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Though such a deficiency is not observed in the performance of GPSO, it is still inferior to GES on the single batch-processing machine scheduling test problem instances.
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An extensive comparative study is conducted to evaluate the performance of GES versus GGA and GPSO (a recently proposed grouping particle swarm optimization algorithm) on test problem instances of the single batch-processing machine scheduling problem and the bin-packing problem.
The job shop scheduling problem (JSP) [90], the test task scheduling problem (TTSP) [52], the parallel machine scheduling problem (PMSP) [91] are typical representatives of the scheduling problem.
We consider the following machine scheduling game.
Numerical computational results focusing on the identical parallel machine scheduling problem and the general parallel machine scheduling problem shows that these algorithms outperform heuristic procedures, and fit for larger scale parallel machine earliness/tardiness scheduling problem.
Consider an arbitrary schedule for a single machine scheduling problem.
In production scheduling, many popular problems have been investigated intensively such as single machine scheduling, parallel machine scheduling, (flexible) flow shop scheduling, (flexible) job shop scheduling, and open shops.
The two phase sub-population genetic algorithm is applied to solve the parallel machine-scheduling problems in testing of the efficiency and efficacy.
I just stood at the front desk and scheduled tests.
Photo by Nancy A. Ruhling Sakis gives a repaired machine a test run.
The San Diego company makes machines that test for DNA variations.
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