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Optimal job scheduling is a class of optimization problems related to scheduling. The inputs to such problems are a list of jobs (also called processes or tasks) and a list of machines (also called processors or workers). The required output is a schedule – an assignment of jobs to machines. The schedule should optimize a certain objective ...
The variant where variables are required to be 0 or 1, called zero-one linear programming, and several other variants are also NP-complete [2] [3]: MP1 Some problems related to Job-shop scheduling; Knapsack problem, quadratic knapsack problem, and several variants [2] [3]: MP9 Some problems related to Multiprocessor scheduling
Interval scheduling is a class of problems in computer science, particularly in the area of algorithm design. The problems consider a set of tasks. The problems consider a set of tasks. Each task is represented by an interval describing the time in which it needs to be processed by some machine (or, equivalently, scheduled on some resource).
Single-machine scheduling or single-resource scheduling is an optimization problem in computer science and operations research. We are given n jobs J 1 , J 2 , ..., J n of varying processing times, which need to be scheduled on a single machine, in a way that optimizes a certain objective, such as the throughput .
Unrelated-machines scheduling is an optimization problem in computer science and operations research. It is a variant of optimal job scheduling . We need to schedule n jobs J 1 , J 2 , ..., J n on m different machines, such that a certain objective function is optimized (usually, the makespan should be minimized).
Local (basic block) scheduling: instructions can't move across basic block boundaries. Global scheduling: instructions can move across basic block boundaries. Modulo scheduling: an algorithm for generating software pipelining, which is a way of increasing instruction level parallelism by interleaving different iterations of an inner loop.
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Identical-machines scheduling is an optimization problem in computer science and operations research. We are given n jobs J 1 , J 2 , ..., J n of varying processing times, which need to be scheduled on m identical machines, such that a certain objective function is optimized, for example, the makespan is minimized.