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Location Decision Making and Transportation Route Planning Considering Fuel Consumption

机译:考虑燃料消耗的位置决策与运输路线规划

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摘要

This study presents the Location Routing Problem (LRP) for which we have created a model for the integration of locating facilities and vehicle routing decisions to solve the problem. The case study is the Palm Oil Collection Center, which is also important for the supply chain system. A mathematical model was made to minimize the total cost of a facility-opening cost, fixed cost of vehicle uses and fuel consumption cost. The fuel consumption cost relies on the distance and road conditions, in case of poor physical condition of a road, and its width, which can be affected the speed of the vehicle as well as the used fuel. Thus, we propose an Adaptive Large Neighborhood Search (ALNS) based on heuristic for solving the LRP. The ALNS method was tested with three datasets of samples divided into small, medium and large problems. Then, the results were compared with the results from the exact method by the Lingo program. The computational study indicated that the ALNS algorithm was competitive to the results of the Lingo for all instance sizes. Moreover, the ALNS was more effective than the exact method; approximately 99% in terms of processing time. We extended this approach to solve the case study, which was considered to be the largest problem, and the ALNS algorithm was efficient with acceptable solutions and short processing time. Therefore, the proposed method provided an effective solution to manage location routing decision of the palm oil collection center.
机译:本研究介绍了我们创建了用于解决问题的定位设施和车辆路径决策的模型的位置路由问题(LRP)。案例研究是棕榈油收集中心,这对供应链系统也很重要。进行了数学模型,以最小化设施开放成本,固定的车辆使用成本和燃料消耗成本的总成本。燃料消耗成本依赖于道路物理状况不佳的距离和道路状况,其宽度,这可能会影响车辆的速度以及使用的燃料。因此,我们提出了一种基于启发式的自适应大邻域搜索(ALNS)来解决LRP。用三个样本数据测试ALNS方法,分为小,中等和大问题。然后,将结果与Lingo程序的精确方法的结果进行了比较。计算研究表明,对于所有实例大小,ALN算法对Lingo的结果具有竞争力。此外,ALN比确切的方法更有效;在处理时间方面约为99%。我们扩展了这种方法来解决案例研究,被认为是最大的问题,ALN算法具有可接受的解决方案和简短的处理时间。因此,所提出的方法提供了一种有效的解决方案来管理棕榈油收集中心的位置路由决定。

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