in production planning
and control: an overview
MARIA CARIDI and SERGIO CAVALIERI
Keywords multi-agent systems, survey, production planning
and control
Abstract. The ever fast changes of customers’ needs and
demands ask for reconfigurable and adaptive production
systems, which can provide companies with the proper level
of agility and effectiveness, without disregarding at the same
time cost factors. In the last decade, a large amount of research
works on the adoption of multi-agent systems (MAS) in several
industrial environments has flourished. This approach, unlike
traditional centralized or multilevel hierarchical approaches,
assumes the presence of several decision-making entities, distrib-
uted inside the manufacturing system, interacting and cooperat-
ing each other in order to achieve optimal global performance.
Aim of this paper is at first to provide readers, which are not
experienced with the multi-agent approach, with some defini-
tionsandcategorizationsofthisparadigm.Secondarily,bymaking
use of an extensive database of more than 100 contributions
on this field, authors intend to evaluate how multi-agents sys-
tems have really impacted on the industrial practices at an
enterprise and at a broader supply chain level. Finally, driven
by the past research experiences of the authors and by
the extensive literature search, considerations and remarks on
the real potential benefits and on the major issues currently
inhibiting the spread out of this paradigm are reported.
1. Introduction
Manufacturing context is evolving towards global and
relevant changes, due essentially to the ever fast changes
of customers’ needs and demand. Highly competitive
pressures are pushing manufacturing systems towards
an exasperated reduction of the product lifecycles, lean
inventories, high utilization of resources and short lead
Authors: Politecnico di Milano, Department of Management, Economics and Industrial
Engineering, Milano, Italy, E-mail: Maria.Caridi@polimi.it and Sergio Cavalieri, Universita`
degli Studi di Bergamo, Department of Industrial Engineering, Italy.
MARIA CARIDI is a researcher in Industrial Production Management at Department of
Management, Economics and Industrial Engineering of Politecnico di Milano, Italy. She received
her PhD in Industrial Plants and Production Systems from the University of Parma. Her research
interests are in different areas of Production Planning and Control: in particular, she has been
studying different issues concerning materials’ management (e.g. security stocks under uncertainty,
managing engineering changes) and the application of Multi-Agent System theory to manufactur-
ing systems’ control. Lastly, as regards the Information Systems, she is concerned in how modern
Advanced Planning and Scheduling systems cover manufacturing system requirements and how
they can be effectively integrated with Enterprise Resource Planning systems.
SERGIO CAVALIERI is currently Associate Professor at the Department of Industrial Engineering of
the University of Bergamo. Graduated in 1994 in Management and Production Engineering,
in 1998 he got the PhD title in Management Engineering at the University of Padua. His main
fields of interest are Modelling and Simulation of Manufacturing Systems, Application of Multi-
Agent Systems and Soft-computing Techniques (Genetic Algorithms, ANNs, Expert Systems) for
Operations and Supply Chain Management. He has been participating to various research projects
at national and international level. He has published two books and about forty papers on national
and international journals and conference proceedings. He is currently coordinator of the IMS
Network of Excellence Special Interest Group on Benchmarking of Production Scheduling Systems
and member of the IFAC-TC on Advanced Manufacturing Technology.
Production Planning & Control ISSN 0953–7287 print/ISSN 1366–5871 online # 2004 Taylor & Francis Ltd
http://www.tandf.co.uk/journals
DOI: 10.1080/09537280410001662556
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3. times. Manufacturing is irreversibly moving from a mass
production to a mass customization fashion.
In order to respond to these requests, production
means need to become reconfigurable and founded on
autonomous and intelligent modules, which dynamically
interact with each other for the achievement of local and
global objectives. Production processes must embed
adaptivity attributes so to provide a company with the
required level of agility, that is the ability to success in
a rapidly changing outer environment. Control systems
should embed intelligence, flexibility, extensiveness,
fault-tolerance and, in order to reduce the amount of
investments, reusability (Shen and Norrie 1999, Zhou
et al. 1999). Moreover, in order to stand for a global
competitiveness and rapid market response, companies
have to abandon their local myopic attitude in favour
of integration with other enterprises in terms of com-
mon management systems. Collaborative strategies are
placing out traditional antagonistic approaches toward
suppliers or customers. Only through sound partnerships
is it possible to pursue a win-win strategy.
From the research side, all these critical factors are
motivating the straining search for a new generation of
advanced production systems which could guarantee their
fulfilment and, as a result, contribute to the strategic
success of today’s companies.
In the last years, a large amount of research work on the
use of multi-agent systems (MAS) in different industrial
environments has been produced. Such models, unlike
the traditional centralized or multilevel hierarchical-
basedarchitectures,assumethepresenceofseveraldecision-
making entities, distributed inside the manufacturing
system, interacting and cooperating each other in order
to achieve optimal global performance. The hypothesis
at the basis of these models is that, from the local auto-
nomous and often conflicting behaviours of the single
decision-making units, a global behaviour of the manu-
facturing system emerges, coherent with the requested
characteristics of reactivity and flexibility.
