arXiv AI By Ger\c{c}ek Budak (Department of Industrial Engineering, Ankara Y{\i}ld{\i}r{\i}m Beyaz{\i}t University, Ke\c{c}i\"oren, Ankara 06010, T\"urkiye), Faraz Gholamzadeh Gharehgheshlaghi (Department of Industrial Engineering, Ankara Y{\i}ld{\i}r{\i}m Beyaz{\i}t University, Ke\c{c}i\"oren, Ankara 06010, T\"urkiye), Melika Barjesteh Vaezi (Department of Kinesiology and Sport Management, Texas Tech University, Lubbock, TX, United States), Ahmad Gholizadeh Lonbar (Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA)

Integrating Deep Learning Demand Forecasting with Multi-Objective Optimization for Circular Coffee Supply Chains: A Data-Driven Framework for Cost, Emissions, and Freshness Management

Read the original on arXiv AI →

arXiv:2606. 08314v1 Announce Type: new Abstract: The coffee supply chain is one of the most complex agri-food networks, marked by geographically dispersed production, multi-tier coordination, and high sensitivity to quality and freshness.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.