Productivity in the Enterprise through OR-CI Synthesis and Integration

Organizers: Bob Fourer, Steve Wright, Jorge Moore, Karthik Ramani

OR: Suvrajeet Sen

Shared CI: Sangtae Kim

Workshop held in Washington, D.C.August 30-31, 2004

Sponsored by the National Science Foundation

OR as an Infrastructure

OR: Science of Decision making

Strengths Integrates theory, algorithms, and software

Provides modeling and analysis tools

Underlies and facilitates productive activity indesign, manufacturing, services, supply-chain management

. . . serves many purposes not originally envisioned

Weaknesses Lack of standards for interoperability of tools

Limited accessibility to modeling and analysis tools

. . . ad hoc, awkward interfaces

DMII Opportunities

Current Product Development Processes are extremely iterative, communication intensive (data driven), and linear.

Challenge areas to be addressed Enterprise applications in such areas as

design optimization and configuration management

total supply network management

production planning over product lifecycles

simulation of decentralized services

Dynamic representations rather than static

. . . bypassed by Atkins report

Integrating OR within Cyberinfrastructure

Remedies for current weaknesses Create an infrastructure to

enable research collaboration across institutions, locations, time, and fields of endeavor

Ensure that the data and software acquired at great expense and effort are available for future researchers

Replace incompatible software tools and structures and take the lead in fostering “coordinated” interoperability

Invest in maintenance and usability of successful OR tools

. . . echoing dangers cited in Atkins report

Making OR a Cyberinfrastructure

Benefits of prompt action

Steer CI towards Meta Models, rather than complete reliance on Meta Data

Focus time and talent on breaking new ground rather than reproducing past efforts

Reduce isolation of the OR communityand among investigators within the community

Preserve-reuse valuable data (models and knowledge) for future research

Combining top-down and bottom-up approaches at multiple levels and scales

CYBER DOMAINSDESIGN, MANUFACTURING, SUPPLY "NETWORKS"

LIFE CYCLE APPLICATIONS (CYBER-PHYSICAL INTERFACES)(GRID-BASED COMMUNITIES)

OPERATIONS CIENTITIES: DATA-INFORMATION, KNOWLEDGE, ALGORITHMS,

SIMULATIONS, OPTIMIZATION-MODELS, CONFIGURATION(linear, non-linear, combinatorial both determiniistic and stochastic)OPERATIONS: LINKING, SEARCH, COMPARING, ANALYZING

PROCESSING

PHYSICAL LAYER(COMMUNICATION INFRASTRUCTURE)

OPERATING SYSTEM

CYBER INFRASTRUCTURE PLATFORMS

System Level Functionality Examples

User Interaction

& Interfaces

Working level of interaction standards with users and user systems.

Interface to various computing environments that enable access from various current and future operating platforms

Applications

Core tools for design, analysis, manufacture and supply chain coordination, etc. (Proprietary, open source, and shared).

CADD, structural analysis programs, flow visualization, simulation tools, enterprise management, collaborative communications, optimization etc.

Community Resources

Algorithms and analytical tools available to the applications and user levels.

Platform for community tool development & management.

Data analysis tools, image processing, statistical analysis functions, data mining, search functions, language translators, optimization languages.

System InteroperationArchitecture

Low level operating software and standards, security, and communication protocols.

Frameworks for interaction, communications, and resource sharing (DATA, INFORMATION, MODELS, KNOWLEDGE).

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Systems view of OR-CI

CI-OR-EA and Engineering Design

Data/Knowledge/Models are the foundation of design (repositories, libraries, catalogs)

CI and OR can facilitate: Access to remote and more current data/knowledge/models Organization of data, data mining and searching

methodologies More compute cycles => explore a much larger scenario

space, anticipate more exceptions and failure modes => more robust designs

More powerful, distributed algorithms for

• design under uncertainty

• design of flexible entities with many more degrees of freedom

Supply-Network Management

The supply “chain”Design and production

transportation and warehousing marketing and delivery

Really a supply “network” Part owned, part outsourced

Decision-making on wide range of time scales,real-time control to short-term scheduling to long-term planning

Reconfigurable network

Robust optimization to deal with uncertainties

. . . a company must be able to easily use the network

Challenges in Supply-Network Management

Diverse tools requiredDrawing on statistical, simulation, and optimization techniques

Communicating with each other, with varied data sources, with human analysts at different levels and locations

Difficult degree of integration required Approached by some costly and specialized proprietary systems

Still out of the reach of most researchers and practitioners

. . . great potential for an operations cyberinfrastructure

CI-OR for Supply-Network Management

Standards for web servicesEnable quick and reliable connections

between diverse analytical methods and data sources

Free time for experimentation with new computational ideas and new software components

Accessibility of the CISpeed integration of new research ideas into practice

Disseminate new supply-network ideas to a broader variety of companies, especially relatively small ones

Promote use of the most challenging approaches, such as optimization under uncertainty, distributed simulation and optimization, global optimization on noisy data, optimization of simulations

Example: CI-OR in Product Lifecycle

Challenges of outsourcingSteadily increasing demands for customization of products, but . . .

Further increases in complexity

Management of interfaces between suppliers threatens to become expensive and inefficient

Dispersion of engineering and production increases opportunities for breakdown in multi-tier supply networks

Hidden logistical and inventory costs and increased lead times

Consequences for design and development Highly iterative

Communication-intensive

Reliant on suppliers from prototyping to production

Example: CI-OR in Product Customization

Consequences for the supply networkSignificant time and cost to develop

a stable, reliable supply network for a product

Intensive coordination between different tiers

Rigid networks, unresponsive to dynamically changing markets

High inventory costs, borne mainly by lower-tier suppliers already under pressure to cut costs

What CI-OR in enterprise applications can provide Competitive advantages through

productivity improvements at all levels

More competitive based on speed and responsiveness of the supply network

Solve large-scale distributed optimization and constraints Handle large scale systems at multiple levels and scales

. . . not just for the biggest players …

CI-OR-EA and Enterprise Design and Services

Includes design of Physical entities (e.g. electrical grid, data networks) Virtual entities (alliances and markets)

Grid and network design: Design for robust (decentralized?) control, to allow for continuing operation after

disruptions (Big algorithmic challenges for OR) Placement of sensors, handling of sensor data are major issues

Market design (electricity markets, health information alliances): CI: Standards for information exchange OR: Use models/algorithms to design policies and pricing mechanisms to facilitate

efficient and fair operation

CI will enable the componentization of business infrastructures and result in service oriented IT models

Pervasive connectivity between physical infrastructure and CI will enable new service models

Modeling System

Modeling Language

OR-Cyberinfrastructure: Example

Local

Agent

Distributed

Solver Interface

Function Evaluator

Solver

Centralized

Registry

FunctionSimulator

Local

Analyzer

Distributed Distributed

ConclusionsThe demands of real time and competitive decisions

designed CI platform.The CI-OR-EA computational engines and service specific

data repositories and libraries increased DMS productivity.

Innovation in design and manufacturing as well as R&D.Capture commonalities and eliminate duplication

increase quality and reliability.OR – CI: resource location, network flow and assignment

problems.Target applications in areas where technologies can be

gainfully employed.CI and OR are essential partners to drive enterprise wide

productivity.