Igor Kiselev, Andrey Glaschenko, Alexander Chevelev, Petr Skobelev
The existing problem of continuous planning in transportation logistics requires the solving of dynamic Vehicle Routing Problems (dynamic VRPs) which is an NP-complete optimization problem. The task of continuous planning assumes the existence of individual commit times for orders to be released for execution with specific commitment strategies and is similar to the problem of maintaining a guaranteed response time in real-time systems that, in a dynamic environment, applies additional restrictions on planning algorithms. This paper describes the developed multi-agent platform for solving the dynamic multi-vehicle pickup and delivery problem with soft time windows dynamic m-PDPSTW) that supports goal-driven behavior of autonomous agents with a multi-objective decision-making model. Further research on the design of adaptive mechanisms for run-time feedback-directed adjustment of scheduling algorithms through learning and experience of applied decision options is outlined. An agent-based near real-time knowledge management support engine for solving time-critical data-mining problems in complex dynamic environments, currently being developed to work concurrently with the scheduling component, is based on the proposed approach to adaptive continuous unsupervised learning and a knowledge-based competitive multi-agent system for implementing it.
Subjects: 7.1 Multi-Agent Systems; 12. Machine Learning and Discovery
Submitted: Apr 10, 2007