Research On Forth Party Reverse Logistics And Its Key Techniques

Reverse logistics is becoming more and more important in industry because it has great economic and environmental benefits. Due to the features of reverse logistics such as complexity and uncertainty, a new mode of reverse logistics is needed. As a new mode of supply chain management, forth party logistics (4PL) has a strong capability to integrate resources, to provide best supply chain solution. It has great potential application in reverse logistics.The current modes of reverse logistics and their characteristics are summarized. Based on the discussion of limitations of each mode, forth party reverse logistics based on eco-non-profit organization is proposed. Three eco-non-profit organizations that might be the potential fourth party reverse logistics provider are yielded.The design of reverse logistics network which affects efficiency and cost of reverse logistics operation is one of the important issues in reverse logistics. In order to deal with the uncertainty of quantities and qualities of returns, a method is proposed to divide the recycled products into remanufacturables and wastes according to their quality. The quantities of each sort are regarded as random. A stochastic programming model aiming at minimizing the total cost of investment and transportation cost is submitted. Accordingly, the numbers, locations and volumes of assigned goods of various logistics facilities are yielded. A hybrid intelligent algorithm consisting of three steps is submitted to solve this model. A case study is given to verify the model and the algorithm.The matching reverse logistics assignments with resources of third party logistics providers is another essential issue of fourth party reverse logistics. It is very difficult for reverse logistics to reach scale economies due to the features of reverse logistics e.g. variety of return goods in small batch and scattered places etc. In order to reduce operation costs of fourth party reverse logistics, a two-step method is proposed. The first-step method is to adopt a clustering method to consolidate scattered reverse logistics assignments into large scale. As every third party logistics providers have its core competition such as collecting, transporting or warehousing, it is reasonable to assign reverse logistics tasks to the most suitable third party logistics provider. Therefore, the second-step method is to decompose a complex reverse logistics assignment into several individual logistics tasks and to them to the most suitable third party logistics provider. A model of matching the reverse logistics task with the resource of third party logistics is proposed and Genetic algorithm is designed. The validity of proposed model and algorithm are verified by a corresponding case study.At last, a summary of the whole thesis is given and the future research directions are outlined.