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Collaborative e-Work, e-Business, and e-Service 88.3 Design Principles for Collaborative e-Work, e-Business, and e-Service 1565

Cell control system

Cell and robot control system

Best

matching

protocol

User

Solid modelling

machine

Cooperation

requirement

planning I

(CRP-I)

Best-matching protocol

Cooperation requirement planning II

(CRP-II)

Assembly

mating

and

constraints

generation

Multi-robot cell

information database

• Geometric

• Operational

• Physical

• Constraint

• Resource

High-level task

plan and assembly component

physical description

Cooperation requirement

matrix

Task assignment and global plan execution

Robot motions and cell

constraints

Assembly

geometric

decription

Assembly

constraints

Multirobot cell status

Fig. 88.19 Best-matching protocol-enabled cooperation requirement planning (CRP) architecture (after [88.76])

Logical

integrated

organization

(Ψ*,Φ)

Distributed

organizations

(Ψ,Φ

)

Optimized

integrated

distributed

organizations

Integration

Stage 1:

Dist. assignment

Stage 3:

Operational

optimization

Stage 2:

min Φ

(Φ should include

server cost)

Organizations at

the same location

Communication

links among

distributed tasks

Tasks being

executed in the

organization:

Integration

(ω

,..., ω

)

→ Ω

1. Measurement of K

when assigning

to distributed sites

2. Comparison of K

and savings from Φ

3. Repeat DPIEM

K >Φ

–Φ

,Φ

Fig. 88.20 Operational stages of the distributed planning of integration execution model (DPIEM) (after [88.48])

Part I 88.3

1566 Part I Home, Ofﬁce, and Enterprise Automation

Time/cost

Degree of parallelism Ψ

Total performance

Server

communication

Production

Fig. 88.21 Tradeoff behavior between production and

communication costs and times (after [88.48])

The principle of e-Work parallelism also inﬂuenced

the development of co-X tools, where “co-” implies

coordinated, cooperative, and/or collaborative support

tools, e.g., co-design and co-plan [88.85]. These tools

are deﬁned at three levels of interaction: (1) sharing of

information, ideas, and plans via an interface, (2) paral-

lelism ofinformation processingand physical tasks, and

(3) inclusion of active protocols and agents to support

parallelism.

Highly relevant to the principle of e-Work par-

allelism is the keep it simple system (KISS) design

guideline. It states that, as long as a computer and

communication-support system is enabled to work au-

tonomously, in parallel to and support of humans at the

simplest level for them, the system can be as complex

as needed. The implications of KISS on the e-Work

parallelism principle dictate that, in order to minimize

the need for human retraining when new versions and

advanced functions are incorporated into software sys-

tems, internal parallel e-Functions must be included in

the design.

Principle of Conﬂict Resolution

Originally developed by Huang and Nof [88.49, 89],

this principle addresses the cost of resolving con-

ﬂicts among collaborating e-Workers. A greater rate

of interactions, which increases the number of active

collaborating parties, also increases the number of con-

ﬂicts and errors that agents can incur. The development

of effective e-Work therefore requires that errors and

conﬂicts be overcome as quickly and inexpensively as

possible. In their work, Huang and Nof observe that the

total costs to resolve a conﬂict depend on the relative

portion of human intervention as follows: (1) a greater

Propagate

PredictedDiagnosed

PropagatePropagate

Activity Result

PredictedDiagnosed

Detection

logic

Prediction

logic

Detect

diagnose

Predict

Predicted

detected

Predicted

detected

Propagate

CEs CEsE[u(r,i, t)]

CEs CEs

C [n(r,t)]

u(i,t)

n(r,t)

Fig. 88.22 Conﬂict and error diagnostics and prognostics

(CEDP) logic over a coordination network (after [88.67])

number of errors and conﬂicts leads to an unbounded

exponential growth of cost of resolution, which im-

plies that the e-Work system will collapse due to the

inefﬁciencies of the system, and (2) for less human in-

tervention, approaching a value of zero (meaning that

information technology (IT) services are designed to

and applied in parallel), the total cost of resolution

reaches an upper bound and can therefore be effective

and sustainable (Fig.88.18).

The principle of conﬂict resolution requires the de-

velopment of better and more powerful IT tools for

detection, prevention, and resolution of errors and con-

ﬂicts, which can yield lower overall costs,better quality,

and more effective e-Work. Recent work has focused on

the development of computer-supported conﬂict and er-

ror detection management (CEDM) and diagnostics and

prognostics (CEDP) methods to eliminate errors and

conﬂicts inrobotic systemsand supplynetworks [88.66,

67] as seen in Fig. 88.22. See also Chap.30 (Automat-

ing Error and Conﬂict Prognostics and Prevention)in

this handbook.

Principle of Collaborative Fault Tolerance

Originally developed by Jeong [88.71], this principle

builds on the synergies between fault-tolerant collab-

orative control and feedforward collaborative control

protocols to yield better, more effective results from

teams of weaker agents (i.e., micro- and nanorobots,

micro- and nanosensors) in comparison with a single

Part I 88.3

Collaborative e-Work, e-Business, and e-Service 88.3 Design Principles for Collaborative e-Work, e-Business, and e-Service 1567

optimized faultless agent (Figs. 88.23 and 88.24). The

principle builds on the well-known fact that a collab-

orative team is better than the sum of the individuals,

and it addresses how to achieve this advantage by using

smart automation.

The Join/Leave/Remain (JLR) Principle

in Collaborative Organizations

This principle addresses the increasing number of vir-

tual enterprises and the dynamic nature of enterprise

alliances, self-organizing agent teams, sensor clusters,

modular systems, and others [88.94]. The principle en-

ables the identiﬁcation of conditions and timing for

individualagents (parties) or organizations tojoin, leave

or remain as part of a collaborative networked orga-

nization (CNO). Among the questions the principle

addresses are:

1. When and why would an organization join a given

CNO?

2. What are the beneﬁts for an individual agent or or-

ganization to be part of a given CNO?

3. What are the costs for an individual agent or orga-

nization to participate (join or remain) in a given

CNO?

4. Why would an individual agent or organization opt

to remain in a given CNO?

5. What are the criteria and performance evaluation

measures to characterize and assess the effective-

ness and performance of the CNO?

Middleware

(MidFSN)

Resource

management

QoS control

services

Base station

Photoelectronic

sensor

Inductive sensor

Fig. 88.24 Industrial application of a wireless microsensor network system with fault-tolerance capabilities (i.e., proto-

cols) to aggregate weak sensor signals (after [88.54])

0 5 10 15 20 25 30 35 40 45 50

Y (m)

X (m)

Fig. 88.23 Faulty sensor routed communication by time-based

control. The lines between nodes represent communication links

(after [88.93])

The join/leave/remain principle can be analyzed at

different levels such as (1) overall CNO level, (2) in-

dividual agent or organization level, (3) sub-CNO,and

(4) multi-CNOs, therefore making it adaptiveand useful

to address many types of agglutination and clustering

of agents or organizations (Figs. 88.25 and 88.26). Fur-

thermore, the analysis can be expanded to include each

Part I 88.3

1568 Part I Home, Ofﬁce, and Enterprise Automation

CNO

CCEE(T)

Fig. 88.25 Example of a collaborative networked orga-

nization (CNO) in a competitive environment at time T

(after [88.77])

phase of the CNO life: creation, activity, dissolution,

and support [88.77].

Principle of Emergent Lines of Collaboration

and Command (LOCC)

The environment in which modern enterprises must

conduct their day-to-day operations has become ever

more challenging due to the dynamics of networks and

the volatility of formal and informal communications

within andbetween themembers of the network, as well

as with suppliers and customers. This principle focuses

on three needs of organizations: (1) to have better col-

laborative decision-making, speciﬁcally under complex

and dynamic events, (2) deliver information and build

0 102030405060708090100

Best fit index (BFI)

Experiment no.

Fig. 88.27 Best-matching protocol (BMP) for the matching of 100 :

100 uniformly distributed parts ina manufacturingsetting. Thepart-

matching information is one of several factors then used to identify

which supplier to match with a given customer

0.1 0.2 0.3

AL(A) for O

0.4 0.5 0.6 0.7 0.8 0.9 1

Agility

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

Agility (r=0.1)

Agility (r=0.2)

Agility (r=0.3)

Agility (r=0.4)

Agility (r=0.5)

Agility (r=0.6)

Agility (r=0.7)

Agility (r=0.8)

Agility (r=0.9)

Fig. 88.26 CNO agility (A) under variable values for un-

expected disturbance (P) and recovery (r) probabilities for

a speciﬁc case (after [88.77])

knowledge in a timely manner without being cost pro-

hibitive, and (3) enable the development of emergent

networks. Emergent networks are deﬁned as evolu-

tionary mechanisms of interaction which build upon

the well-established theories of organizational learning

and that are characterized by ad hoc decisions, effec-

tive improvisation, on-the-spot creation of contacts, and

best-matching protocols for pairing system alerts with

decisions, decision-makers, and the executors of the de-

cisions (Figs. 88.27 and 88.28).

The principle was originally developedby Velásquez

and Nof [88.76] to enable organizations to answer the

questions of (1) whom to contact, (2) under what cir-

cumstances to begin a contact, (3) when to contact,

and (4) how to begin a contact under time pressure.

A number of systems have already been developed

which could be further enhanced by this collabora-

tive e-Work principle. Examples are (1) SACHEM,

which is a large-scale knowledge base for monitor-

ing, alerting, and diagnosing dynamic processes in blast

furnaces [88.95, 96], and (2) the public-health emer-

gency response information system (PHERIS), which

is a system developed to facilitate disease surveillance,

command center for detection, collection of data and

timely reporting, and resource planning and allocation

for emergency response [88.97].

e-Measures: Criteria and Evaluation

With the advent of new work models, the need for

new performance criteria and measures to assess the

Part I 88.3

Collaborative e-Work, e-Business, and e-Service 88.3 Design Principles for Collaborative e-Work, e-Business, and e-Service 1569

Unit cost = 1

Unit cost = 2

Unit cost = 3

Unit cost = 4

123456

Economic value of a match (EVM) (cost units)

Supplier

–1

–2

–3

Fig. 88.28 Economic value of a match to identify which

supplier out of six to match to a given customer ac-

cording to the costs to manufacture and match parts

(note: parts were generated following a normal distribu-

tion)

effectiveness of e-Work has increased. With the evo-

lution of computer-integrated manufacturing (CIM)

and networked enterprises, several measures have

emerged, among them: ﬂexibility, connectivity, agility

(Fig.88.29), integration ability, scalability, and reacha-

bility. Two recent performance measures developed at

the PRISM Center are viability and autonomy [88.90].

Viability in the e-Work context has been deﬁned as

the ratio between operating and sustaining agents, and

the rewards or beneﬁts from their services. Autonomy

implies the level of authority delegated and decen-

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9

System agility

0.5

0.45

0.4

0.35

0.3

0.25

0.2

0.15

0.1

0.05

p = 0.1

p = 0.2

p = 0.3

p = 0.4

p = 0.5

p = 0.6

p = 0.7

p = 0.8

p = 0.9

Fig. 88.29 System agility under various error probabilities

p and recovery probabilities r (after [88.92])

tralization, and how active the agents, protocols, and

other e-Work participants are. The development of new

measures has not stopped and some of the emerging

measures include: learning ability, collaboration abil-

ity, error and conﬂict detectability, error and conﬂict

severity, and information signiﬁcance.

The implications of the new measures of collab-

orative e-Work performance and characteristics have

also been studied with the development of agent design

and interaction theories. Anussornnitisarn [88.51] con-

cludes that the performance of viability-based protocols

exceeds that of protocols that did not take viability into

consideration.

88.3.2 Emerging Collaborative e-Work,

e-Business, and e-Service Design

Principles

Bioinspired Collaborative Control

As the synergies between areas of research continue to

evolve so will the theories and foundations of e-Work.

In recent years, the biological sciences have started to

inﬂuence collaborative control in e-Work, e-Business,

and e-Service [88.98, 99]. Animal-to-animal interac-

tions such as those observed among ants and bees have

been adapted to enable the coordination of agents. The

limited rationalizing capabilityof insectsis enhanced by

their abilityto use pheromones as hintsand therefore re-

duce information overloads. Similarly, in collaborative

networks, the need to reduce complexity yet provide the

appropriate hints to enable the agent to make a deci-

sion requires that only local and valuable information

be shared by agents [88.100].

Bioinspired applications have included virtual in-

sects (ants) as mobile software agents, message-based

interactions among agents and robots, and measures

such asviability (survivability) and autonomy (indepen-

dence). Negotiation-based and bioinspired techniques

are just two of the many useful techniques that have

and will continue to inﬂuence multiagent collaborative

control, as seen in Fig.88.30.

Learning and Adaptation

in Collaborative Control

Learning and other similar adaptive methodologies

have played and will continue to play a critical role

in automation and production/enterprise control. Ge-

netic algorithms have been used, for instance, for

the scheduling of complex products with multiple re-

source constraints and a multitier product structure

(Fig.88.31). The algorithms developed by Pongcharoen

Part I 88.3

1570 Part I Home, Ofﬁce, and Enterprise Automation

Arrival of

order 1 at

time t

*Op1

*Op2

*Op3

*Op4

*Op3

*Op2

{R1-R3-R5-R6}

{R1-R3-R4-R6}

{R1-R2-R4-R6}

Nominated

solution 1

Time

Nominated

solution 2

Time

Nominated

solution 3

Time

Fig. 88.30 Exploring mechanism of an ant agent in multiagent manufacturing control systems (after [88.102])

Population Genetic operation

Crossover operation

Mutation operation

Repair process

Chromosome

Start

Solution

space

Stop

Terminate?

Next generation

Decoding

Chromosome

selection

Yes

Encode

Randomly

Combine

Genes

Chromosome

Random

selection

Chromosome

Offspring 1

Parent 1

Offspring 1

Parent 2 Offspring 2

Parent 3 Offspring 3

Fitness

testing

Check and reorder

components

Check and reorder

operations

Identify and avoid

deadlock

Check capacity

and adjust timing

Roulette

wheel

Fitness measure

Offspring 2

Offspring 3

Fig. 88.31 Genetic algorithm structure for a production scheduling scenario (after [88.101] with permission from Else-

vier)

et al. [88.101] achieve on-time delivery and a 63% re-

duction in costs when compared with simulation results

produced by a participating company.

In another work that involves decentralized and

interactive learning, Lagrangian relaxation in combi-

nation with genetic algorithms was used to coordinate

Part I 88.3

Collaborative e-Work, e-Business, and e-Service 88.4 Conclusions and Challenges 1571

Fig. 88.32a–f Bioinspired network models of e-Work,

e-Service, and e-Business are developed.

(a) Channels (ar-

teries) modify their shape and plasticity over time;

(b) the

surface of channels changes dynamically and may have

multiple internal layers;

spatial orientation;

(d) time-variable electromagnetic chan-

nels waves as observed via nanowires in mice;

(e) network

learning: structural plasticity has a role in behavior – chan-

nel rigidity allows free ﬂow versus channel blockages

which constrict ﬂow;

(f) network learning: rigid versus

ﬂexible channels (after [88.9])



and optimize the production planning of indepen-

dent partners linked by material ﬂows in multitier

supply chains [88.103]. Adaptation and learning are

effective methodologies in market-based resource al-

location to satisfy customer orders and needs [88.104,

105]. Studies at several laboratories around the

world have found that adaptation by agents per-

forms better at allocating and balancing resources and

tasks.

Bioinspired Network Models

Recent work in biomembranes [88.106], neurology

[88.107], and other areas reveals the potential of

bioinspired mechanisms for adaptation, learning, and

collaborative control. Just as synapses and spines col-

laborate to achieve survival and competitive animal

behaviors, so can human-made enablers of collabora-

tive control, such as adaptivity, dependability, discovery

by data mining, decision support systems, and active

middleware. The inﬂuence of biological collaborative

control is still in its infancy and it is expected that

new discoveries in these areas will lead to new mod-

els, theories, and solutions that satisfy the ever-growing

needs of optimizing e-Work, e-Business and e-Service

(Fig.88.32).

Surface layers

Orientation

FlexibleRigid

F(t

)

(t)

(t)–F

(t) F

(t)>F

(t)

F(t

)

Tube channel

surface properties

)

88.4 Conclusions and Challenges

The methods by which humans, machines and com-

puters interact and collaborate are rapidly being trans-

formed in different areas of the economy, i.e., business,

commerce, education, logistics, etc. Many of the ben-

eﬁts of e-Work, e-Business and e-Service have been

the direct result of the automation of collaborative

processes, activities and interactions that individuals

and enterprises conduct in a day-to-day basis, among

them:

1. Error detection, reduction, and prevention:the

automation of processes enables enterprises to de-

velop error mitigation and reduction strategies that

guarantee better service, cost reduction, and faster

response.

2. Cost reduction of business and service processes:

the automation of business processes allows an en-

terprise to operate 24h a day, 7days a week without

having employees work during some of the shifts,

Part I 88.4

1572 Part I Home, Ofﬁce, and Enterprise Automation

therefore reducing labor expenses. In addition, op-

erational savings on items such as business forms

and ofﬁce supplies also reduce the costs of business

services.

3. The ability to collaborate more effectively:being

able to access electronic business information and

knowledge from anywhere, and anytime, has en-

abled enterprises to collaborate along the entire

spectrum of the supply networks to provide all

members of the network with just the information

they need to work towards similar goals.

4. Enhanced supply network management:theau-

tomation of many of the business activities and

processes that enterprises conduct has enabled the

creation of more agile, ﬂexible, and responsive net-

works, which in turn have led to the development of

a more collaborative and responsive environment.

5. Focus on added-value activities: the automation

of processes and activities has enabled enterprises

to make better use of their resources (e.g., man-

power, equipment) to reduce waste and idle time,

and concentrate all their efforts on those tasks that

contribute to the value of their products and ser-

vices.

The future of collaborative e-Systems is promising

and will require the exploration of different areas and

the deﬁnition of further collaborative theories, princi-

ples, methodologies, and implementations:

•

Deﬁne and develop the foundation for collabora-

tive control theory to address many of the issues

described in previous sections by ﬁnding synergies

between existing control theories and other areas

such as biological systems (see Chap.75, Robust

Control in Biological Systems).

•

Expand the breadth of investigation and imple-

mentation of collaborative e-Work principles and

methodologies to not only beneﬁt and better enable

large enterprises to compete and excel in the e-

World but also small and medium-sized enterprises

(SMEs). For example, in the book by Corbitt and

Al-Qirim [88.108], studies on the implications of e-

Business and e-Commerce principles to SMEs are

summarized and their challenges are highlighted.

•

Newways of working and interacting in thee-World.

For instance, education in an e-enabled world (e-

Learning and e-Training) requires the development

of new skills, interfaces, teaching methods, and ar-

chitectures so that activities such as e-Mentoring,

e-Training, and others can yield the desired ed-

ucational outcomes [88.109]. More information

on e-Learning, e-Training, and serious games for

teaching and learning can be found in Chaps. 85

(Automation in Education/Learning Systems)and

73 (Automating Serious Games), respectively.

•

As organizations expand and become part of larger

networks many questions regarding optimal mem-

bership, clustering, and performance measures

(i.e., functional, economic, and quality) still re-

main to be addressed. Inﬂuenced by areas such

as bioinspired networks, artiﬁcial intelligence, and

bioinspired control, collaborative control will ad-

dress these questions and move e-Work, e-Business,

and e-Service into new frontiers.

•

It is necessary to deﬁne and assess new mea-

sures for production, service, and productivity to

better understand and improve the emerging e-

World environment. Examples of such measures

are collaboration-ability, recoverability, detectabil-

ity, and more.