Undoubtedly, the advent of multi-agent systems has
represented in the last decade a real breakthrough in
the world of research, involving researchers and practi-
tioners coming from heterogeneous and, often, distant
fields. The nature of the single agent and, in a more
complex fashion, the complexity of interaction among
more agents has in fact attracted, among others, biolo-
gists, game theorists, AI researchers, social scientists and
management scientists. Sen (1997) provides an interest-
ing historical overview on the various research fields
involved in the MAS work.
Aim of this paper is to analyse how this promising
paradigm is being adopted in the industrial practice in
reality and, in particular, in the production planning and
control area. The analysis has been conducted by making
use of a database comprising more than 100 papers clas-
sifiable as focusing on MAS applications. In particular,
after a brief taxonomy of terms and definition on MAS
(section 2), the paper (section 3) will provide an insight
on the current known applications of MAS in the supply
chain contexts, and, at the enterprise level, in single
production systems. Section 4 will report the analysis of
application maturity of the surveyed MAS application.
Finally, section 5 will draw some conclusions and raise
some further points of investigation on this research topic.
2. Definitions and categorizations of multi-agent
systems
Since the early 1980’s, a flourish of definitions on
multi-agent systems has been proposed in the literature.
For an extensive review of agent theories, architectures
and languages, readers can refer to Wooldridge and
Jennings (1995).
Treating MAS as a monolithic approach is quite pre-
tentious. Rather, MAS features depend on the specific
requirements each application field gives more emphasis
to. For this reason, literature reports several proposals
of classification of multi-agent systems, in the attempt to
make a clarification on the different definitions research-
ers and practitioners have so far provided.
The most interesting taxonomies of MAS found out
in literature can be distinguished on the basis of their
focus on:
. design specifications – the pioneering work of Decker
(1987) is recalled, who distinguishes two main
dimensions of classification for Distributed
Problem Solving, that are control and communication:
the former relates to the cooperation degree among
agents, the coordination among cooperating agents
and the dynamics for reaching coordination; the
latter relates to communication paradigm, semantic
content and protocol. Later, Tchako (1994) extends
the Decker’s classification by adding the agent
dimension, which describes the characteristics of
the agents populating the MAS, e.g. adaptivity
and autonomy. Keilman (1995) proposes a clas-
sification strongly focused on the central role of
coordination and communication.
. industrial applications – this is mainly represented by
the research separately carried out by Parunak,
Jennings and Woolridge. The former proposes
a classification of agents according to their applica-
tion functions and observes that most of MAS
applications in manufacturing are related to pro-
duction and design (Parunak, 1994). Later, the
same Parunak (1998) provides a more extensive
taxonomy of industrial applications of MAS,
Multi-agent systems in production planning and control 107
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4. where the dimension of the maturity of application
appears for the first time. A different segmentation
of the application context is provided by Jennings
and Wooldridge (1998), who distinguish between
industrial, commercial, entertainment and medical appli-
cations. Lastly, it is worth recalling the taxonomy
by Weng and Ren (1996), which is more focused on
scheduling applications of MAS.
The proposal by Sachdev et al. (1998) represents a
bridge among the two streams of taxonomy works, since
their classification considers the application dimension –
encompassing not only industrial applications but
even entertainment, human – computer interaction, etc. –
besides the dimensions of agent, organization and
interaction.
In this paper, the taxonomy shown in figure 1 is
adopted, which is instrumental in analysing the features
of MAS applications. A brief description of MAS
categories and some related relevant literature references
follow:
Application domain – Each literature reference is classified
according to the enterprise activity (e.g. quotation,
design, engineering) modelled by the MAS application.
Inside the modelled function, each agent is responsible
for one or more activities and interacts with other agents
in order to fulfil its tasks. In some literature contribu-
tions, the application domain is wider than a specific
enterprise activity and encompasses a network of organi-
zations. In this case, each agent represents an organiza-
tion or a macroprocess inside an organization.
Agent – An agent is a decisional unit pursuing its own
objectives by communicating with other decisional
units of the system. The main attributes of an agent
are: adaptivity, that is its capability to adapt to the
dynamic evolution of the environment in order to
maintain its role and pursue its objectives; learning cap-
ability, that is the capability to increment dynamically its
own competences (Keilmann 1995); autonomy and proactive-
ness, that is the capability to elaborate internally its
own decision-making strategies and, according to them,
decide autonomously which behaviour and actions are
carried out on the outer environment (Steels 1995). In
the proposed taxonomy for industrial MAS applica-
tions, agents are classified according to the role they
play inside the system: it spans from cost management
agents for the quotation process to warehouse agents for
distribution management.
