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1 2003

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PLA-based permutation-scheduling

Jędrzejowicz J., Jędrzejowicz P.

Foundations of Computing and Decision Sciences

2003

Vol. 28, No. 3

159-177

The paper proposes the implementation of population learning algorithm (PLA) for solving three well-known NP-hard permutation-scheduling problems. PLA is a recently developed method belonging to the class of the population-based algorithms. One of possible applications of the PLA is solving difficult optimization problems. The first of the discussed problems involves scheduling tasks on a single machine against common due date with earliness and tardiness penalties. The second is known as the permutation flow shop problem. The third one involves scheduling tasks on a single machine with total weighted tardiness as a criterion. To evaluate the proposed implementations computational experiments have been carried. They involved solving available sets of benchmark problems and comparing the results with the optimum or best-known solutions. PLA has found better upper bounds on several benchmark instances. Experiments have also helped to identify some behavioral characteristics of the proposed algorithms.