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资源说明：微粒群算法的经典文献
We consider an n job m machine lot streaming problem in a flowshop with equal size sublots where the objective is to
minimize the total weighted earliness and tardiness To solve this problem we first propose a so called net benefit of movement
NBM algorithm which is much more efficient than the existing linear programming model for obtaining the optimal
starting and completion times of sublots for a given job sequence A new discrete particle swarm optimization DPSO
algorithm incorporating the NBM algorithm is then developed to search for the best sequence The new DPSO improves
the existing DPSO by introducing an inheritance scheme inspired by a genetic algorithm into particles construction To
verify the proposed DPSO algorithm comparisons with the existing DPSO algorithm and a hybrid genetic algorithm
HGA are made Computational results show that the proposed DPSO algorithm with a two point inheritance scheme
is very competitive for the lot streaming flowshop scheduling problem ">微粒群算法的经典文献
We consider an n job m machine lot streaming problem in a flowshop with equal size sublots where the objective is to
minimize the total weighted earliness and tardiness To solve this problem we first propose a so called net benefit of movement
NBM algorithm which [更多]
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