Control – Distributed decision-making systems are affected
by a complexity of control due to the interdependencies
between agents (as effect of the segmentation of the prob-
lem). This requires the need to adopt proper coordina-
tion mechanisms in order to guarantee a consistency
between local actions and global objectives of the overall
system. Coordination mechanisms can be declined in:
. implicit mechanisms, based on behaviour logics of
the single agents which are exante defined and
known; this can avoid any need of formal commu-
nication among agents; typical examples are game
theory (Vamos 1986; Keilmann 1995; Busuioc
1996) and the behaviour-based approach (Dorigo
et al. 1996);
. explicit or cooperation mechanisms, through which
agents can explicitly express their own intentions
and mutually agree on common action plans; the
degree of cooperation can vary from fully to antag-
onistic cooperation (refer to Decker 1987 for more
insights on cooperation mechanisms); typical exam-
ples are the well known contract net mechanism (Smith
1980), where the coordination problem is solved
out through a contracting mechanism between a
supplier and purchaser agent, and the voting system
(Rosenschein and Ephrati 1993), where the coop-
eration is reached through a consensual process
based on a voting procedure.
Organization – organization in a MAS results from the
way tasks are distributed among the decisional units;
the main features of organization are:
. decision-making distribution: it spans from rigid
unidirectional control of master/slave organization
to a contracting system among the agents;
. organizational structure: it describes the hierarchi-
cal relationship among the agents, which depicts
also the hierarchical relationship among tasks
assigned to agents; it spans from centralized organi-
zation to heterarchical organization (see figure 2)
Communication – it is a fundamental feature in a MAS,
since it enables the explicit coordination among the
agents; communication can be classified according to:
. communication vehicle: it is the set of logical-
physical structures through which information is
HETERARCHICAL HIERARCHICAL CENTRALIZED
Figure 2. Forms of organizations.
MAS
APPLICATION
DOMAIN
AGENT CONTROL ORGANIZATION COMMUNICATION
Figure 1. Categories of classification.
108 M. Caridi and S. Cavalieri
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5. interchanged among the agents; examples of
communication vehicle are message passing and
the blackboard system (Kuru and Akin 1994,
Lefrancois et al. 1996, Kadar et al. 1997);
. protocol: it describes the semantic structure and
the content of the messages exchanged between
the agents; first efforts in creating a standard
protocol are the Knowledge Interchange Format
(Genesereth and Fikes 1992), with its syntac-
tic rules, the Knowledge Query Manipulation
Language, in its various upgrades (Finin et al.
1993), providing a set of semantic performatives,
and the COOL protocol proposed by Barbuceanu
and Fox (1995).
3. Application of multi-agent systems in the
manufacturing context
Literature analysis has led to the identification of more
than 100 contributions dealing with MAS application to
production planning and control. This section presents
the results of literature classification on the basis of the
taxonomy described in the previous section. The review
is mainly finalized to the evaluation of the application
domains, the way MAS have been implemented and
their impact on the underlying production systems.
Through the description, some relevant examples are
also reported.
3.1. Application domains and role of agents
Table 1 reports the degree of application of MAS
with regards to the main application domains: the per-
centage of contributions dealing with each domain is
reported and the main roles of MAS agents are high-
lighted. Finally, the last column highlights some refer-
ences to relevant examples available in literature for
each domain. Since most of the contributions are across
different application domains (e.g. MAS application to
scheduling and monitoring), for each domain only the
applications presenting peculiar features have been
reported in the table.
A fertile application is design. Among the contribu-
tions related to this specific application domain, it is
worth recalling the work by Mori and Cutkosky (1998),
which is focused on the development of a MAS for the
design of electronic board subassemblies. Ozawa et al.
(2000) offer another interesting application of concurrent
engineering of electromechanical products, where one
of the critical issues resides on the strong need of
Table 1. MAS application fields.
Application domain
Spread
(%) Role of agents Sources
Order quotation 5 Cost management agents Balasubramanian and Norrie (1996),
Parsons et al. (1999)
Design 13 Design agents, Geometric
interface agents, Feature agents
Frost and Cutkosky (1996), Bohez and
Limsombutanan (1997), Deshmukh and
Middelkoop (1998), Mori and Cutkosky (1998),
Vidal (1998), Ozawa et al. (2000)
Engineering 6 Process design agents,
Manufacturing design agents
Muir et al. (1997), Brown et al. (1998),
Gowdy and Rizzi (1999)
Demand forecast 5 Sales agents, Marketing agents Parunak (1998), Baker (1996)
Order management 7 Order agents, Order holon Bongaerts and Valckenaers (1995),
Papaioannou and Edwards (1998)
Master production
Schedule
6 Production planner agents Maturana et al. (1997), Gupta et al. (1998),
Wang and Paredis (1999)
Material requirements
planning
9 Production planner agents Kanchanasevee and Biswas (1997),
Sikora and Shaw (1997)
Scheduling 20 Scheduler agents,
Dispatching agents
Baker (1992), Daouas et al. (1995),
Maturana and Norrie (1995), Saad et al. (1995),
Tharumarajah and Bemelman (1997)
Purchasing 7 Order agents, Purchase order
agents, Supplier agents
Kouba and Lhotska (1998)
Monitoring 17 Controller agents, Monitor agents,
Quality control holon
Lin and Solberg (1992), Liu and Sycara (1996),
Heikkila et al. (1997), Parunak (1998),
Fraile et al. (1999)
Distribution 5 Inventory storage agents,
Warehouse holon
Fisher and Muller (1995)
Multi-agent systems in production planning and control 109
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6. coordination between mechanical and electronic depart-
ments in order to anticipate design infeasibilities. The
work of Deshmukh and Middelkoop (1998) is specifically
applied to highly sophisticated products.
