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Collaborative e-Work, e-Business, and e-Service 88.2 Theoretical Foundations of e-Work and Collaborative Control Theory (CCT) 1555

e-Business [88.23, 24], healthcare [88.25, 26], trans-

portation [88.27], construction [88.28], and more.

88.2.6 Computer-Integrated

Manufacturing

Computer-integrated manufacturing (CIM) enables the

harmonized integration and management of the entire

product lifecycle fromdesign tocustomer service.Mod-

ern business is centered on intra- and intercollaboration

processes, such as supply chain/network management,

customer relationship management, employee–business

integration, and more. To fully garner the beneﬁts of

this high level of collaboration, organizations need to

implement integrated-enterprise systems. For instance,

Ho and Lin [88.29] focus on the need for better col-

laboration throughout the complete business cycle and

not just focusing on technical integration. In their work,

they incorporate critical success factors (i.e., imple-

mentation strategy, change management, etc.) into the

development of the different stages and components

of an integrated-enterprise system (Fig. 88.9). Choi

et al. [88.30] also develop an integrated enterprise ar-

chitecture, but their focus is on virtual enterprise chains.

Suppliers

and

business

partners

Customers

business

partners

Medium of

interaction

Marketplace

exchange

Marketplace

exchange

Face

to face

Internet

Supplier relationship

management system

Business applications integration infrastructure

Shop-floor integration infrastructure

Customer relationship

management system

Engineering

support systems

Database and

network systems

Manufacturing

execution system

Business

support systems

Manufacturing

support systems

The enterprise lineThe enterprise line

FMS = Flexible Manufacturing Systems; SCM = Supply Chain Management; WH = Warehouse; ERP = Enterprise Resource Planning; EIS = Enterprise Information System;

HR = Human Resources; KM = Knowledge Management; AGVs = Automated Guided Vehicles; CMS = Content Management System; PCBA = Printed Circuit Board Assembly

The extended enterprise

CIM

modules

Mobile

devices

SCM

CNC machines

PCBA

Robots

FMS

AGVs

Test equip

CMS

Packing

Data

ERP EIS HR KM

Telephone

Medium of

interaction

Face

to face

Internet

Mobile

devices

Telephone

Fig. 88.9 The integrated enterprise systemreference architecture (after[88.29] withpermission from Taylor and Francis)

Efforts such as these demonstrate the revolution that the

collaborative enterprise world is experiencing with the

evolution of collaborative e-Work principles, theories,

and methodologies.

88.2.7 Human–Computer Interaction

Human–computer interaction (HCI) enables the de-

velopment of systems, platforms, and interfaces that

support humans in their roles as learners, workers, and

researchers in computer environments. The majority

of the work being conducted in HCI has focused on

the methodologies and processes for developing in-

terfaces, the design, implementation or evaluation of

interfaces, and the development of theories and pre-

dictive models of interaction. As the roles of humans

in today’s workplace and society continue to evolve

with the development of new technologies, so will

the focal areas for HCI, among them ambient in-

terfaces, affective computing, sensing interfaces, and

tangible user interfaces [88.31]. For instance, in the

work by Altuntas et al. [88.32], the authors present

a formal approach to include a human material handler

in a computer-integrated manufacturing (CIM) system.

Part I 88.2

1556 Part I Home, Ofﬁce, and Enterprise Automation

Fig. 88.10 Microsoft Surface (human–computer interface)

for Sheraton Hotels (courtesy of Microsoft)

As the authors point out, most of the work in control

has focused on automata-based methods for complex

computer-controlled systems, whose application has

been limited as the number of unmanned systems is

relatively small.

HCI applications are starting to cross into the

mainstream; for instance, Microsoft recently released

Surface, an interface that enables users instant access to

information and entertainment via their hands and ges-

MRP, MRP II

Digital business in extended virtual enterprises

Extended product in dynamic enterprises

Four walls of a plant

Supporting the extended enterprise

Production planning

Manufacturing DistributionMarketing and salesSubsuppliers Suppliers CAD/CAM

Reuse

Disposal

Maintenance

Operation

e-Channels Best practicesDigital market places

JIT = Just in Time; MRP = Material Requirement Planning; CAD/CAM = Computer-aided Design/Computer-aided Manufacturing; OPT = Optimized Production Technology

Business to business Business to consumer

JIT, OPT, etc.

