A time indexed formulation of non-preemptive single machine scheduling problems
Mathematical Programming: Series A and B
Modern heuristic techniques for combinatorial problems
Modern heuristic techniques for combinatorial problems
Time-Indexed Formulations for Machine Scheduling Problems: Column Generation
INFORMS Journal on Computing
Scheduling Space–Ground Communications for the Air Force Satellite Control Network
Journal of Scheduling
Three Scheduling Algorithms Applied to the Earth Observing Systems Domain
Management Science
Solving Project Scheduling Problems by Minimum Cut Computations
Management Science
Exploring relaxation induced neighborhoods to improve MIP solutions
Mathematical Programming: Series A and B
Near-Optimal Solutions of Large-Scale Single-Machine Scheduling Problems
INFORMS Journal on Computing
Fix and Relax Heuristic for a Stochastic Lot-Sizing Problem
Computational Optimization and Applications
Selected Topics in Column Generation
Operations Research
Daily imaging scheduling of an Earth observation satellite
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
The stable set problem and the thinness of a graph
Operations Research Letters
Multi-satellite control resource scheduling based on ant colony optimization
Expert Systems with Applications: An International Journal
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The data exchange between ground stations and satellite constellations is becoming a challenging task, as more and more communication requests must be daily scheduled on a few, expensive stations located all around the Earth. Most of the scheduling procedures adopted in practice cannot cope with such complexity, and the development of optimization-based tools is strongly spurred. We show that the problem can be formulated as a multiprocessor task scheduling problem in which each job (communication) requires a time dependent pair of resources (ground station and satellite) to be processed, and the objective consists of maximizing the total revenue of on-time jobs. A time-indexed {0,1}-linear programming formulation is then introduced able to include all the complex technological constraints of current constellations. Unfortunately, relevant real-world scenarios yield integer programs with hundreds of thousands variables and a few million constraints, which cannot be tackled by standard integer programming (either exact or heuristic) techniques. To overcome this difficulty, we developed a Lagrangian version of the Fix-and-Relax MIP heuristic. It is based on a Lagrangian relaxation of the problem which is shown to be equivalent to a sequence of maximum weighted independent set problems on interval graphs. The heuristic has been implemented in a tool used by the Italian reference operator for the GALILEO constellation, providing near optimal solutions to relevant large scale test problems.