As regards the production scheduling and monitoring
field, further results of classification are reported in table
2 with an insight on the kind of production systems.
Discrete Manufacturing – Most of the applications are
directed towards discrete manufacturing production
and, in particular, the fabrication domain. Unfortu-
nately, most of the reviewed papers, classified as ‘generic
shop-floor systems’, do not explicitly indicate the specific
application environment, since they are mainly focused
to the model definition or to a specific description of
algorithms at a theoretical state.
The statistics confirm that research efforts are mainly
addressed to production systems with non-linear flows
and high workload, as job-shops and assembly shops.
Among the most interesting works, it is worth recalling
the study of Baker (1992), which reports a MAS appli-
cation to the Small Parts Shop at GE Power Generation
Business. Another interesting application to scheduling
and monitoring is in Liu and Sycara (1996), where
agents are completely delegated with some scheduling
tasks of production jobs.
MAS flexibility and effectiveness has been investigated
by many other applications: scheduling and monitoring
of a generic shop floor (Lin and Solberg 1992; Maturana
and Norrie 1995; Tharumarajah and Bemelman 1997);
flow shop scheduling (Daouas et al. 1995), which com-
bines the multi-agent technique with simulated anneal-
ing; flexible manual assembly line design (Sprumont and
Muller 1997), whose aim is to determine functional specif-
ications of components and the least expensive organi-
zational structure of an assembly line. Moreover, the
multi-agent paradigm appears particularly suitable for
induction engine assembly (Kanchanasevee and Biswas
1997) or for scheduling of ships assembly (Choi and Park
1997): they are in fact examples of low volume produc-
tion and wide product range, which highlight the lack of
flexibility of centralized management. Another interest-
ing application to fabrication and assembly is in Sikora
and Shaw (1997), where agents coordinate automated
and manual lines in printed circuits manufacturing.
Finally, it is worth recalling the experiences with
the Minifactory concept (Gowdy and Rizzi 1999).
Minifactory is a miniature of an assembly system,
which is modular and highly sophisticated, based on a
precise integration between hardware and software
applications. The integration is nowadays possible by
building an architecture of mechanical and computa-
tional agents, which are aware of their capabilities and
of the role each of them plays inside the assembly system.
They cooperate each other through a peer-to-peer
negotiation mechanism.
Continuous processes – Multi-agent paradigm has been
applied also to continuous processes. It is remarkable
the control system for air supplying to a painting shop
developed for the General Motors assembly plant in Fort
Wayne (Parunak 1998): each humidifier, burner, steam
generator is controlled by an agent which is responsively
autonomous and reacts to different environment con-
figurations. The benefits of this application are: paint
saving, thanks to the lower number of colour setups,
40% reduction of software control, setup time reduction
and system managing simplification.
Other works deal with applications to semiconductor
manufacturing (Parunak 1998), mould designing for
plastic injection print (Vidal 1998), sheet metal cutting
and paper cutting design and scheduling (Parunak
1998). Gupta et al. (1998) present an interesting applica-
tion of distributed artificial intelligence to the planning of
automated process for sheet metal bending: each compo-
nent of the sheet metal bending press-brake is controlled
by a specialized agent; the distributed architecture allows
embedding the specific knowledge of each agent in a
separate module and utilizing different problem solving
techniques and system representations for each module.
The modularity of the architecture simplifies the system
updating as consequence of possible changes, since only
Table 2. Typologies of production systems and frequency of MAS application.
Production system Spread (%)
Discrete manufacturing 94
Fabrication Job shop 9 52
Lines 3
Flow shop 2
FMS 3
Generic shop floor 35
Assembly Manual assembly lines 1 42
Automated lines 2
Generic assembly shop 36
Minifactory 3
110 M. Caridi and S. Cavalieri
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7. the involved modules are updated. The coordination
among the specialized agents is obtained through
constraint sharing.
Supply chain level – Traditionally, MAS have been
applied at enterprise level in order to solve issues addres-
sing specific functional needs or involving a specific
decisional-making activity. Then, coherently with the
new managerial practices consolidated in the 1990s,
which have shifted the competitive edge to a process-
based organization and to an integrated perspective,
MAS approaches have flourished in supporting supply
chain management and, in general, in aiding decision-
making activities among external organizations. As the
well known MIT Beer Game (Sterman 1989) shows, the
interdependencies between the single tiers affect the over-
all outcome of the logistics chain from the final retailer
to the manufacturer (bull-whip effect). So, supply chain
management does not strive for internal efficiency of
operations (as logistics aimed in the past), but rather
for the management and coordination of the activities
throughout the whole supply chain.