Reverse

logistics

Customer

driven design

Supply chain

management

Customer

relationship

management

Co-design

product

models

Design for X

Product and

process design

Production

planning and

control

Fig. 88.11 Extended products in dynamic enterprises (EXPIDE) architecture (after [88.2])

tures while improving collaboration and empowering

consumers in the decision-making process (Fig.88.10).

88.2.8 Extended Enterprises

Extended enterprises enable the creation of architec-

tures, models, and methodologies to integrate business

processes that go beyond the limits of a single enter-

prise and which include end-to-end functions, internal

operations, and entire supply networks. The level of

integration and connectivity of all value areas of sup-

ply networks has signiﬁcantly increased with the advent

of e-Business, e-Commerce, e-Services, and global-

ization. This trend has also increased the demand for

new e-Work and e-Business applications and solu-

tions to address the challenges faced when the world

around us becomes an e-World. Figure 88.11 presents

the transformation from traditional enterprises into ex-

tended enterprises, as deﬁned by the extended products

in dynamic enterprises (EXPIDE) project cluster from

researchers of the European Information Society Tech-

nologies. The transformation of the enterprise serves as

an example of the challenges and needs for the study

and development of new methodologies, performance

measures, and tools in the e-World.

Part I 88.2

Collaborative e-Work, e-Business, and e-Service 88.2 Theoretical Foundations of e-Work and Collaborative Control Theory (CCT) 1557

The third theoretical foundation area is distributed

decision support and comprises three e-Dimensions:

(1) decision models, (2) distributed control systems,

and (3) collaborative problem-solving, as described

below.

88.2.9 Decision Models

Decision models integrate computer tools and method-

ologies in the implementation of decision theories in

online decision support systems, to provide more ef-

fective and better-quality decisions. The development

and implementation of decision models to function in

the e-world spans a wide spectrum. For instance, in

e-Finance, one model called create–collect–manage–

protect (CCMP) extends the ﬁnance framework from

solely providing the technology perspective to one

that better enables strategic decision-making by mak-

ing use of enterprise application integration (EAI) with

common object request broker architecture (CORBA)

and web services to demonstrate the added value

of using a network approach to the digitalization of

ﬁnancial and business content [88.34]. In a differ-

Administration module

Physical links

Finance OthersMarketing/salesCustomers

Physical links

ORB/IIOP

Quality-centric module

IIOP = Internet Inter-ORB Protocol; ORB = Object Request Broker; DBMS = Database Management System; SPC = Statistical Process Control;

GUI = Graphical User Interface; FMEA = Failure Modes and Effects Analysis

Purchasing

Gloal-based

subcontractors

Production

engineering

Quality

assurance

Product

design

Specifications

Other tools

FMEA

QIS GUI

SPC

Raw data

DBMS/workflow

manager module

IT support

Human

resources

Production

planning

LogisticsSuppliers

Fig. 88.12 Online quality information system (OQIS) reference architecture (after [88.33])

ent application, fuzzy analytic hierarchy and fuzzy

Delphi methodologies are used for multicriteria eval-

uation of e-Marketplace selection to deal with the

uncertainty and vagueness of humans in group deci-

sion process, and therefore guarantee more effective

decision making [88.35]. In a different application

area, e-Manufacturing, an online quality information

system (OQIS) using an object broker architecture

along with a client–server enables its users both

to obtain real-time data and information from any

location and to address complex quality and deploy-

ment costs decisions (Fig.88.12). The three examples

of decision models described, along with their ap-

plications, highlight the inﬂuence and evolution of

decision models needed in e-Business, e-Works and

e-Service.

88.2.10 Distributed Control Systems

Distributed control systems empower systems with

a greater degree of autonomy by enabling all parties

(e.g., members of a supply network) to negotiate for

the assignment/allocation of tasks and their respective

Part I 88.2

1558 Part I Home, Ofﬁce, and Enterprise Automation

Fig. 88.13 SmartGridCity by Xcel Energy integrates high-speed communication technologies with the electric grid. The

system enables real-time control by customers using devices installed at their homes to automate their home energy use

and the use of integrated infrastructure such as plug-in hybrids, battery systems, solar panels, and more (courtesy of Xcel

Energy)

Integrated process control solutions (ProcessLogix“)

PC-based control (A-B Open Controller“, SoftLogix“)

Motion control (A-B GMC“, CNC“)

I/O systems (A-B FLEX“ I/O, FLEX Ex“, ArmorBlock I/O“)

Information network (Ethernet

)

