带扩张的固定费用网络问题的自组织极值点Tabu搜索算法
A Self-Organizing Extreme-Point Tabu-Search Algorithm for Fixed Charge Network Problems with Extensions
摘要：
我们提出了一个新的自组织算法的固定收费网络流问题的基础上，鬼图像（GI）过程中提出的Glover（1994年）和适应固定收费运输问题的Glover，Amini和Kochenberger（2005年）。我们的自组织GI算法迭代地修改了一个理想化的代表性的问题体现在一个参数的鬼图像，使所有的步骤进行与原始网络流算法上的参数GI。计算测试进行了广泛的一组基准问题，其中包括以前最大的一组在文献中，比较我们的算法，以前提出的最好的方法固定收费运输问题，虽然我们的算法是不是专门为这一类。我们还提供了额外的更一般的固定收费网络流问题与Cplex 12.8的比较，以证明新的自组织GI算法是有效的大型问题的情况下，找到解决方案与统计等效的目标值至少快700倍。目前的GI/TS实现产生的有吸引力的结果提供了一个显着的进步，我们的能力，有效地解决固定成本的网络问题，并邀请其用于更大的实例，从各种应用领域。
英文摘要：
We propose a new self-organizing algorithm for fixed-charge network flow problems based on ghost image (GI) processes as proposed in Glover (1994) and adapted to fixed-charge transportation problems in Glover, Amini and Kochenberger (2005). Our self-organizing GI algorithm iteratively modifies an idealized representation of the problem embodied in a parametric ghost image, enabling all steps to be performed with a primal network flow algorithm operating on the parametric GI. Computational tests are carried out on an extensive set of benchmark problems which includes the previous largest set in the literature, comparing our algorithm to the best methods previously proposed for fixed-charge transportation problems, though our algorithm is not specialized to this class. We also provide comparisons for additional more general fixed-charge network flow problems against Cplex 12.8 to demonstrate that the new self-organizing GI algorithm is effective on large problem instances, finding solutions with statistically equivalent objective values at least 700 times faster. The attractive outcomes produced by the current GI/TS implementation provide a significant advance in our ability to solve fixed-cost network problems efficiently and invites its use for larger instances from a variety of application domains.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