As Hinkkannen et al. (1997) and Strader et al. (1998)
maintain, the Supply Chain model naturally suggests the
decomposition approach that, in turn, allows for the
design of a multi-agent organization. Within an organi-
zation, agents can support human decision-makers
in monitoring and controlling time consuming and
highly computing activities, as for example inventory
management, and assisting them in sending out orders
or carrying out negotiation activities without the need
for unwieldy centralized or top-down management
schemes. This would relieve humans from routinary
and programmable tasks.
Various proposals dealing with two or more logistics
tiers are retrievable in literature. Among the most
remarkable, the ISCM (Integrated Supply Chain
Management) agent-based model (Fox et al. 1993) can
be considered one of the pioneers in this context. The
ISCM is composed of a set of cooperating agents,
where each agent performs one or more supply chain
management functions, and coordinates its decisions
with other relevant agents. Agents are expected to per-
form different roles; in particular, functional agents are
entitled to manage the relationships with the downstream
customers (by acquiring and managing orders) and to
carry out all the subsequent related tasks, starting from
re-supply orders to the production and transportation
planning; information agents support functional agents
by providing information and communication services.
Sauter and Parunak (1999) propose the ANTS (Agent
Network for Task Scheduling) architecture that decom-
poses each firm into a fictitious miniaturized supply
chain, made up of producers and consumers. As a result,
the interfaces between agents within a firm are the same
as those among the firms inside the real supply chain; the
result is that the integration among the firms becomes
more natural.
Strader et al. (1998) develop a multi-agent simulation
platform, which supports decision-making of supply
chain managers. Their model is used to study the impact
of information sharing on order fulfilment in divergent
assembly supply chain; their main conclusion is that
through information sharing among actors, uncertainty
can be reduced thus decreasing overall inventory costs.
Several other multi-agent architectures for supply
chain management have been proposed in literature;
here are recalled: MASCOT (Multi-Agent Supply Chain
Coordination Tool), based on a blackboard commu-
nication paradigm, whose aim is to support supply chain
key functionalities (Sadeh et al. 1999); the supply chain
dynamics modelling approach based on software compo-
nents, proposed by Swaminathan et al. (1998); the appli-
cation of agent technology to decision support systems
for supply chain real-time management, proposed by
Hinkkanen et al. (1997).
3.2. Organization and control
Table 3 reports the diffusion of different agent archi-
tectures into the analysed literature.
Heterarchical architecture – In heterarchical architectures,
no hierarchical relationship among agents takes place.
It is worth recalling the well-known model by Lin and
Solberg (1994), who propose a heterarchical architecture
for adaptive scheduling and monitoring in a dynamic
manufacturing environment. Heterarchical architectures
have been applied in several other works: a market-
driven approach for planning and control (Baker 1992);
the above-introduced Minifactory system (Gowdy and
Rizzi 1999); scheduling of job shops (Saad et al. 1995),
generic shop floor (Tharumarajah and Bemelman 1997,
Krothapalli and Deshmukh 1999), flow shop (Daouas
et al. 1995); control systems for multimanipulation assem-
bly (Fraile et al. 1999); the above-introduced ISCM
(Integrated Supply Chain Management) by Fox et al.
(1993); the organization inside the design agents team
for ship manufacturing proposed by Choi and Park
(1997). Literature review shows that, though multi-
agent systems based on heterarchical architectures are
the most widespread, they hardly turn out at prototypical
or production phase. This is expression of the fact that
their level of maturity is quite low. Industry is still far
away from the idea of realizing completely distributed
systems, with loose or null connections among the auton-
omous agents. Moreover, this kind of architecture is char-
acterized by communication overload and, consequently,
Multi-agent systems in production planning and control 111
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8. high implementation costs, being the overall optimum
not guaranteed.
Heterarchical architecture with coordinators – In this type
of architecture, even if no hierarchical relationship
among the agents takes place, there are particular agents
(e.g. facilitators, mediators, brokers) which help coordi-
nation and communication among agents and settle pos-
sible disputes in order to assure system stability. An
example of heterachical architecture with mediators is
MetaMorph II (Shehory and Kraus 1998), whose aim
is the integration of a company’s operations (e.g. design,
planning, scheduling, execution, distribution) with the
ones of its suppliers, customers and partners, in an open
and distributed system: it is an hybrid architecture,
whose higher level is made up of different interconnected
subsystems, which are integrated to the main system via
internet/intranet through mediators. In ANTS architec-
ture, presented by Sauter and Parunak (1999) for supply
chain management, coordinators play the role of brokers.
Brokers are also modelled in Frost and Cutkosky (1996),
where they represent the interface between system
agents and service agents. In Maturana and Norrie
(1995), mediators are organized in a distributed structure
for supporting and coordinating system activities,
whereas in Sun et al. (1999) they facilitate planning
and scheduling process.
Hierarchical architectures – In hierarchical architectures,
lower levels depend on higher levels, which completely
or partially control them. An example of this type of
architecture is provided by the multi-agent system for
planning resources allocation in a manufacturing
environment, proposed by Bastos and Oliveira (1998).
Keilmann and Conen (1996) present an hierarchical
architecture, where each agent task is decomposed in
tasks of lower level agents. The PROCURA model
(Project Management Model of Concurrent Planning
and Design) integrates tactical and execution planning
through a top-down hierarchical approach (Golfarelli
and Maio 1997).