Device network (DeviceNet“)

Control and SCADA networks (ControlNet“; A-B DF1“,

DH+“, DH485, Remote I/O)

Programming software (Rockwell software RSLogix 5“,

RSLogix 500“, RSLogix 5000“)

Modular control (A-B SLC“, PLC

, ControlLogix“; Reliance electric AutoMax“)

MMI software (Rockwell software RSView32“, RSTools“; Reliance electric SIGMA“)

AC and DC industrial motors (A-B 1329R, 1329L, 1325DR, 1365, 1395, 1397;

Reliance electric DutyMaster

, E-Master

, RPM“ AC, RPM“ III, IQ intelligent“, XE“)

Variable speed AC and DC drives (A-B SSC“, 1305, 1336 PLUS II, FORCE“,

1336 IMPACT“, 1397; Reliance electric GV3000/SE“, FlexPak

3000, SA3100, SD3000)

Drive systems (Rockwell automation)

Gearing (DODGE TORQUE-ARM“, TIGEAR“, MAXUM

, MASTER XL“, REEVES“,

ULTIMA“, CST“, CYCLONE“, QUANTIS“)

Bearings (DODGE SLEEVOIL“, UNIFIED“, SAF-XT, Plummer block, IMPERIAL“,

S-2000, D-LOK“, SC, SCM)

Motor control centers (A-B CENTERLINE

)

Supportive technologies

Core technologies

Communications software (Rockwell software RSLinx“)

Process and control software (Rockwell software

RSBatch“, RSLogix Frameworks“)

Information software (Rockwell software RSSql“)

Sensors (A-B Photoswitch

, Photoswitch Series 9000,

Photoswitch 10000; DODGE EZLINK“)

Switches (A-B safety and limit switches)

Encoders and resolvers (A-B)

Push buttons and signalling (A-B RediSTATION“,

RediPANEL“, Control tower“ stack lights)

Message displays (A-B Dataliner“, MessageView“,

MessageBuilder“)

Graphic terminals/industrial computer products

(A-B PanelView“, PanelBuilder“, RAC 6000 Series)

Low-voltage motor management (A-B MCS“,

SMM, SMC, SMP, contactors, overload protection)

Power quality and automation

(A-B Powermonitor II, Line Synchronization Module“)

Power transmission components (DODGE D-Flex,

PARA-FLEX“, GRID-LIGN“, FLEXIDYNE“,

Mine Duty EXTRA“)

Medium voltage control (A-B AC drives, starters, SMC)

Encompass™ referenced

Products/technology

¥ MES

¥ Weigh scales

¥ Surge protection

¥ Vision products

¥ Wireless modems

Iron ore

Limestone

Pellets

Sinter

Blast

furnace

Direct

reduction

Crushed

Coal

Slag

Molten iron

Coke ovens

Oxygen

Oxygen furnace

Lime & flux

Ladle

metallurgy

Scrap

Electric

furnace

Slab

Hot strip

Continuous casting

Bloom

Billet

Steel platesPlate

Skelp

Structural shapes

Rails

Seamless pipe

Tube rounds

Rods

Wire & wire

products

Cold drawn

barsHot rolled bars

Welded pipe

Slitting, cut-to-length galvanizing,

and other coated flat-rolled products

Temper rolling

Annealing

Cold strip

Pickling

& oiling

Fig. 88.14 Distributed control system in the steel industry (courtesy of Rockwell Automation, Inc.)

individual and group reward (incentives). Distributed

control systems are common in manufacturing sys-

tems and in other processes in which the control

is more effective and responsive when it is not lo-

cated centrally but rather is distributed throughout

the system, with local components being controlled

by one or more controls. Typical examples of dis-

tributed systems include: electrical power grids and

Part I 88.2

Collaborative e-Work, e-Business, and e-Service 88.2 Theoretical Foundations of e-Work and Collaborative Control Theory (CCT) 1559

power plants (Fig.88.13), water management systems,

oil reﬁning systems, sensor networks, pharmaceuti-

cal manufacturing, cement process (Fig.88.14), and

more.

