CAST: A Successful Project in Support of the International Space Station Logistics
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The International Space Station (ISS) is one of the most challenging currently ongoing space programs. It has led to a number of very demanding logistic issues, in particular in relation to the on-orbit maintenance and resource resupply.
A fleet of launchers and vehicles is periodically made available by the most prominent space agencies in order to serve this scope. An overall traffic plan schedules the recurrent upload and download interventions. The relevant Cargo Manifest (delivered by NASA) establishes, for each carrier launch and re-entry, the shipment that is supposed to be transported from Earth to orbit and vice versa.
The European Space Agency (ESA) contributed annually to the ISS logistics from 2008 to 2014, by accomplishing five Automated Transfer Vehicle (ATV) missions. The ATV transportation system was conceived to support the recurrent upload phases from Earth to the ISS.
Within the relevant cargo accommodation context, in addition to tight balancing conditions, intricate three-dimensional packing issues arose. Furthermore, besides the remarkable complexity related, per se, to the loading aspects, very strict deadlines were usually imposed to accomplish the task. Last minute upgrades or even significant changes, moreover, often were expected to take place.
CAST (Cargo Accommodation Support Tool) is a dedicated optimization framework, funded by ESA and developed by Thales Alenia Space to carry out the whole analytical ATV cargo accommodation. This chapter describes the ATV loading problem first. The basic concept of CAST is further outlined, highlighting the advantages of the methodology adopted, both in terms of solution quality and time saving. Current extensions and possible future enhancements are investigated.
KeywordsSpace engineering Space vehicle/module Cargo accommodation International Space Station (ISS) Automated Transfer Vehicle (ATV) Columbus Laboratory Packing optimization Static and dynamic balancing Additional conditions Mixed integer programming (MIP) Heuristics
Thanks are due to Janos D. Pintér for his in-depth review of the manuscript. We wish to thank Jane Evans for her accurate review of the text.
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