Method Of Adaptive Cargo Flow Scheduling For Iss Rs Based On Multi-Agent Technology

INDUSTRIAL APPLICATIONS OF HOLONIC AND MULTI-AGENT SYSTEMS(2017)

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Abstract
Problem statement: The problem of real-time cargo flow scheduling for the Russian Segment of the International Space Station (ISS RS) is considered. Strategic and tactical scheduling of flight plans, delivery, return, disposal and allocation of RS ISS cargo flow, including more than 3500 entities, is a very complex and time-consuming task. To solve this problem one has to consider numerous factors, constraints and preferences, such as changing demand in fuel, water and supplies, ballistics and solar activity, peculiarities of spaceship types and docking modules. Changes in dates of launch, landing, docking and undocking, number of crew members and other parameters influence the flight program and cargo flow. These changes require dynamic re-scheduling in the chain of changes of interconnected parameters that should be specified, recalculated and coordinated. The problem is represented as a dynamical balance of interests between demands and resources. Methods: A method of adaptive ISS RS cargo flow scheduling in real-time is suggested, considering cargo priorities. The method is based on multi-agent technology for solving conflicts through negotiations of agents. This method is capable of flexible and efficient adaptation of ISS RS cargo flow schedule depending on events in real-time. Results: The developed method is used in the interactive multi-agent system for scheduling of flight program, cargo flow and resources of ISS RS. Practical relevance: The developed system has been implemented in industrial operation and is used for cargo flow scheduling of ISS RS resources. The system provides the following advantages: ISS cargo flow scheduling similar to the schedules created by the experienced operators; flexible and quick reaction to the events that cause cargo flow re-scheduling; reduction of manual labor and increased decision-making efficiency by 2-3 times; real-time monitoring and control of the schedule implementation.
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Key words
Decision-making support, Adaptive planning, Multi-agent technology, Cargo flow scheduling, Events
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