88.2.11 Collaborative Problem-Solving

Collaborative problem-solving systems provide thenec-

essary information exchange methodologiesand models

to inﬂuence parties (e.g., members of a supply net-

work, or emergency response network, Fig.88.15)in

their decision-making processes so as to achieve har-

monization, coherence in decisions, and better results

(e.g., ﬁnancial, customer service rankings) for all in-

volved parties. For example, in the work by Farand

et al. [88.37] the authors examine collaborative clinical

problem-solving in the context of telemedicinal con-

sultations in order to assess the degree to which the

technological environment preserves medical reasoning

under traditional settings. A theoretical framework and

case study were implemented to measure the achieve-

ment of the goals deﬁned. Another example in the

medical ﬁeld applies Bayesian networks to model in-

dividual student and group knowledge to better deﬁne

problem-based learning (PBL) methodology for the

teaching of clinical reasoning skills. The system de-

veloped, collaborative medical tutor (COMET), is an

intelligent tutoring system for collaborative medical

PBL [88.38].

Assets

manager

VOD

DVD

Beta

Camera

Linear TV Livecost

Advertising Affiliates

Web 2.0

elements

Content

aggregator

UGC CRM

Encoding DRM Reports

Business

manager

AD server

API

CRM

Content source

Playlist

Media serverCDN Encoder

e-Payment

InternetInternet

Web interface

DRM = Digital Right Management; VOD = Video on Demand; API = Application Programming Interface; PDA = Personal Digital Assistant;

CDN = Content Delivery Network; CRM = Customer Relationship Management; AD = Advertising

Set-top box

PDA

V-ware™

Fig. 88.16 v-Ware middleware architecture (printed with permission from Best TV Video Management Solutions)

Fig. 88.15 Mock/drill training interface for collaborative and dis-

tributed problem solving. The tool was designed to train trans-

portation employees for emergency response. Trainees are able to

interact with different users, see their actions, create their own

action plans and decisions, request resources, and have access to

a variety of organizational information needed to address the task at

hand (after [88.36])

The fourth theoretical foundation area is active

middleware and comprises four e-Dimensions: (1) mid-

dleware technology, (2) distributed knowledge systems,

(3) grid computing, and (4) knowledge-based systems,

as described below.

Part I 88.2

1560 Part I Home, Ofﬁce, and Enterprise Automation

88.2.12 Middleware

Middleware technology enables interoperability among

distributed, heterogeneous applications, including leg-

acy and advanced environments. Middleware is a layer

of services or applications that provides the connectiv-

ity between the operating systems and network layers,

and the application layer. For instance, in its basic op-

eration, middleware enables the smooth connectivity

and communication, and therefore functionality, be-

tween an older computer and a new scanner, printer

or other peripheral components. In more advanced ap-

plications, middleware enables interoperability among

more complex systems and networked organizations.

Middleware is classiﬁed into the following categories

of its e-Services according to the functions they pro-

vide: (1) distributed tupleswhich function as distributed

relational databases, the most widely deployed middle-

ware, (2) remote procedure call (RPC), which enables

programmers to invoke a procedure across a network,

(3) message-oriented middleware (MOM), which pro-

vides access to a message queue across a network, and

(4) distributed object middleware, which provides the

abstraction of an object that is remote but whose meth-

ods can be recalled just like those of an object in the

same local address as the user. An example of a mid-

dleware technology concerning video applications is

V-Ware, a middleware solution for online video ser-

vices that supports video on demand (Vo D), linear TV,

user generated content (UGC), and live broadcasting,

and is able to distribute it across diverse networks of

TV, personal computers (PCs), and portable devices

(Fig.88.16).

88.2.13 Distributed Knowledge Systems

Distributed knowledge systems are a collection of

autonomous knowledge-based objects sometimes also

referred to as knowledge agents. In collaborative envi-

ronments, agents (Sect.88.2.2) interact with each other

and work together to evolve the knowledge needed to

improve decisions or solve queries, based on their re-

spective abilities. In the work by Ho et al. [88.39]

for instance, the authors develop a distributed knowl-

edge model for knowledge management to support

the development and implementation of enterprise

systems involving information, system, and software-

engineering technologies. The developed knowledge

systems need to be updated because they are required to

maintain high reliability, integrity, andquality of service

for large-scale implementations.

Fig. 88.17 View inside a server tape Storateck in the com-

puter center for the grid technology of the Large Hadron

Collider (courtesy of CERN)

88.2.14 Grid Computing

Grid computing is a term that evolved in the mid

1990s to denote well-organized computing services

that enable rapid sharing, selection, and aggregation of

a wide variety of geographically distributed comput-

ing resources for solving large-scale and data-intensive

computing applications. A computational grid provides

dependable, pervasive, consistent, and inexpensive ac-

cess to high-end computational resources [88.40]and

enables its users to perceive heterogeneous resources as

homogeneous. The creation of virtual (v-)environments

(i.e., v-Factories, v-Organizations, etc.) requires the de-

velopment of the necessary grid technology [88.41].

