The concept of digital supply chain gathers models, methods and tools to plan decisions on digital prototypes of supply chains. This doctoral dissertation proposes two contributions to digital supply chain. Mainly, our results address small and medium enterprises/industries. Firstly, we study two new problems related to service network design for short and local fresh food supply chains. For each of them we propose a Mixed Integer Linear Programming formulation. Decomposition-based methods are implemented in order to solve large scale instances. For each problem this approach is applied on a case study conducted with several local institutions. Secondly, we address the tactical supply chain planning problem: how to plan production, transportation and storage activities. As opposed to the classic centralized version, the decision making process is considered decentralized. We study how to decompose the decisions between actors as well as their individual behaviour. We also analyze negotiation processes based on limited information sharing. In order to address the double complexity of the problem, we propose an innovative tool coupling a multi-agent based simulation approach with optimization approaches based on mathematical programming.
