@conference {Khaoula2020,
	title = {The integrated multi-plant production routing problem with simultaneous delivery and pickup},
	booktitle = {Proceedings - 2020 5th International Conference on Logistics Operations Management, GOL 2020},
	year = {2020},
	note = {cited By 1},
	abstract = {The Production Routing Problem (PRP) is concerned with coordinating the manufacturing, holding, distribution, and routing decisions to meet customer demands with an objective of minimizing the total operational costs for a defined period. The PRP can be expanded unaffectedly to concern reverse logistics and return flow processes, which typically consist on collecting used products or recyclable packaging from consumers to the depot or plant for a potential refurbishment, upgrading or recycling. Here, we will model and resolve to optimality the Multiple Plant Production Routing Problem in a Reverse Logistics context. {\textcopyright} 2020 IEEE.},
	keywords = {Customer demands, Manufacture, Multi-plants, Plant production, Return flow, Reverse logistics, Routing decisions, Routing problems, Used product},
	doi = {10.1109/GOL49479.2020.9314715},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85100174617\&doi=10.1109\%2fGOL49479.2020.9314715\&partnerID=40\&md5=439e942e353af832a22471f4c9ac7fb3},
	author = {Khaoula, B. and Mohammed, E.A. and Youssef, B.}
}
@conference {Kawtar2014171,
	title = {Stochastic Model Predictive Control for costs optimization in a supply chain under a stochastic demand},
	booktitle = {Proceedings of 2nd IEEE International Conference on Logistics Operations Management, GOL 2014},
	year = {2014},
	note = {cited By 1},
	pages = {171-175},
	abstract = {This paper aims to present a Stochastic Model Predictive Control to optimize the costs of shipping and storage in a supply chain. The goal is to minimize the objective function of the combined costs in a multi-stage and a multi-level supply chain responding to a stochastic multi-product demand. The simulation using Matlab provides a comparison between classical models and the proposed model, and shows that the Affine Recourse Stochastic Model Predictive Control - AR SMPC offers better results. {\textcopyright} 2014 IEEE.},
	doi = {10.1109/GOL.2014.6887436},
	url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908611786\&doi=10.1109\%2fGOL.2014.6887436\&partnerID=40\&md5=4a6eec4c578041a83cf910609deb9598},
	author = {Kawtar, T. and Said, A. and Youssef, B.}
}
