Bir parti büyüklüğü oyununda özgün makine öğrenmesi tekniklerini kullanarak maliyetlerin dağıtılması

In supply chain management (SCM), effective resource utilization is the key to achieving certain strategic benefits such as minimizing costs, increasing service levels, reducing inventories, increasing responsiveness, and finally improving customer satisfaction. Collaborative approaches among supply chain entities have become increasingly popular to increase resource utilization. In this thesis, we analyze a collaborative production setting where several companies facing varying demands throughout a finite planning horizon attempt to reduce their procurement costs by ordering from a common supplier. As the capacity of the common supplier is better utilized in such a collaborative solution, it will yield benefits that will be shared by the collaborators. Our objective is to design a cost allocation framework to ensure the sustainability of the collaborative purchasing organization. We propose various methods, including novel and computationally efficient machine learning based methods, using two different architectures, gradient boosting mechanism, and artificial neural networks which ensure the scalability of the proposed framework. We perform an extensive computational study and observe that our proposed method significantly outperforms the generic methods in the literature in terms of solution quality and computation time.