Ms. Tugba Firdolas Profile

Tugba Firdolas

at Yýldýz Teknik Üniv

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Proceedings Article | 8 November 2005 Paper

An integrated conceptual framework for selecting reverse logistics providers in the presence of vagueness

Tugba Fırdolaş, Semih Önüt, Elif Kongar

Proceedings Volume 5997, 599709 (2005) https://doi.org/10.1117/12.632082

KEYWORDS: Neural networks, Fuzzy logic, Reverse modeling, Computer programming, Neurons, Manufacturing, Data modeling, Machine learning, Environmental management, Mathematical modeling

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In recent years, relating organization's attitude towards sustainable development, environmental management is gaining an increasing interest among researchers in supply chain management. With regard to a long term requirement of a shift from a linear economy towards a cycle economy, businesses should be motivated to embrace change brought about by consumers, government, competition, and ethical responsibility. To achieve business goals and objectives, a company must reply to increasing consumer demand for "green" products and implement environmentally responsible plans. Reverse logistics is an activity within organizations delegated to the customer service function, where customers with warranted or defective products would return them to their supplier. Emergence of reverse logistics enables to provide a competitive advantage and significant return on investment with an indirect effect on profitability. Many organizations are hiring third-party providers to implement reverse logistics programs designed to retain value by getting products back. Reverse logistics vendors play an important role in helping organizations in closing the loop for products offered by the organizations. In this regard, the selection of third-party providers issue is increasingly becoming an area of reverse logistics concept and practice. This study aims to assist managers in determining which third-party logistics provider to collaborate in the reverse logistics process with an alternative approach based on an integrated model using neural networks and fuzzy logic. An illustrative case study is discussed and the best provider is identified through the solution of this model.

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