Fleet estimation for defence logistics using a multi-objective learning classifier system
Kamran Shafi, Axel Bender, Hussein A. Abbass · 2011
Predicting optimal size and mix of future transportation fleets is of great importance to defence logistics planners but faces some challenges. Firstly, the future is uncertain because the environment changes constantly and adversaries are highly adaptive. Secondly, optimising a large heterogeneous transport fleet is inherently complex. Heuristic-based optimisation techniques are therefore often applied that provide approximate solutions to support the decision making in such complex problems. However, heuristic-based methods act as black boxes and do not offer insights into the relationships between future scenarios and the solutions found. In this paper, we use an evolutionary rule-based approach to understand these relationships. A multi-objective Learning Classifier System (LCS) is employed to learn interpretable patterns of future scenarios and to associate them with the best performing heuristics under given conditions. To accomplish this, two novel reward functions are introduced that assign credits to classifiers based on the multi-objective performance of the predicted heuristics. Results show that LCS generalises well to relate scenario characteristics with Pareto optimal heuristics.