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首页> 美国卫生研究院文献>other >Introducing the MCHF/OVRP/SDMP: Multicapacitated/Heterogeneous Fleet/Open Vehicle Routing Problems with Split Deliveries and Multiproducts
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Introducing the MCHF/OVRP/SDMP: Multicapacitated/Heterogeneous Fleet/Open Vehicle Routing Problems with Split Deliveries and Multiproducts

机译：引入MCHF / OVRP / SDMP：多容量/异构机队/开放式运输路线问题包括分批交付和多种产品

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摘要

In this paper, we analyze a real-world OVRP problem for a production company. Considering real-world constrains, we classify our problem as multicapacitated/heterogeneous fleet/open vehicle routing problem with split deliveries and multiproduct (MCHF/OVRP/SDMP) which is a novel classification of an OVRP. We have developed a mixed integer programming (MIP) model for the problem and generated test problems in different size (10–90 customers) considering real-world parameters. Although MIP is able to find optimal solutions of small size (10 customers) problems, when the number of customers increases, the problem gets harder to solve, and thus MIP could not find optimal solutions for problems that contain more than 10 customers. Moreover, MIP fails to find any feasible solution of large-scale problems (50–90 customers) within time limits (7200 seconds). Therefore, we have developed a genetic algorithm (GA) based solution approach for large-scale problems. The experimental results show that the GA based approach reaches successful solutions with 9.66% gap in 392.8 s on average instead of 7200 s for the problems that contain 10–50 customers. For large-scale problems (50–90 customers), GA reaches feasible solutions of problems within time limits. In conclusion, for the real-world applications, GA is preferable rather than MIP to reach feasible solutions in short time periods.
机译：在本文中，我们分析了生产公司的实际OVRP问题。考虑到现实世界中的限制，我们将问题分类为多容量/异构机队/开放式运输路线问题，其中包括分开交付和多产品（MCHF / OVRP / SDMP），这是OVRP的新分类。我们针对该问题开发了混合整数编程（MIP）模型，并考虑了实际参数，以不同大小（10–90个客户）生成了测试问题。尽管MIP能够找到小规模（10个客户）问题的最佳解决方案，但是当客户数量增加时，该问题将变得更难解决，因此MIP不能为包含10个以上客户的问题找到最佳解决方案。此外，MIP无法在时限（7200秒）内找到任何可行的解决大规模问题（50-90个客户）的方案。因此，我们针对大规模问题开发了一种基于遗传算法（GA）的解决方案。实验结果表明，基于遗传算法的方法可以成功解决方案，平均差距为9.66％，平均间隔392.8，而不是包含10-50个客户的问题的间隔为7200。对于大规模问题（50-90个客户），GA可以在时限内提供可行的问题解决方案。总而言之，对于现实世界的应用而言，GA优于MIP可以在短时间内达成可行的解决方案。

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