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首页> 外文期刊>RAIRO Operation Research >LARGE NEIGHBORHOOD IMPROVEMENTS FOR SOLVING CAR SEQUENCING PROBLEMS
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LARGE NEIGHBORHOOD IMPROVEMENTS FOR SOLVING CAR SEQUENCING PROBLEMS

机译:解决汽车排队问题的大型近邻改进

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摘要

The NP-hard problem of car sequencing has received a lot of attention these last years. Whereas a direct approach based on integer programming or constraint programming is generally fruitless when the number of vehicles to sequence exceeds the hundred, several heuristics have shown their efficiency. In this paper, very large-scale neighborhood improvement techniques based on integer programming and linear assignment are presented for solving car sequencing problems. The effectiveness of this approach is demonstrated through an experimental study made on seminal CSPLIB's benchmarks.
机译:近年来,汽车测序的NP难题已经引起了广泛关注。当要排序的车辆数量超过一百时,基于整数规划或约束规划的直接方法通常是徒劳的,但几种启发式方法已显示出其效率。本文提出了一种基于整数规划和线性分配的大规模邻域改进技术来解决汽车排序问题。通过对CSPLIB开创性基准进行的实验研究证明了这种方法的有效性。

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