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Applied Artificial Bee colony algorithm for multiple capacitated vehicle routing problem: Case study of the plastic packaging industry

机译:应用人工蜂群算法求解多容量车辆路径问题:以塑料包装行业为例

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The aim of this research is to investigate the transportation problem of a plastic packaging industry case study because at present, competition within the plastic packaging business requires good services and on time delivery. Therefore, customer complaints and delayed deliveries are major problems for this industry group and that is why it is required to create more reliability and satisfaction for customers. Nowadays, this industry engages a transport supplier for goods delivery but it still has high transportation cost problems due to vehicle routing management. Artificial Bee colony algorithm is applied to overcome this issue. This algorithm is verified by using Vehicle Routing Problem benchmark (VRP) in terms of minimized total traveling distance. Then, for presenting high efficiency, this algorithm was validated for good performance compared with other algorithms. After algorithm validation, Artificial Bee colony algorithm is employed to solve the vehicle routing problem that has various vehicle capacities in the plastic packaging industry case study. The experimental results show that the purposed algorithm can be used to tackle the transportation problem in terms of minimizing total traveling distance and transportation costs, and also enhancing quality service levels and vehicle utilization for the multi-capacitated Vehicle Routing Problem.
机译:这项研究的目的是调查塑料包装行业案例研究中的运输问题,因为目前,塑料包装行业内的竞争需要优质的服务和准时的交货期。因此,客户投诉和交货延迟是该行业组的主要问题,这就是为什么需要为客户创造更高的可靠性和满意度。如今,该行业聘请运输供应商来进行货物交付,但是由于车辆路线管理,它仍然存在较高的运输成本问题。人工蜂群算法被用来克服这个问题。通过使用“车辆路径问题基准”(VRP),以最小的总行驶距离验证了该算法。然后,为了表现出较高的效率,与其他算法相比,该算法经验证具有良好的性能。经过算法验证后,在塑料包装行业案例研究中,采用人工蜂群算法解决了具有多种车辆承载能力的车辆路径问题。实验结果表明,针对多容量车辆路径问题,该算法既可用于解决运输问题,又可最大程度地减少总行驶距离和运输成本,并提高质量服务水平和车辆利用率。

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