Modified hierarchical architectures – In spite of the high
degree of autonomy of each agent, these forms of archi-
tecture preserve a hierarchical level in order to guarantee
system stability. Kouiss and Pierreval (1997) propose a
modified hierarchical architecture for dynamic schedul-
ing in a FMS for real-time job allocation to resources: the
allocation depends on the shop floor status (e.g. machine
breakdowns, resource availability, bottleneck position)
and on manufacturing objectives (e.g. WIP reduction,
lateness minimization). In Fisher et al. (1993) a ship-
ping company agent allocates transportation orders to
trucks agents, in compliance with customer requirements,
and it can cooperate or compete with other ship com-
pany agents for the acquisition of transportation orders.
Park et al. (1994) presents an architecture for the con-
current design of industrial cables, where system decom-
position reflects the hierarchical approach, since four
peripheral agents, each endowed with specific tasks
and a certain degree of autonomy, are interfaced with a
central node.
Holonic architecture – In holonic architectures, the dis-
tributed system is made up of holons which dynamically
adapt to the life cycle of the manufacturing system
(Bongaerts and Valckenaers 1995). Holonic manufactur-
ing systems summarize the best properties of hierarchical
and heterarchical ones: high quality and predictability of
results, soundness to possible disturbs. They have been
applied to planning and scheduling of an assembly shop
(Biswas et al. 1995), to scheduling and monitoring of a
generic shop floor (Zhang and Norrie 1999), to engine
assembly scheduling (Kanchanasevee and Biswas 1997),
for managing and coordinating manufacturing activities
(Deen 1994).
Table 3. Architectures in surveyed MAS.
Multiagent architecture Spread (%) Sources
Heterarchical 48 Baker (1992), Fox et al. (1993), Lin and Solberg (1994),
Daouas et al. (1995), Saad et al. (1995),
Choi and Park (1997), Tharumarajah and Bemelman (1997),
Fraile et al. (1999), Gowdy and Rizzi (1999),
Krothapalli and Deshmukh (1999),
Heterarchical with coordinators 26 Maturana and Norrie (1995), Frost and Cutkosky (1996),
Shehory and Kraus (1998), Sauter and Parunak (1999),
Sun et al. (1999)
Hierarchical 4 Keilmann and Conen (1996), Golfarelli and Maio (1997),
Bastos and Oliveira (1998)
Modified hierarchical 12 Fisher et al. (1993), Park et al. (1994),
Kouiss and Pierreval (1997)
Holonic 10 Deen (1994), Biswas et al. (1995), Kanchanasevee and
Biswas (1997), Zhang and Norrie (1999)
112 M. Caridi and S. Cavalieri
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9. 3.3. Communication
Table 4 reports the level of diffusion of the two main
communications paradigms (i.e. message passing and
blackboard) being adopted in the reviewed works.
Communication is one of the most relevant features
when developing a multi-agent system. It aims at updat-
ing in real-time the agents about the evolutions of envi-
ronment, so that agents can promptly react. Moreover,
it supports the agent’s forecast capability, so that uncer-
tainty is reduced. Finally, it enables agents to have
knowledge about other agents’ behaviour and to
cooperate in order to pursue agent’s objectives.
Communication can be classified according to the
paradigm and the protocol. The paradigm defines the
way the communication takes place (i.e. shared global
memory or blackboard, and message passing). The pro-
tocol specifies: the structure of the dialogue among the
agents (i.e. reactive protocol, voting protocol, contract
net, constraint propagation, speech acts), the form of
addressee selecting (selective communication, multicast
communication, broadcast communication); finally, at
a higher level, the semantic structure and the content
of exchanged messages.
All the above-stated communication components are
variously combined in the surveyed applications. The
most adopted communication paradigm is message
passing, with contract net as explicit mechanism.
In MASCOT (Sadeh et al. 1999), communication is
based on the blackboard paradigm: it is an effective
means for integrating multiple sources of knowledge; in
fact it allows to embed the problem solving knowledge
of different knowledge sources, which develop solutions
to problems by communicating through a layered black-
board; each layer corresponds to a specific ‘context’, that
is a particular status of the environment (e.g. production
orders to be planned or scheduled, available resources,
agreements with suppliers). Other interesting examples of
a blackboard paradigm are available in Liu and Sycara
(1996), Vidal (1998) and Fraile et al. (1999).
As for message passing, the work by Saad et al. (1995)
is recalled, dealing with the job-shop dynamic schedul-
ing: in this application, agents do not share a fix memory
location where communication is stored, on the contrary
they send and receive messages according to several
forms; the contract net protocol is utilized in this case.
Applications of message passing and contract net proto-
col can be found in Lin and Solberg (1994), the market
driven system by Baker (1992), the distributed resource
allocation by Bastos and Oliveira (1998), the holonic
manufacturing system by Kanchanasevee and Biswas
(1997).
