Strategic Positioning in Tactical Scenario Planning

TL;DR

This paper explores computational scenario-based planning in tactical contexts, emphasizing multi-objective optimization and strategic positioning, demonstrated through resource planning solved with evolutionary algorithms.

Contribution

It introduces a novel approach to scenario-based planning that incorporates strategic positioning within tactical planning using multi-objective evolutionary algorithms.

Findings

Scenario-based planning fits naturally into a multi-objective framework.

Conflicting objectives occur across different system levels, not just within a single system.

Evolutionary Computation is well suited for complex planning problems.

Abstract

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.