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An experimental evaluation of heuristic algorithms for bus-depot matching problem of urban road transport systems

机译:城市道路交通系统公交站点匹配问题启发式算法的实验评估

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This research is motivated by a bus-depot matching problem observed in Urban Road Transport Systems (URTS). In URTS, buses are parked overnight at depots. Starting points of routes are usually different from depot locations. A bus has to cover the distance from its depot to the starting point of its route before being engaged on regular service. Likewise, buses usually do not provide service to the depot at the end of the service period. The distance travelled by a bus in a day from a depot to a starting terminus and/or from the ending or last terminus back to the depot without carrying passengers is known as 'dead kilometers'. The dead kilometers can be reduced by efficiently allocating the buses to depots. In the literature this problem is solved using mathematical model and heuristic algorithms. However, there is no detailed computational analysis to highlight the merits and demerits of various solution methodologies, so far addressed in the literature. In this study a set of heuristic algorithms are considered to make an efficient decisions in buses-depots matching problem. A computational experiment is carried out to understand the efficiency of the heuristic algorithms considered in this study for various large size problems in comparison with exact solutions. From the computational analysis, two out of the five heuristic algorithms considered in this study, resulted very close to exact solutions in most of the problem instances. All the heuristic algorithms considered in this study takes very meager computational time in Pentium IV for the large size problem of 30 depots and 5,031 buses considered in this study.
机译:这项研究的动机是在城市道路运输系统(URTS)中观察到的公交场站匹配问题。在URTS中,公交车在停车场停放过夜。路线的起点通常与仓库位置不同。在进行常规服务之前,公交车必须覆盖从其仓库到路线起点的距离。同样,公交车通常在服务期结束时不向仓库提供服务。一天中公交车从仓库到起始终点站和/或从终点或最后一个终点到仓库而没有载运乘客的距离称为“死角”。通过有效地将公交车分配到仓库,可以减少空载公里数。在文献中,这个问题是使用数学模型和启发式算法解决的。但是,目前尚无详细的计算分析来突出各种解决方案方法的优缺点。在这项研究中,一组启发式算法被认为可以在公交车场匹配问题中做出有效的决策。进行了计算实验,以了解与精确解相比,本研究中考虑的启发式算法对各种大型问题的效率。从计算分析来看,本研究考虑的五种启发式算法中有两种在大多数问题实例中都非常接近精确的解决方案。对于本研究中考虑的30个仓库和5,031辆公交车的大型问题,本研究中考虑的所有启发式算法在Pentium IV中花费的计算时间都很少。

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