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To cope with extremely large-scale logistic optimization for strategic planning and real time operational optimizations as well, in this paper, we have proposed an extended algorithm of our hybrid method so that it becomes available for parallel computing. We have also developed a novel algorithm of particle swarm optimization (PSO) associated with binary decision variables. It is quite effective for finding the optimum opening distribution centers in three-echelon logistic network by parallel computing. Eventually, we have implemented the procedure in the parallel algorithm deployed as a multi-population based approach using multi-thread programming technique. Taking two topologies belonging to a coarse grain parallelism, we compared their effects on the performance of the algorithm through large scale logistics optimization. Finally, we confirmed that the proposed method can bring about high performance for the parallel computing that is suitable for the present goal and circumstance through numerical experiments.

Acyclic partial scan design is an efficient DFT method. This paper presents a scheduling method for reducing the number of scan registers for acyclic structure. In order to estimate the number of scan registers during scheduling, we propose provisional binding of operational units, and show a force-directed scheduling algorithm with the provisional binding. Experimental results show that the number of scan registers in the resulting RTL datapaths can be reduced by our method combined with the binding algorithm for acyclic partial scan.

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