Knapsack problems: algorithms and computer implementations
Knapsack problems: algorithms and computer implementations
Periodic multiprocessor scheduling
PARLE '91 Proceedings on Parallel architectures and languages Europe : volume I: parallel architectures and algorithms: volume I: parallel architectures and algorithms
Feasibility problems for recurring tasks on one processor
MFCS '90 Selected papers of the 15th international symposium on Mathematical foundations of computer science
Solving binary cutting stock problems by column generation and branch-and-bound
Computational Optimization and Applications
BISON: a fast hybrid procedure for exactly solving the one-dimensional bin packing problem
Computers and Operations Research
New heuristics for one-dimensional bin-packing
Computers and Operations Research
Approximation algorithms for scheduling problems
Approximation algorithms for scheduling problems
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Handbook of Scheduling: Algorithms, Models, and Performance Analysis
Hard Real-time Computing Systems: Predictable Scheduling Algorithms And Applications (Real-Time Systems Series)
New bin packing fast lower bounds
Computers and Operations Research
Solving the one-dimensional bin packing problem with a weight annealing heuristic
Computers and Operations Research
Scheduling periodic tasks in a hard real-time environment
ICALP'10 Proceedings of the 37th international colloquium conference on Automata, languages and programming
Strictly periodic scheduling in IMA-based architectures
Real-Time Systems
Hi-index | 0.00 |
We report on the solution of a real-time scheduling problem that arises in the design of software-based operation control of aircraft. A set of tasks has to be distributed on a minimum number of machines and offsets of the tasks have to be computed. The tasks emit jobs periodically starting at their offset and then need to be executed on the machines without any delay. Also, further constraints in terms of memory usage and redundancy requirements have to be met. Approaches based on standard integer programming formulations fail to solve our real-world instances. By exploiting structural insights of the problem we obtain an IP-formulation and primal heuristics that together solve the real-world instances to optimality and outperform text-book approaches by several orders of magnitude. Our methods lead, for the first time, to an industry strength tool to optimally schedule aircraft sized problems.