No abstract

For motion coordination of a multi-agent system, a simulation-based rule generating method is needed. However, previous studies assuming understandabieness of calculated rules for users do not exist. In this paper, a simulation-based simplified and robust rule generation system for multi-agent scheduling problem is proposed using parallel algorithm discovery and orchestration (PADO) and simulated annealing programming (SAP). In addition, a method to extract constraints of rules from a simulation is also proposed. This proposed method is evaluated with an aircraft control problem, and robust rules can be calculated. Moreover, with the method to extract constraints, the average calculation time can be 80 % less than that without the proposed method.

In this paper, we propose a method to expand taxiways at large airports that shortens taxiing time and reduces costs. To ease congestion at airports, expanding taxiways is needed. But the exorbitant cost of expansion dictates the discreet selection of expanded taxiways. Determining expanded taxiways is a multilevel and multi-objective optimization problem containing 1-0 integer problem and routing problem. However, little research on this topic is available. In this paper, we propose an algorithm to calculate Pareto solutions applying a Multi-Objective Genetic Algorithm (MOGA) and Ant Colony Optimization (ACO). The proposed method is twice as accurate as other methods, and solutions calculated by proposed method are available when flight schedules are disturbed