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Tactical design of rail freight networks. Part I: Exact and heuristic methods

机译:铁路货运网络的战术设计。第一部分:精确和启发式方法

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摘要

The tactical planning of rail freight networks is studied with the help of non-convex optimization models which are difficult to solve using only exact methods. For this reason it is necessary to use heuristic methods to obtain the solution for realistic networks. In this paper, local search and Branch and Bound methods are compared. In Part I, the model and the methods are defined and tested. In Part II, the methods are compared among one another using statistical analysis. The problem is to decide the optimal assignment of the trains to the service network and simultaneously assign the demand of cars to the routes. The formulation includes some features such as car transfer and classification, different capacities and a limited fleet of trains available for use. Different heuristics have been developed to obtain the solution for the proposed model: Simulated Annealing, Tabu Search and a particular heuristic that will be mentioned with the name of ‘Descending’. The objective function becomes piece-wise linear if the investment cost to purchase trains is considered. For small size networks a reformulation of the problem is studied to compare the heuristic approaches with the exact solution provided by Branch and Bound. To solve large networks, only heuristic methods can be used.
机译:在非凸优化模型的帮助下研究了铁路货运网络的战术规划,这些模型仅使用精确的方法很难解决。因此,有必要使用启发式方法来获得现实网络的解决方案。在本文中,比较了局部搜索和分支与绑定方法。在第一部分中,定义并测试了模型和方法。在第二部分中,使用统计分析对方法进行了比较。问题在于确定火车到服务网络的最佳分配,同时将汽车的需求分配给路线。该表述包括一些功能,例如汽车调动和分类,不同的容量以及可供使用的有限的火车车队。已经开发了不同的启发式方法来获得所建议模型的解决方案:模拟退火,禁忌搜索和一种特殊的启发式方法,其名称将为“降序”。如果考虑购买火车的投资成本,则目标函数变为分段线性。对于小型网络,研究了问题的重新表述,以将启发式方法与Branch and Bound提供的确切解决方案进行比较。为了解决大型网络,只能使用启发式方法。

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