research-article

Computational scenario-based capability planning

Authors:

Hussein A. Abbass,

James Whitacre, and

Ruhul SarkerAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

Pages 1437 - 1444

https://doi.org/10.1145/1389095.1389378

Published: 12 July 2008 Publication History

Abstract

Scenarios are pen-pictures of plausible futures, used for strategic planning. The aim of this investigation is to expand the horizon of scenario-based planning through computational models that are able to aid the analyst in the planning process. The investigation builds upon the advances of Information and Communication Technology (ICT) to create a novel, flexible and customizable computational capability-based planning methodology that is practical and theoretically sound. We will show how evolutionary computation, in particular evolutionary multi-objective optimization, can play a central role - both as an optimizer and as a source for innovation.

References

[1]

Abbass HA., Baker S., Bender A. & Sarker R. (2007) Anticipating Future Army Supply Chain Fleet Capabilities, SimTecT.

[2]

Baker S., Bender A., Abbass HA. & Sarker R. 2007. A Scenario-based Evolutionary Scheduling Approach for Assessing Future Supply Chain Fleet Capabilities, In Evolutionary Scheduling, Dahal K. Tan K.C. & Cowling P. (eds.), Studies in Computational Intelligence Series, Springer, Vol. 49, 485--512.

[3]

Bankes, SC. 2002. Tools and techniques for developing policies for complex and uncertain systems. Proceedings of the National Academy of Science, USA 99(3) 7263--7266.

[4]

Checkland P. 1981. Systems thinking, systems practice, John Wiley & Sons, Ltd, Chichester.

[5]

Coyle G. 2004. Practical strategy: structured tools and techniques, Prentice Hall, Pearson Education Ltd, Essex.

[6]

Davis PK. & Finch L. 1993. Defence planning for the post-cold war era: giving meaning to flexibility, adaptiveness, and robustness of capability, RAND Corporation, CA, USA.

[7]

Davis PK. & Kahan JP. 2007. Theory and methods for supporting high level military decisionmaking, RAND Corporation, Santa Monica, CA, USA.

[8]

Davis PK., Bankes SC. & Egner M. 2007. Enhancing strategic planning with massive scenario generation, RAND Corporation, Santa Monica, CA, USA.

[9]

Deb K., Pratap A., Agarwal S., & Meyarivan T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II, IEEE Transactions on Evolutionary Computation, 6(2), 182--197.

Digital Library

[10]

Eden C. 1987. Problem solving/finishing, in Jackson & Keys (Eds), New Direction in Management sciences, Gower, Aldershot.

[11]

Haeckel SH. 1999. Adaptive enterprise, Harvard Business School Press.

[12]

Ilachinski A. 2004. Artificial war, multiagent-based simulation of combat, World scientific publishing.

Digital Library

[13]

Kauffman S. 1995. At the home in the Universe: the search for the laws of complexity, Viking, London.

[14]

Mendonça S., e Cunha MP., Kaivo-oja J. and Ruff F. 2004. Wild Cards, Weak Signals and Organizational Improvisation, Futures 36, 201--218.

[15]

Mintzberg H. 1979. Patterns in strategy formation, Management Science, 1979.

[16]

Popper, SW, Lempert RJ. and Bankes SC. 2005. Shaping the future. Scientific America 292(4) 66--71.

[17]

Schoemaker PJH. 1993. Multiple scenario development: Its conceptual and behavioural foundation. Strategic Management Journal 14: 193--213.

[18]

Yang A., Abbass HA. & Sarker R. 2006. Characterizing Warfare in Red Teaming, IEEE Transactions on Systems, Man, Cybernetics, Part B, 36(1).

Digital Library

[19]

Yang A., Abbass HA. & Sarker R. 2006. Land Combat Scenario Planning: A Multiobjective Approach, Lecture Notes Computer Science, LNCS 4247, Springer-Verlag, pp. 837--844.

Digital Library

[20]

RTO, Handbook on Long Term Defence Planning (Manuel sur la planification de defense a long terme). 2003, Research and Technology Organisation, North Atlantic Treaty Organization (NATO): NEUILLY-SUR-SEINE CEDEX, France.

[21]

Radford K.J., Strategic Planning: An Analytical Approach. 1980: Reston Publishing Company.

