Solving the Multi-Objective Travelling Salesman Problem with Real Data Application

Barraq Subhi Kaml, M. Ibrahim
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引用次数: 2

Abstract

The aim of this paper is building a mathematical model for Travelling salesman problem (TSP) with multi-objective; the model describes the problem of (TSP) with three objectives (cost, distance, time), Real data were collected with a sample of twenty states of United State of America, Three methods were used (Branch and Bound algorithm, Nearest neighbor and two-way exchange improvement heuristic), The comparison was conducted among results reached. To solve the problem multi-objective of (TSP), The weighted model demonstrated the effectiveness and flexibility to solve real problems of multi-objective (TSP), where it can be said that it is impossible to solve this problem without resorting to multiple -objective mathematical models, In other words, the number of possible rout for the 20 town is , to find the optimal routs among these routs it takes very long time and a lot of effort, here stand out importance of two-way exchange improvement heuristic algorithm, where this rout is satisfactory to the decision maker in terms of cost, distance and time.
用实际数据解决多目标旅行商问题
本文的目的是建立具有多目标的旅行商问题的数学模型;该模型以成本、距离、时间三个目标来描述(TSP)问题,以美国20个州为样本,采用了三种方法(分支定界法、最近邻法和双向交换改进启发式法),并对所得结果进行了比较。为了解决多目标(TSP)问题,加权模型显示了解决实际多目标(TSP)问题的有效性和灵活性,其中可以说,不借助多目标数学模型是不可能解决这个问题的,也就是说,20个城镇可能的路由数为,在这些路由中找到最优路由需要很长时间和大量的精力。这里突出了双向交换改进启发式算法的重要性,这种路由在成本、距离和时间方面都让决策者满意。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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