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首页> 外文期刊>International Journal of Computer Integrated Manufacturing >A multi-period reverse logistics optimisation model for end-of-life vehicles recovery based on EU Directive
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A multi-period reverse logistics optimisation model for end-of-life vehicles recovery based on EU Directive

机译：基于欧盟指令的报废车辆回收的多周期逆向物流优化模型

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摘要

The end-of-life (EoL) phase is a stage in every product lifecycle, where its management is affected by economical and environmental parameters. The main problem facing manufacturers is how to collect the EoL products and what to do with them in order to obtain the maximum economic benefits from their recovery and at the same time fulfilling the relevant legislations. By introduction of the European Union Directive on end-of-life vehicles (ELVs), the manufacturers are responsible for free take back and recovery of their vehicles. Implementing this Directive will impose new additional costs on manufacturers. In order to achieve an efficient management of the recovery process and minimising the costs, manufacturers should join with treatment facilities and hence creating a network. In this paper, these new cost drivers are established and then based on the number, location and the capacity of the collection centres, dismantlers and also the amount of materials flow between different facilities, a multi-period reverse logistics optimisation model is developed. A solution methodology has been developed based on a multiple start search algorithm where a heuristic method is performed in each iteration. Two procedures for improving the quality of the generated solutions are proposed. The first procedure is based on a sub-problem optimisation technique and the second one is a search algorithm.
机译：寿命终止（EoL）阶段是每个产品生命周期中的一个阶段，其管理受经济和环境参数的影响。制造商面临的主要问题是如何收集EoL产品以及如何对其进行处理，以便从其回收中获得最大的经济利益，同时又要遵守相关法规。通过引入有关报废车辆（ELV）的欧盟指令，制造商有责任免费收回和回收其车辆。实施该指令将给制造商带来新的额外成本。为了实现对回收过程的有效管理并最大程度地降低成本，制造商应加入处理设施，从而建立一个网络。在本文中，建立了这些新的成本动因，然后根据收集中心，拆除者的数量，位置和能力以及不同设施之间的物料流动量，开发了一个多期间逆向物流优化模型。已经基于多重开始搜索算法开发了一种解决方法，其中在每次迭代中都执行启发式方法。提出了两种方法来提高生成的解决方案的质量。第一个过程基于子问题优化技术，第二个过程是搜索算法。

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