Cited By

Harrison KElsayed SGaranovich IWeir TGalister MBoswell STaylor RSarker R(2020)Portfolio Optimization for Defence ApplicationsIEEE Access10.1109/ACCESS.2020.29831418(60152-60178)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2983141
Xiong JWang RJiang J(2019)Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided NeighborhoodsComplexity10.1155/2019/75897602019Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1155/2019/7589760
Chan FWang ZSingh YWang XRuan JTiwari M(2019)Activity scheduling and resource allocation with uncertainties and learning in activitiesIndustrial Management & Data Systems10.1108/IMDS-01-2019-0002Online publication date: 7-Jun-2019
https://doi.org/10.1108/IMDS-01-2019-0002
Show More Cited By

Index Terms

Computational scenario-based capability planning

Recommendations

Strategic positioning in tactical scenario planning
GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Capability planning problems are pervasive throughout many areas of human interest with prominent examples found in defense and security. Planning provides a unique context for optimization that has not been explored in great detail and involves a ...
Read More
Scenario-based multi-period program optimization for capability-based planning using evolutionary algorithms

Graphical abstractDisplay Omitted HighlightsProblem: Project portfolio optimization under multiple planning scenarios.Approach: Optimizing simulation using many objective optimization and Q-learning.Application: Defence capability project portfolio or ...
Read More
Assumption-based planning: generating plans and explanations under incomplete knowledge
AAAI'13: Proceedings of the Twenty-Seventh AAAI Conference on Artificial Intelligence

Many practical planning problems necessitate the generation of a plan under incomplete information about the state of the world. In this paper we propose the notion of Assumption-Based Planning. Unlike conformant planning, which attempts to find a plan ...
Read More

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 12 July 2008

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

GECCO08

Sponsor:

GECCO08: Genetic and Evolutionary Computation Conference

July 12 - 16, 2008

GA, Atlanta, USA

Acceptance Rates

Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

17
Total Citations
View Citations
326
Total Downloads

Downloads (Last 12 months)2
Downloads (Last 6 weeks)0

Other Metrics

View Author Metrics

Citations

Cited By

Harrison KElsayed SGaranovich IWeir TGalister MBoswell STaylor RSarker R(2020)Portfolio Optimization for Defence ApplicationsIEEE Access10.1109/ACCESS.2020.29831418(60152-60178)Online publication date: 2020
https://doi.org/10.1109/ACCESS.2020.2983141
Xiong JWang RJiang J(2019)Weapon Selection and Planning Problems Using MOEA/D with Distance-Based Divided NeighborhoodsComplexity10.1155/2019/75897602019Online publication date: 22-Nov-2019
https://dl.acm.org/doi/10.1155/2019/7589760
Chan FWang ZSingh YWang XRuan JTiwari M(2019)Activity scheduling and resource allocation with uncertainties and learning in activitiesIndustrial Management & Data Systems10.1108/IMDS-01-2019-0002Online publication date: 7-Jun-2019
https://doi.org/10.1108/IMDS-01-2019-0002
Yuan XLiu JHao X(2017)A moving block sequence-based evolutionary algorithm for resource investment project scheduling problemsBig Data and Information Analytics10.3934/bdia.20170072:1(39-58)Online publication date: Sep-2017
https://doi.org/10.3934/bdia.2017007
Shen XHan YFu J(2017)Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problemsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2245-421:21(6531-6554)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s00500-016-2245-4
Shi JLiao TTian KZhou ZXiong J(2016)A multi-objective approach for weapon selection and planning problems in dynamic environmentsJournal of Industrial and Management Optimization10.3934/jimo.201606813:3(1189-1211)Online publication date: Oct-2016
https://doi.org/10.3934/jimo.2016068
He HLin CTan KKendall GCangelosi A(2016)CIS Publication Spotlight [Publication Spotlight]IEEE Computational Intelligence Magazine10.1109/MCI.2015.250212711:1(15-17)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/MCI.2015.2502127
Estevez P(2016)CIS in the Next Decade [President's Message]IEEE Computational Intelligence Magazine10.1109/MCI.2015.250212111:1(3-53)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/MCI.2015.2502121
Kavakeb SNguyen TYang ZJenkinson I(2016)Evolutionary Fleet Sizing in Static and Uncertain Environments with Shuttle Transportation Tasks-The Case Studies of Container Terminals [Application Notes]IEEE Computational Intelligence Magazine10.1109/MCI.2015.250155211:1(55-69)Online publication date: 1-Feb-2016
https://dl.acm.org/doi/10.1109/MCI.2015.2501552
Jian Xiong Jing Liu Yingwu Chen Abbass H(2014)A Knowledge-Based Evolutionary Multiobjective Approach for Stochastic Extended Resource Investment Project Scheduling ProblemsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2013.228391618:5(742-763)Online publication date: Oct-2014
https://doi.org/10.1109/TEVC.2013.2283916
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents