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Fuzzy Optimisation of Reverse Logistics Networks

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If you have a question about this talk, please contact David Greaves.

Reverse logistics concerns the return and integration of used or obsolete products back to a supply chain. Although the concept of reuse of products and materials is not new, it has been gaining increasing attention in the last decade or so, from various industries, such as automotive manufacturing, electronics and consumer appliance industries. Two main reasons for importance of product recovery are: (1) environmental; growth of environmental awareness has led to development of new legislation concerning waste reduction requirements, take-back obligations, and disposal practices, and (2) economical; reverse logistics has been used as a competitive weapon for effective reduction of the production cost and savings in raw material.

We have considered a generic type of reverse logistics network. It consists of a traditional forward production route, inspection of returned products, and three return routes including repair, remanufacturing and disposal routes. The route which a returned product will take depends on its quality. Returned products of better quality are repaired, and of lower quality are remanufactured or disposed. It is assumed that demand and return quantities are uncertain. They are specified by linguistic terms and modelled using fuzzy numbers.

We have developed a novel two phase fuzzy mixed integer optimisation algorithm for decision making in the reverse logistics network. We have carried out various numerical experiments and sensitivity analysis to better understand the effects of quality of returns and reverse logistics network parameters on the network performance.

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This talk is part of the Wednesday Seminars - Department of Computer Science and Technology series.

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