A Genetic-Fuzzy Framework for Optimization of Reverse Logistics Networks with Multiple Recovery Routes

Ali Niknejad, Dobrila Petrovic

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Recent changes in legislation and public awareness toward more environmental friendly and sustainable industry have contributed to growing attention to reverse logistics operations. Characteristics of the return flow (such as varying quality of returned products, uncertainty in a volume of returned products, etc) and the complexity of integrating reverse and forward network flows have led to complex optimization problems. In this paper, an inventory control model for a reverse logistics network with distinguishable quality levels of returned products, two alternative recovery routes including repair and remanufacturing routes and a disposal route, in addition to the traditional production route, is proposed. A genetic-fuzzy framework is developed to optimize the network in a multi objective optimization setting with respect to three objectives; cost, environmental emission and customer service level. A multi objective genetic algorithm (MOGA) is developed to design fuzzy controllers to generate the Pareto optimal solutions. Various demand and return rates and their impacts on the network performance are investigated.
Original languageEnglish
Title of host publicationProceedings of the 53rd Conference of the Operational Research Society 2011
PublisherOperational Research Society
Pages53-38
Publication statusPublished - 2011

Bibliographical note

This paper was given at the 53rd Conference of the Operational Research Society 2011. The paper is not available on the repository

Fingerprint Dive into the research topics of 'A Genetic-Fuzzy Framework for Optimization of Reverse Logistics Networks with Multiple Recovery Routes'. Together they form a unique fingerprint.

  • Cite this

    Niknejad, A., & Petrovic, D. (2011). A Genetic-Fuzzy Framework for Optimization of Reverse Logistics Networks with Multiple Recovery Routes. In Proceedings of the 53rd Conference of the Operational Research Society 2011 (pp. 53-38). Operational Research Society.