Feature-based telescope scheduler

Elahesadat Naghib, Robert J. Vanderbei, Christopher Stubbs

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations


Feature-Based Scheduler offers a sequencing strategy for ground-based telescopes. This scheduler is designed in the framework of Markovian Decision Process (MDP), and consists of a sub-linear online controller, and an offline supervisory control-optimizer. Online control law is computed at the moment of decision for the next visit, and the supervisory optimizer trains the controller by simulation data. Choice of the Differential Evolution (DE) optimizer, and introducing a reduced state space of the telescope system, offer an efficient and parallelizable optimization algorithm. In this study, we applied the proposed scheduler to the problem of Large Synoptic Survey Telescope (LSST). Preliminary results for a simplified model of LSST is promising in terms of both optimality, and computational cost.

Original languageEnglish (US)
Title of host publicationObservatory Operations
Subtitle of host publicationStrategies, Processes, and Systems VI
EditorsAlison B. Peck, Robert L. Seaman, Chris R. Benn
ISBN (Electronic)9781510601994
StatePublished - 2016
EventObservatory Operations: Strategies, Processes, and Systems VI - Edinburgh, United Kingdom
Duration: Jun 27 2016Jul 1 2016

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X


OtherObservatory Operations: Strategies, Processes, and Systems VI
Country/TerritoryUnited Kingdom

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering


  • Decision making
  • Evolu-tionary Algorithm
  • LSST
  • Observation scheduling
  • Observing strategy
  • Optimal control
  • Optimization
  • Telescope scheduler


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