首页> 外文会议>International Fuzzy Systems Association., World Congress >Intuitionistic Fuzzy Model of Traffic Jam Regions and Rush Hours for the Time Dependent Traveling Salesman Problem
【24h】

Intuitionistic Fuzzy Model of Traffic Jam Regions and Rush Hours for the Time Dependent Traveling Salesman Problem

机译:无线堵塞地区的直观模糊模型,依赖于旅游推销员问题的流量堵塞地区和高峰时间

获取原文

摘要

The Traveling Salesman Problem (TSP) is one of the most extensively studied NP-hard graph search problems. Many researchers published numerous approaches for quality solutions, applying various techniques in order to find the optimum (least cost) or semi optimum solution. Moreover, there are many different extensions and modifications of the original problem, The Time Dependent Traveling Salesman Problem (TD TSP) is a prime example. TD TSP indeed was one of the most realistic extensions of the original TSP towards assessment of traffic conditions [1]. Where the edges between nodes are assigned different cost (weight), considering whether they are traveled during the rush hour periods or they cross the traffic jam regions. In such conditions edges are assigned higher costs [1]. In this paper we introduce an even more realistic approach, the IFTD TSP (Intuitionistic Fuzzy Time Dependent Traveling Salesman Problem); which is an extension of the classic TD TSP with the additional notion of intuitionistic fuzzy sets. Our core concept is to employ intuitionistic fuzzy sets of the cost between nodes to quantify traffic jam regions, and the rush hour periods. Since the intuitionistic fuzzy sets are generalizations of the original fuzzy sets [2], then our approach is a usefully extended, alternative model of the original abstract problem. By demonstrating the addition of intuitionistic fuzzy elements to quantify the intangible jam factors and rush hours, and creating an inference system that approximates the tour cost in a more realistic way [3]. Since our motivation is to give a useful and practical alternative (extension) of the basic TD TSP problem, the DBMEA (Discrete Bacterial Memetic Evolutionary Algorithm) was used in order to calculate the (quasi-)optimum or semi optimum solution. DBMEA has been proven to be effective and efficient in a wide segment of NP-hard problems, including the original TSP and the TD TSP as well [4]. The results from the runs based on the exte
机译:旅行推销员问题(TSP)是最广泛研究的NP硬图搜索问题之一。许多研究人员公布了许多质量解决方案方法,应用各种技术,以找到最佳(最低成本)或半最佳解决方案。此外,存在许多不同的扩展和修改原始问题,时间依赖于旅行的推销员问题(TD TSP)是一个主要示例。 TD TSP确实是原始TSP最逼真的扩展之一,以评估交通状况[1]。考虑到它们是否在高峰时段期间或穿过交通堵塞区域时,节点之间的边缘被分配不同的成本(重量)。在这种情况下,边缘被分配更高的成本[1]。在本文中,我们介绍了一种更现实的方法,IFTD TSP(直觉模糊时间依赖旅行推销员问题);这是Classic TD TSP的扩展,其具有直觉模糊集的附加概念。我们的核心概念是使用节点之间的成本的直觉模糊集,以量化交通堵塞区域,以及高峰时段。由于直觉模糊集是原始模糊集的概括[2],因此我们的方法是原始抽象问题的有用扩展,替代模型。通过演示加入直觉模糊元素来量化无形的卡纸因子和高峰时间,并创建推理系统,使推理系统近似于更现实的方式[3]。由于我们的动机是给出基本TD TSP问题的有用和实际的替代(扩展),因此使用DBMEA(离散细菌膜进化算法)以计算(准)最佳或半最佳解决方案。 DBMEA已被证明在广泛的NP硬问题中有效和高效,包括原始TSP和TD TSP [4]。基于exte的运行结果

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号