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Modelling and Solving the Senior Transportation Problem

机译:建模和解决高级运输问题

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This paper defines a novel transportation problem, the Senior Transportation Problem (STP), which is inspired by the elderly door-to-door transportation services provided by non-profit organizations. Building on the vehicle routing literature, we develop solution approaches including mixed integer programming (MIP), constraint programming (CP), two logic-based Benders decompositions (LBBD), and a construction heuristic. Empirical analyses on both randomly generated datasets and large real-life datasets are performed. CP achieved the best results, solving to optimality 89% of our real-life instances of up to 270 vehicles with 385 requests in under 600 s. The best LBBD model can only solve 17% of those instances to optimality. Further investigation of this somewhat surprising result indicates that, compared to the LBBD approaches, the pure CP model is able to find better solutions faster and then is able to use the bounds from these sub-optimal solutions to reduce the search space slightly more effectively than the decomposition models.
机译:本文定义了一个新的运输问题,即高级运输问题(STP),该问题是受非营利组织提供的老年人门到门运输服务的启发。基于车辆路线文献,我们开发了解决方案方法,包括混合整数规划(MIP),约束规划(CP),两个基于逻辑的Benders分解(LBBD)和构造启发式方法。对随机生成的数据集和大型现实数据集都进行了经验分析。 CP取得了最佳结果,在600 s内将多达270辆车辆的实际实例中的89%响应了385个请求,从而达到了最优。最好的LBBD模型只能将其中17%的实例求解为最优。对这种令人惊讶的结果的进一步研究表明,与LBBD方法相比,纯CP模型能够更快地找到更好的解决方案,然后能够使用这些次优解决方案的界限来稍微有效地减少搜索空间分解模型。

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