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

Christopher G. Valicka, Deanna Garcia, Andrea Staid, Jean-Paul Watson, Gabriel Hackebeil, Sivakumar Rathinam and Lewis Ntaimo

European Journal of Operational Research, 2019, vol. 275, issue 2, 431-445

Abstract: We consider 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 results 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.

Keywords: Scheduling; Integer programming; Stochastic programming; Remote sensing; Weather uncertainty (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:275:y:2019:i:2:p:431-445

DOI: 10.1016/j.ejor.2018.11.043

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