skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty

Abstract

We introduce the problem of scheduling observations on a constellation of remote sensors, to maximize the aggregate quality of the collections obtained. While automated tools exist to schedule remote sensors, they are often based on heuristic scheduling techniques, which typically fail to provide bounds on the quality of the resultant schedules. To address this issue, we first introduce a novel deterministic mixed-integer programming (MIP) model for scheduling a constellation of one to n satellites, which relies on extensive pre-computations associated with orbital propagators and sensor collection simulators to mitigate model size and complexity. Our MIP model captures realistic and complex constellation-target geometries, with solutions providing optimality guarantees. We then extend our base deterministic MIP model to obtain two-stage and three-stage stochastic MIP models that proactively schedule to maximize expected collection quality across a set of scenarios representing cloud cover uncertainty. Our experimental conclusions on instances of one and two satellites demonstrate that our stochastic MIP models yield significantly improved collection quality relative to our base deterministic MIP model. We further demonstrate that commercial off-the-shelf MIP solvers can produce provably optimal or near-optimal schedules from these models in time frames suitable for sensor operations.

Authors:
ORCiD logo [1];  [1];  [1];  [1];  [2];  [3]; ORCiD logo [3]
  1. Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
  2. Univ. of Michigan, Ann Arbor, MI (United States)
  3. Texas A & M Univ., College Station, TX (United States)
Publication Date:
Research Org.:
Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1524209
Report Number(s):
SAND-2016-9028J
Journal ID: ISSN 0377-2217; 647351
Grant/Contract Number:  
AC04-94AL85000; NA0003525
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
European Journal of Operational Research
Additional Journal Information:
Journal Volume: 275; Journal Issue: 2; Journal ID: ISSN 0377-2217
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Scheduling; Integer programming; Stochastic programming; Remote sensing; Weather uncertainty

Citation Formats

Valicka, Christopher G., Garcia, Deanna, Staid, Andrea, Watson, Jean-Paul, Hackebeil, Gabriel, Rathinam, Sivakumar, and Ntaimo, Lewis. Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty. United States: N. p., 2018. Web. doi:10.1016/j.ejor.2018.11.043.
Valicka, Christopher G., Garcia, Deanna, Staid, Andrea, Watson, Jean-Paul, Hackebeil, Gabriel, Rathinam, Sivakumar, & Ntaimo, Lewis. Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty. United States. https://doi.org/10.1016/j.ejor.2018.11.043
Valicka, Christopher G., Garcia, Deanna, Staid, Andrea, Watson, Jean-Paul, Hackebeil, Gabriel, Rathinam, Sivakumar, and Ntaimo, Lewis. Fri . "Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty". United States. https://doi.org/10.1016/j.ejor.2018.11.043. https://www.osti.gov/servlets/purl/1524209.
@article{osti_1524209,
title = {Mixed-integer programming models for optimal constellation scheduling given cloud cover uncertainty},
author = {Valicka, Christopher G. and Garcia, Deanna and Staid, Andrea and Watson, Jean-Paul and Hackebeil, Gabriel and Rathinam, Sivakumar and Ntaimo, Lewis},
abstractNote = {We introduce the problem of scheduling observations on a constellation of remote sensors, to maximize the aggregate quality of the collections obtained. While automated tools exist to schedule remote sensors, they are often based on heuristic scheduling techniques, which typically fail to provide bounds on the quality of the resultant schedules. To address this issue, we first introduce a novel deterministic mixed-integer programming (MIP) model for scheduling a constellation of one to n satellites, which relies on extensive pre-computations associated with orbital propagators and sensor collection simulators to mitigate model size and complexity. Our MIP model captures realistic and complex constellation-target geometries, with solutions providing optimality guarantees. We then extend our base deterministic MIP model to obtain two-stage and three-stage stochastic MIP models that proactively schedule to maximize expected collection quality across a set of scenarios representing cloud cover uncertainty. Our experimental conclusions on instances of one and two satellites demonstrate that our stochastic MIP models yield significantly improved collection quality relative to our base deterministic MIP model. We further demonstrate that commercial off-the-shelf MIP solvers can produce provably optimal or near-optimal schedules from these models in time frames suitable for sensor operations.},
doi = {10.1016/j.ejor.2018.11.043},
url = {https://www.osti.gov/biblio/1524209}, journal = {European Journal of Operational Research},
issn = {0377-2217},
number = 2,
volume = 275,
place = {United States},
year = {2018},
month = {11}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record

Citation Metrics:
Cited by: 2 works
Citation information provided by
Web of Science

Save / Share: