Simulation optimization: simulation optimization
Proceedings of the 34th conference on Winter simulation: exploring new frontiers
New paradigms and new challenges
WSC '05 Proceedings of the 37th conference on Winter simulation
Land combat scenario planning: a multiobjective approach
SEAL'06 Proceedings of the 6th international conference on Simulated Evolution And Learning
Evolutionary optimization in uncertain environments-a survey
IEEE Transactions on Evolutionary Computation
Characterizing warfare in red teaming
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Application notes: MEBRA: multiobjective evolutionary-based risk assessment
IEEE Computational Intelligence Magazine
| Hi-index | 0.01 |
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 number of interesting challenges which are distinct from traditional optimization research. Planning problems demand solutions that can satisfy a number of competing objectives on multiple scales related to robustness, adaptiveness, risk, etc. The scenario method is a key approach for planning. Scenarios can be defined for long-term as well as short-term plans. This paper introduces computational scenario-based planning problems and proposes ways to accommodate strategic positioning within the tactical planning domain. We demonstrate the methodology in a resource planning problem that is solved with a multi-objective evolutionary algorithm. Our discussion and results highlight the fact that scenario-based planning is naturally framed within a multi-objective setting. However, the conflicting objectives occur on different system levels rather than within a single system alone. This paper also contends that planning problems are of vital interest in many human endeavors and that Evolutionary Computation may be well positioned for this problem domain.