88.5 Further Reading

•

L.N. de Castro: Fundamentals of Natural Comput-

ing: Basic Concepts, Algorithms, and Applications

(Chapman Hall/CRC, New York 2006)

•

H.M. Deitel, P.J. Deitel, K. Steinbuhler: e-Business

and e-Commerce for Managers (Prentice-Hall, New

Jersey 2001)

•

P. Harmon, M. Rosen, M. Guttman: Developing

e-Business Systems and Architectures: A Manager’s

Guide (Academic, San Diego 2001)

•

S. Johnson: Emergence: The Connected Lives of

Ants, Brains, Cities and Software (Scribner, New

York 2002)

•

M. Kevin, K.M. Passino: Biomimicry for Optimiza-

tion, Control, and Automation (Springer, London

2005)

•

M.A. Lara, J.P. Witzerman, S.Y. Nof: Facility

description language for integrating distributed de-

signs Int. J. Prod. Res. 38(11), 2471–2488 (2000)

•

P.B. Lowry, J.O. Cherrington, R.R. Watson (Eds.):

The e-Business Handbook (St. Lucie, New York

2002)

•

C. Ma, A. Concepcion: A security evaluation model

for multi-agent distributed systems. In: Technolo-

gies for Business Information Systems, ed. by

Part I 88.5

Collaborative e-Work, e-Business, and e-Service References 1573

W. Abramowicz, H.C. Mayr (Springer, New York

2007) pp.403–415

•

S. McKie: e-Business Best Practices Leveraging

Technology for Business Advantage (Wiley, New

York 2001)

•

R.T. Rust, P.K. Kannan (Eds.): e-Service: New Di-

rections in Theory and Practice (Sharpe, New York

2002)

•

University of Michigan Digital Library Project:

http://www.si.umich.edu/UMDL/

•

J.-K. Won, K.Kwang-Hoon:Global-workﬂowmod-

eling methodology and system for workﬂow-driven

e-Commerce, Adv. Commun. Technol. 2, 1012–

1017 (2004)

References

88.1 A. Rosen: The e-Commerce Question and Answer

Book (American Management Association, New

York 2002)

88.2 J. Browne, P. Higgins, I. Hunt: e-Business prin-

ciples, trends and visions. In: e-Business Appli-

cations Technologies for Tomorrow’s solutions,ed.

by J. Gasós, K.-D. Thoben (Springer, New York 2003)

pp. 3–16

88.3 S.Y. Nof: Design of effective e-Work: Review of

models, tools and emerging challenges, Product.

Plan. Control, Special Issue on e-Work: Models and

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88.4 L.M.Camarinha-Matos,H.Afsarmanesh:Collab-

orativenetworks:Valuecreationinaknowledge

society, Proc. PROLAMAT 2006, IFIP Int. Conf. Knowl.

Enterp. – New Challenges, Shanghai (Springer

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88.5 L.M. Camarinha-Matos, H. Afsarmanesh: Concept

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and Virtual Organizations,ed.byG.Putnik,

M. Cunha (Hershey, Pennsylvania 2008)

88.6 S.Y. Nof: Information and Collaboration Models of

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88.7 A. Cassidy: A Practical Guide to Planning for e-

Business Success: How to e-Enable Your Enterprise

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88.8 S.Y. Nof: Modeling of e-Work with TIE, a team-

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88.10 S.Y. Nof: Availability, integrability and depend-

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88.11 S.Y. Nof, F.G. Filip, A. Molina, L. Monostori,

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88.12 A.W. Colombo, F. Jammes, H. Smith, R. Harrison,

J.L.M. Lastra, I.M. Delamer: Service-oriented ar-

chitectures for collaborative automation, Proc. 31st

Annu. Conf. IEEE Ind. Electron. Soc. (2005)

88.13 D.J. Bruemmer, D.A. Few, R.L. Boring, J.L. Marble,

M.C. Walton, C.W. Nielsen: Shared understand-

ing for collaborative control, IEEE Trans. Syst. Man

Cybern. A 35(4), 494–504 (2005)

88.14 T. Fong, S. Grange, C. Thorpe, C. Baur: Multi-robot

remote driving with collaborative control, Proc.

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88.15 M. Klusch: Agent-mediated trading: Intelligent

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pp. 1–31

88.17 J. Ferber:

Multi-Agent Systems: An Introduction to

Distributed Artiﬁcial Intelligence (Addison Wesley,

Longman 1999)

88.18 S.S. Fatima, M. Wooldridge: Adaptive task and re-

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Part I 88