One application of grid technology that has garnered

and will continue to capture people’s attention is the

Large Hadron Collider (LHC)atCERN, the European

Organization for Nuclear Research. The LHC comput-

inggrid(LCG) will be one of the largest data-intensive

applications in the world, combining and analyzing

Part I 88.2

Collaborative e-Work, e-Business, and e-Service 88.2 Theoretical Foundations of e-Work and Collaborative Control Theory (CCT) 1561

Table 88.1 MeDICIS components (after [88.42], with permission from Elsevier)

Levels of Components Uniﬁed modeling Method Other sources

MeDICIS of MeDICIS language (UML)foranalyzing of inspiration

element and structuring

knowledge

(MASK) elements

Macro Business model Case diagrams; Uniﬁed modeling

Class diagrams methodologies (UMM),

Uniﬁed enterprise modeling

language (UEML),

Universal description discovery

and integration (UDDI)

Cooperation model Case diagrams; UEML

Class diagrams

Agent model Class diagrams Common knowledge acquisition

Micro Communication Class diagrams; and documentation structuring

model Sequence diagrams (KADS)

Multiagent systems (MAS)

Coordination model Concept model; Structured analysis and design

Task model technique (SADT), UML,

MASK; workﬂow: Integrated

deﬁnition methods (IDEF3)

Collaborative Concept model;

problem solving Task model

(CPS) model

Tool Case diagrams; Others

speciﬁcation Component diagram;

Deployment diagram

data from the LHC detectors for over 10 000 scientists

and over tens of thousands of computers around the

world (Fig. 88.17). The computing grid will sift through

petabytes (a million gigabytes) of particle collision data

to uncover clues about the origins of the Universe.

From a business and service point of view, Brocke

et al. [88.43] discuss the alignment of business needs

of today’s organizations and the availability of grid

technology to better support virtual organizations. The

authors argue that the collaborative reference model is

a promising technique to enable the previously men-

tioned alignment by supporting distributed agents and

the integration of technological, methodological, and

organizational aspects.

88.2.15 Knowledge-Based Systems

Knowledge-based systems provide the capabilities to

mine, reﬁne, construct, and extract information from

a variety of sources, enabling its transformation into

knowledge capital. For instance, Boughzala [88.42]

introduces the methodology for designing interenter-

prise cooperative information system (MeDICIS), an

information system that models cooperative processes

at three levels (communication, coordination, and

collective problem-solving) in order to manage the

knowledge available and generated by the cooper-

ation. The development of MeDICIS highlights the

existing interconnections between the many differ-

Part I 88.2

1562 Part I Home, Ofﬁce, and Enterprise Automation

ent dimensions of e-Work, as presented before, as it

integrates, to name just a few (1) agents, (2) collab-

orative problem-solving, and (3) extended enterprises

(Table 88.1).

88.3 Design Principles for Collaborative e-Work, e-Business,

and e-Service

The ﬂattening of the world [88.44] has signiﬁcantly

changed the ways in which business and commerce

are conducted in the new world landscape. To a large

extent, this has been enabled by automation, from

communication and digital information exchange to

extended supply and logistics networks, international

transportation, and more. As a result of the ﬂatten-

ing effect, work has become more distributed and

decentralized and the magnitude and distribution of

worldwide markets has signiﬁcantly increased. These

changes have brought opportunities for the develop-

ment of better e-Work methods,augmentation of human

physical, cognitive, temporal, and location abilities at

work. However, justas theopportunities haveexpanded,

so have the challenges and complexities associated with

e-Work, particularly the scalability of workﬂow, infor-

mation, and task overload. A number of projects that

have attempted to understand and address these new re-

quirements and challenges, and in turn develop useful

theories and solutions, are summarized in Table 88.2.

88.3.1 e-Work Design Principles

The growing number of distributed and decentralized

work systems over the last two decades has given

e-Work the foundation for the exploration and devel-

opment of new theories, models, and methodologies.

