Titolo della tesi: Mathematical optimization and learning models to address uncertainties and sustainability of supply chain management
Green Supply Chain Management can achieve its goals through innovative management approaches that consider sustainable efficiency and profitability to be clearly linked by the savings that result from applying optimization techniques. Besides sustainability, uncertainty is another critical issue in Green Supply Chain Management. Considering a deterministic approach would definitely fail to provide concrete decision support when modeling those kinds of scenarios. According to various hypotheses and strategies, uncertainties can be addressed by exploiting several modeling approaches arising from statistics, statistical learning and mathematical programming. In this dissertation, mathematical and learning models are exploited according to different approaches and models combinations, providing new formulations and frameworks to address strategic and operational problems of GSCM under uncertainty. All the models and frameworks presented in this dissertation have been tested on real-world instances.