.png){kind=icon}
.png){kind=icon}
Return
Optimization-Based Decision Support Software for a Team-in-the-Loop Experiment: Multilevel Asset Allocation
.png){kind=icon}
PRE
AI
DOI：10.1109/TSMC.2013.2295360.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Motivated by the Navy's interest in decision support tools that augment planning activities within a maritime operations center (MOC), we have developed a multilevel resource allocation model that is capable of interacting with human planners to dynamically allocate hierarchically-organized assets to process interdependent tasks in order to accomplish mission objectives. The planning problem is formulated as a mixed-integer nonlinear programming (MINLP) problem of minimizing the overall difference between the human-specified desired task accuracy performance criteria and the expected performance outcomes, the latter being based on how well the assigned resources match the required resources, subject to a number of real-world planning constraints. To solve the resulting large-scale MINLP problem, we propose two methods: 1) a Lagrangian relaxation method that solves the multilevel asset allocation problem with a measure of sub-optimality in terms of an approximate duality gap and 2) a dynamic list planning heuristic algorithm that provides high-quality sub-optimal solutions rapidly (less than 10 s for the scenarios considered here). Finally, we verify our methods using realistic MOC planning scenarios, provide a comparative evaluation of the performance measures of the two proposed methods, and investigate the value of information via human-in-the-loop experiments.
Keywords:
Decision support for resource allocation
dynamic list planning
Lagrangian relaxation method
multilevel asset allocation problem
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
10.5
Papers:
1.1W
Citations:
5.0W
View more organizations