The environment in which e-Work research has evolved

has shown the complex needs and challenges that lie

ahead for the effective developmentand implementation

of e-Work. e-Work brings new and exciting oppor-

tunities for the deﬁnition of emerging and enhanced

work methods and systems, as well as better outcomes

and higher yields, by augmenting with e-Support the

physical, cognitive, temporal, and locational human

abilities. Research on collaborative e-Work, conducted

at the Production, Robotics, and Integration Software

for Manufacturing and Management (PRISM) Center

at Purdue University to understand the needs and chal-

lenges, and develop appropriate theories, and solutions

is highlighted in Table 88.2. In the table, many of the

15 dimensions introduced in Sect.88.2 are addressed

in various manufacturing, production, and service ap-

plication domains. This research has been rooted in

successful, proven principles and models of heteroge-

neous, autonomous, distributed, and parallel computing

and communication.

Several collaborative e-Work design principles,

which have emerged over the years as useful results

of the collaborative control theory, are described in this

section.

Principle of Cooperation Requirement Planning

(CRP)

Originally developed by Rajan and Nof [88.45,46], this

principle considers collaboration as one of the most

powerfulaugmentations ofwork abilities.Collaboration

can be measured in a spectrum from minimal informa-

tion sharing and exchange (implying a certain degree of

cooperation) to fully integrated and collaborative enter-

prises. In order to achieve effective collaboration in any

portion of the spectrum it is necessary to plan ahead.

The principle of cooperation requirement planning in-

cludes two phases: (1) a detailed requirement plan of

who, how, and when (CRP-I) is generated based on

work objectives and available resources; (2) during ex-

ecution, real-time implementation enables the revision

of the plan to CRP-II, which meets spatial and temporal

challenges, changes, and constraints. More recent work

on this principle has included the integration of CRP

with error diagnostics, recovery, and conﬂict resolution

(Fig.88.18) and the use of best-matching protocols for

the identiﬁcation and further implementation of best

policies to address and correct errors and conﬂicts in

robotic assembly and disassembly (Fig.88.19). Future

extensions will need to incorporate fuzzy logic and

learning strategies (i. e., neural networks, genetic algo-

rithms) to further enable real-timechanges andlearning.

The effective implementation of this principle requires

both advanced and adaptive real-time planning in or-

der for the cooperation and collaboration efforts to be

fruitful and efﬁcient.

Principle of e-Work Parallelism

Originally developed by Ceroni and Nof [88.47, 48],

this principle exploits the fact that work and interac-

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 1563

Table 88.2 PRISM Center discoveries of e-Work theories, models, and beneﬁts

CCT guideline Collaborative Principles used Production/business/ Results: tool/model

e-Work beneﬁts service areas

Cooperation Collaboration planning Resource planning Multirobotic assembly CRP [88.45]

requirement

and interaction Multiprocessors CRP [88.46]

planning (CRP)

Multiagents design Agent theory Manufacturing operations ABMS [88.19,49]

Parallelism Collaboration protocol Telecomm. protocols ERP/CRM applications TIE/P [88.50–52]

design Adaptive control Electronics testing TestLAN [88.53]

Comm. protocols Wireless MEMS TIF [88.54]

Exchange protocols Supply networks BMP [88.55]

Middleware protocols Client–servers Automotive electronics RAP [88.56,57]

Flexible assembly TOP [88.58,59]

Parallelism Parallel computing Global shoe design & mfg. DPIEM [88.47,48]

Resource and task Local-area networks Electronics assembly & test TestLAN [88.60,61]

allocation Internet Global automotive supply net. MEN method [88.62]

Conﬂict and error Synchronize/ Agent theory Robotic maintenance ServSim [88.63]

detection and

resynchronize

prevention (CEDP)

Information assurance Total quality Agent-based mfg./service (MERP) [88.64]

Error detection Computer recovery Robotic assembly NEFUSER [88.65]

and prevention Multiagents Multirobot systems EDPA [88.66];

CEPD [88.67]

Fault tolerance Fault-tolerant Sensor fusion Flow MEMS sensors FTTP* [88.68–70]

integration Wireless MEMS sensors TIE/MEMS [88.71]

(operate despite faults)

Conﬂict resolution Telecommunication Facility design by a team FDL [88.72]

Coassembly Multirobot systems FDL-CR [88.73,74]

Assembly/disassembly CRP-CR [88.75]

CRP-BMP [88.76]

Join/leave/remain Multienterprise Network ﬂow Distributed networked MEN Opt. [88.62]

optimization Enterprises JLR [88.77]

Organizational learning Enterprise computing Manufacturing/ CMS [88.78]

assembly corp.

