Session: DFMLC-03-01-Design for Supply Chain and End of Life Recovery

Paper Number: 91294

91294 - Utilizing Bayesian Inference to Optimization Manufacturing Facility Configuration and Task Sequencing in Product Remanufacturing 

The landscape of production has evolved drastically from its nascency. The emergence of diverse demand, globalization, environmental and alternative aspects of the global economy, constitutes greater complexity in manufacturing. The need for companies to stay competitive warrant robust business models and systems capable of accommodating uncertainty in markets. Increased attention to sustainability in manufacturing is promoting remanufacturing directives poised to extend product service life which could present uncertainty in supply. This paper proposes a framework/modeling approach to equip manufacturing systems to respond to uncertainty in market demand and supply, with motivation nested in remanufacturing techniques that mitigate compromise in stakeholder requirements whilst accommodating more sustainable practice. The proposed production model implements Bayesian inferential methods to enable data driven capability in the model accounting for uncertainty, heuristics methods in the form of genetic algorithms for adaptability to system deliverables, and discrete modeling approaches to simulate shop floor behaviour through the generation of sample paths. Additionally, this paper provides an overview of contemporary manufacturing in industry and research pertinent to sustainability, intended to present the reader with a clearer view of the landscape and where the research lays. This paper has been written as a precursor to ongoing development of the model presenting current results.

Presenting Author: Beshoy Morkos University of Georgia

Presenting Author Biography: N/A

Authors:

Toluwalase Olajoyegbe University of Georgia
Beshoy Morkos University of Georgia