Other implemented protocols are: the voting scheme
for communication among agents for scheduling of
resources in a semiconductor fabrication (Parunak
1998); the speech acts protocol in the ISCM by Fox
et al. (1993); the constraint propagation in the concurrent
engineering application by Petrie (1997) and in Sachdev
et al. (1998); the reactive protocol of the Minifactory
assembly system (Gowdy and Rizzi 1999) and of the
CASPER project (Sohier et al. 1998).
4. Maturity degree of surveyed MAS applications
In this section, the maturity degree of multi-agent
system applications found in literature is analysed. The
aim is to identify the application domains where actual
(not emulated) MAS applications provide better perfor-
mances in comparison with the traditional approach.
The surveyed literature suggests that the MAS models
that are operatively utilized by a company (‘Production’
column in Table 5) or have been translated into a com-
mercial product (‘Product’ column in Table 5) are few,
Table 4. Communication paradigms and protocols in surveyed MAS.
Communication
Paradigm Protocol Spread (%) Sources
Message passing Reactive protocol 8 86 Sohier et al. (1998), Gowdy and
Rizzi (1999)
Voting protocol 2 Parunak (1998)
Contract net 53 Baker (1992), Lin and Solberg (1994),
Saad et al. (1995), Kanchanasevee and
Biswas (1997), Bastos and Oliveira (1998)
Constraint propagation 1 Petrie (1997), Sachdev et al. (1998)
Speech Acts 6 Fox et al. (1993)
Not specified 16 –
Blackboard 14 13 Liu and Sycara (1996), Vidal (1998),
Fraile et al. (1999), Sadeh et al. (1999)
Multi-agent systems in production planning and control 113
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10. whereas most of the applications are at an emulated
stage. Notice that the most mature applications
(‘product’ stage) apply to scheduling and monitoring:
in particular, they apply to the scheduling of continuous
processes (GM paint shop, and paper and steel mill,
in Parunak (1998)) and to transfer lines monitoring
(Zone Logic, in Parunak 1998).
Among the applications in the ‘production’ stage,
Sadeh et al. (1999) is recalled where a management
system for a generic supply chain is presented; Parunak
(1998) reports several applications such as: AMROSE,
for the scheduling of the assembly of ocean-going vessels;
ADS (Autonomous Distributed System) for sheet steel
processing lines control; Daewoo Scheduling System
for the press shop at Daewoo Motors; LMS (Logistics
Management System) for tool managing in semicon-
ductor fabrication.
Promising applications relate to the design domain,
although they are still at an ‘emulated’, ‘prototype’ or
‘pilot’ stage. As far the other application domains
are concerned, most of the MAS remain at a ‘modelled’
stage (Deen 1994, Golfarelli and Maio 1997,
Swaminathan et al. 1998, Bastos and Oliveira 1998) or
‘emulated’ stage (Lin and Solberg 1994, Heikkila et al.
1997, Kouiss and Pierreval 1997, Papaioannou and
Edwards 1998, Baker et al. 2001) or ‘prototype’ stage
(Balasubramanian and Norrie 1996, Hollis and Quaid
1995, Sprumont and Muller 1997, Valckenaers et al.
1997, Barbuceanu et al. 1999).
5. Final remarks
Given the flourish of proposals of MAS-based models
in heterogeneous application fields, the literature survey
reported in this paper, though thorough and extensive,
cannot certainly aim to be comprehensive. During the
1990’s, the multi-agent approach has been a fashionable
topic, where numerous researchers have contributed in
the common effort to derive industrial applications.
However, despite the density of efforts and projects
carried out, there is still no clear understanding where
and how multi-agent systems can provide better results
than ‘traditional’ models. Authors often dwell on the
theoretical description of design hypotheses and struc-
tural characteristics, but do not provide satisfactory indi-
cations on their level of applicability. As a result, it is
evident that without giving a clear answer to this funda-
mental question, the technology gap between research
and industrial application would dramatically widen.
From the present perspective, the MAS paradigm is
characterized by some general properties which, given
a specific context where to be applied, may be inhibiting
or, vice versa, enhancing their applicability.
As for the strength points, five basic features can be
identified: multi-agent systems are suitable for applica-
tions which are modular, decentralized, complex, time
varying, ill-structured (Parunak 1998).
. Modularity allows the system to be modified, mod-
ule by module, so that reconfiguration costs are
drastically reduced and system reusability increases.
. Decentralisation minimizes the impact of local mod-
ifications on other system modules. In fact, in a
decentralized system, the behaviour of a single mod-
ule influences only the modules that are interacting
with it, whereas the remaining part of the system is
not affected. This feature is important when dealing
with production systems characterized by a phy-
sical distribution of production and logistic units
and often affected by local disturbs (e.g. machine
breakdown, material shortage) which require local
re-planning.
Table 5. Degree of maturity of MAS applications classified according to the application domain.
