Scheduling jobs with fixed start and end times
Discrete Applied Mathematics
Introduction to algorithms
Proceedings of the third international conference on Genetic algorithms
On the k-coloring of intervals
Discrete Applied Mathematics
Using iterative repair to automate planning and scheduling of shuttle payload operations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Tabu Search
Approximating the Throughput of Multiple Machines in Real-Time Scheduling
SIAM Journal on Computing
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Scheduling Problems and Traveling Salesmen: The Genetic Edge Recombination Operator
Proceedings of the 3rd International Conference on Genetic Algorithms
A Comparison of Genetic Algorithms for the Static Job Shop Scheduling Problem
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
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
Leap before you look: an effective strategy in an oversubscribed scheduling problem
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Journal of Artificial Intelligence Research
Applying adaptive algorithms to epistatic domains
IJCAI'85 Proceedings of the 9th international joint conference on Artificial intelligence - Volume 1
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Maximizing flexibility: a retraction heuristic for oversubscribed scheduling problems
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Slack-based heuristics for constraint satisfaction scheduling
AAAI'93 Proceedings of the eleventh national conference on Artificial intelligence
Texture-based heuristics for scheduling revisited
AAAI'97/IAAI'97 Proceedings of the fourteenth national conference on artificial intelligence and ninth conference on Innovative applications of artificial intelligence
Heuristic-biased stochastic sampling
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Graph colouring approaches for a satellite range scheduling problem
Journal of Scheduling
Computers and Industrial Engineering
Scheduling satellite launch missions: an MILP approach
Journal of Scheduling
Genetic algorithms for satellite scheduling problems
Mobile Information Systems
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The Air Force Satellite Control Network (AFSCN) coordinates communications to more than 100 satellites via nine ground stations positioned around the globe. Customers request an antenna at a ground station for a specific time window along with possible alternative slots. Typically, 500 requests per day result in more than 100 conflicts, which are requests that cannot be satisfied because other tasks need the same slot. Scheduling access requests is referred to as the Satellite Range Scheduling Problem (SRSP). This paper presents an overview of three key issues: (1) how has the problem changed over the last 10 years, (2) what algorithms work best and (3) what objective function is appropriate for AFSCN. We compared data sets from 1992 and from 2002/2003 and found significant differences in the problems. Our evaluation of solutions focuses on three algorithms: local search, Gooley's algorithm from AFIT, and the Genitor genetic algorithm. It can be shown that local search (and therefore metaheuristics based on local search) fail to compete with Gooley's algorithm and Genitor. Finally, while all prior work on AFSCN minimizes request conflicts, we explore an alternative objective function. Because human schedulers must eventually schedule all requests, it might be better to optimize schedules for ''repairability''. Our results suggest that minimizing schedule overlaps makes it easier to fit larger requests into the schedule.