Lines of Workﬂow integration Data ﬂow Aerospace mfg. DFI [88.79]

collaboration

and coordination Distributed database Computer-integrated mfg. DAFNet & AIMIS [88.80,81]

and command

Workﬂow protocols BMP [88.82]

(LOCC)

Information sharing Virtual environments Manufacturing cells FDL [88.83]

and collaboration Task graphs Distributed designers IDM [88.84]

Network computing Distributed teams Co-X Tools [88.85]

Internet/intranet Supply networks Shared Process [88.24]

Construction supply chain Share Process [88.86]

Production and sales T-C-M [88.87]

e-Learning/e-training Learning theory ERP applications MERP/C [88.88]

Distributed DSS Emergency response TSTP [88.36]

Viability measures Artiﬁcial life Human–robot facilities TIE/A [88.89–91]

e-Work scalability Distributed computers Supply networks MEN Opt. [88.62]

Part I 88.3

1564 Part I Home, Ofﬁce, and Enterprise Automation

Table 88.2 (cont.)

ABMS: agent-based management system; AIMIS: agent interaction management system; BMP: best-matching protocol;

CMS: corporate memory system; Co-X: collaborative tool for function X; CEPD: conﬂict and error prediction and detection;

CRP: collaboration requirements planning; DAFNet: data activity ﬂow network; DFI: data activity ﬂow integration;

DPIEM: distributed parallel integration evaluation method; EDPA: error detection and prediction algorithms;

FDL: facility design language; FDL/CR: facility design language/conﬂict resolution;

*FTTP: fault-tolerance time-out protocol (patent pending); IDM: iterative design model; IRD: interactive robotic device;

JLR: join/leave/remain; MEN: multienterprise network; MERP/C: ERP e-Learning by MBE simulations with collaboration;

NEFUSER: neuro-fuzzy systems for error recovery; RAP: resource allocation protocol; ServSim: maintenance service simulator;

TestLAN: testers local area network; TIE/A: teamwork integration evaluator/agent; TIE/MEMS: teamwork integration evaluator/MEMS;

TIE/P: teamwork integration evaluator/protocol; TIF: data information forwarding; TOP: time-out protocol;

TSTP: transportation security training portal

123456789101112131415

b) Cost

Number of iterations

100

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a) Cost

Number of iterations

1000

900

800

700

600

500

400

300

200

100

s = 0.1

s = 0.3

s = 0.5

s = 0.7

s = 0.9

s = 0.1

s = 0.3

s = 0.5

s = 0.7

s = 0.9

Fig. 88.18a,b Comparison of expected costs of conﬂict

resolution

(a) without conﬂict resolution methodology

Mcr (unbounded cost), and

(b) with conﬂict resolu-

tion methodology Mcr (bounded cost) (after [88.92])

(s =rate of conﬂicts)

tions in software and human workspaces can and must

be allowed to run in parallel. To be effective, e-Work

systems cannot be constrained by sequential (linear)

tasks. In e-Work, the principle of workﬂow parallelism

has deeper implications due to the distributed nature

of the e-Activities and the fact that interactions take

place over human and software spaces and can include

(1) human–human interactions, (2) human–machine

and human–computer interactions, and (3) machine–

machine and computer–computer interactions.

Ceroni [88.47] deﬁned the degree of parallelism

(DOP) as the maximum level of resources and task

parallelism necessary to balance the tradeoff between

increased communication, transportation, and equip-

ment costs and the increased productivity gained by

the given level of parallelism. The authors developed

the distributed planning of integrated execution method

(DPIEM) as an effort to satisfy a CRP-based plan

(Fig.88.20). Among the issues explored in the planning

was the ability to determine the optimal DOP, as seen

in Fig.88.21.

In addition to the deﬁnition of DOP,theprinciple

of e-Work parallelism also addresses issues regarding

the design and implementation of coordinated problem-

solving and decision support that are of concern to the

development of active middleware. The principle in-

cludes ﬁve guidelines to let e-Work support systems

(EWSS):

1. Formulate, decompose, and allocate problems

2. Enable applications to communicate and interact

under task administration protocols

3. Trigger and resynchronizeindependent agents to act

coherently and cohesively in addressing problems,

making decisions, and acting

4. Enable agents to reason, interact, and coordinate

with other agents when needed

5. Develop conﬂict resolution, error recovery, and di-

agnostic and recovery strategies.

Part I 88.3