Degree of application’s maturity
Application domain
Modelled
(%)
Emulated
(%)
Prototype
(%)
Pilot
(%)
Production
(%)
Product
(%)
Total
(%)
Order quotation 1 2 2 – – – 5
Design 2 4 5 1 1 – 13
Engineering 1 3 2 – – – 6
Demand forecast 1 2 1 – 1 – 5
Order management 1 3 3 – – – 7
Master production Schedule 1 3 1 – 1 – 6
Material requirements planning 1 3 3 1 1 – 9
Scheduling 2 10 5 1 1 1 20
Purchasing 1 3 2 – 1 – 7
Monitoring 2 8 4 – 1 2 17
Distribution 1 3 1 – – – 5
Total 14 44 29 3 7 3 100
114 M. Caridi and S. Cavalieri
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11. . The capability to embed multiobjective functions
and multiple constraints and variables to be con-
trolled provides a reasonable trade-off in approach-
ing complex problem solving environments.
. The multi-agent concept allows to effectively model
time-varying physical systems. This is a very impor-
tant feature when applied to production systems,
which frequently modify their configuration, due
to market requirements or to internal resources
endowment.
. Finally, when designing a new production system,
not all the requirements are available at the begin-
ning of the design phase: for instance, which entities
have to communicate, how the interfaces among the
communicating entities should be designed. As a
consequence, the designed system encounters the
risk of resulting ill-structure when all requirements
are clearly stated, which implies extra costs and
delays in project release. Multi-agent systems can
contribute in avoiding these pitfalls. Agents may
interact with agents endowed with the role of
modifying the environment within ranges which
can be managed by the other agents.
On the contrary, as regards the critical issues inhibit-
ing a widespread application of multi-agent systems in
production and control domain, the following can be
recalled.
. First of all, agent-based problem-solving does not
always succeed in optimally solving a problem and
may result computationally unstable, that is it may
not reach a feasible solution within a given compu-
tational time.
. MAS approach fails in modelling physical systems
that cannot be decomposed into sub-problems
and subobjectives. In order to quantify agent-
based model exposition to the above-stated limita-
tions, it is necessary to fairly design appropriate tests
to measure the quality of system performance.
. Agent-based systems require large investment in
monitor equipment and support equipment. In
fact, testing and tuning this equipment result hard
and expensive. Traditionally, simulation is utilized
in order to test MAS under various operative con-
ditions; unfortunately, simulation experiments can-
not cover large ranges of operative conditions (as
large as those that will actually stress the system
during the real utilization) without undermining
the computational efficiency of the test.
In the opinion of the authors, when specifically dealing
with application of multi-agent systems to production
planning and control, nowadays the trade-off between
pros and cons of MAS applications is unbalanced
towards the cons: the high investment and the risk related
to system effectiveness still act as a disincentive to the
development of real industrial applications based on
this paradigm. Moreover, it is observed that industrial
companies and software houses are not yet receptive:
the few applications referenced in literature are mainly
specific outcomes of research programmes, with a certain
difficulty to be generally extended to a wider industrial
community in the form of a commercial on-the-shelf
software.
Considering the wide experiences carried out in these
years by the research community, multi-agent approach
can turn out to be effective in all those fields where much
of the efforts and time are spent in carrying out collab-
oration tasks among a definite and limited number
of actors. This is typical of processes like Concurrent
Engineering (Wheelwright and Clark 1992), SCEM
(Supply Chain Event Management) (Marabotti 2002)
and CPFR (Collaborative Planning Forecasting and
Replenishment) (Vics 2002), where decision-making
activities are naturally distributed among more partners.
In such environments, it is more feasible to elicit the
knowledge bases of the single actors and transfer them
into automatic decision-making activities which can
relieve humans from carrying out routinary tasks, by
supporting collaboration, providing high-speed comput-
ing and guaranteeing a solution convergence.
On the contrary, whereas the problem is hardly
decomposable or it is quite difficult to provide agents
with knowledge representation (since no direct elicitation
from humans to agents is possible), some critical issues
come out, reducing the chance to evolve from conceptual
(or emulated) models to industrial products. This is the
case of multi-agent systems applied to job-shop schedul-
ing which, despite the wide spectrum of proposals avail-
able in literature, have not yet turned out in an industrial
phase. Two main factors affect their applicability on this
domain:
. the extensive number of agents required – in the
case of heterarchical models equal to the number
of jobs and resources populating the production
systems – which can strongly inhibit a convergence
of the solution and a computational efficiency;
. the high dependence of the design parameters of
a MAS-based scheduler on the production system
and scenarios to be controlled. It is still missing a
systematic and quantitative analysis demonstrating
that, given a production system with specific fea-
tures (e.g. plant lay-out, process routings, loading
distributions, . . .) it is possible to identify which
multi-agent architecture and which set of rules of
protocols outperform other competing (distributed
or not) control algorithms.
Multi-agent systems in production planning and control 115
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12. One possible solution for addressing better which pro-
duction domains can benefit from the application of a
distributed decision-making approach is the development
of a benchmarking service (Cavalieri et al. 2000, Cavalieri
and Macchi 2001). The service will allow carrying out a
mutual comparison of novel and traditional approaches
under different production systems and a variety of
manufacturing scenarios. Such a benchmarking service
is one of the main activities currently developed under
the framework of the IMS-Network of Excellence (IMS
NoE 2002), actually funded by the European
Commission.
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