Search

In recent aerospace missions, space logistics have proven essential in storing, delivering and returning crew and materials between terrestrial facilities and space stations. Unlike classical commercial logistics, space logistics operations are cost-prohibitive and mission-driven, and its replenishment cycle for essential materials is relatively long. Therefore, the complete utilisation of spacecraft payload is of utmost importance. The theory of the inventory packing problem is extended in this study to build autonomous agents that interact with one another within a space logistics decision support system to reinforce the replenishment decision, chunk loading optimisation, and quality inspection. With the long replenishment cycle time, an agent embedded with interval type-2 fuzzy logic is explored to support chaotic time-series demand forecasting to derive re-order quantities in the desired period. Afterwards, the second agent solves the space chunk loading problem using the differential evolution algorithm to utilise payloads and capacities, particularly cylindrical chunks fully. The third agent measures actual item dimensions and quality to deploy the three-dimensional object scanning devices. Feedback is provided to the second agent to derive optimal chunk-loading instructions. Thanks to the autonomous interactions among the above agents, mission-critical decisions for space logistics are supported to achieve operational excellence., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 ISA. Published by Elsevier Ltd. All rights reserved.)