In this dissertation, we investigate the optimal design of efficient and sustainable supply chains, which has been recognized as one of the most critical long-term strategic objective in today's business.

For the efficient Supply Chain Network Design (SCND), we propose a comprehensive model that captures significant strategic decisions involved in designing or re-designing high performance supply chains from the perspective of the manufacturer. The problem setting considers deterministic demand estimates by multiple customers, for multiple products, over the periods of a long term horizon. The strategic decisions involve selection of raw material suppliers, establishment or resizing of production facilities and/or selection of production subcontractors, establishment/resizing of distribution centers and/or subcontracting of the related activities, and selection of transportation modes and routes. The problem is formulated by a MILP model. Its objective is to minimize the overall costs associated with procurement, production, inventory, warehousing, and transportation over the design horizon. Appropriate constraints model the complex relationships among the links of the supply chain. In order to test the value of the proposed model in tackling the very significant complexities of current business reality, we apply the proposed model to a large case study of a global manufacturing firm. This validation unveils all application challenges (e.g. determining appropriate model parameters that reliably reflect the firm’s environment), and provides valuable insights into the efficient transformation of the firm’s current supply chain network. The network design obtained from solving the model is analyzed under variations of key parameters to determine its robustness.

For the Sustainability in Supply Chain Network Design (SSCND), we propose a new Multi-objective MMILP model, which captures significant decisions involved in designing or re-designing high performance, sustainable supply chains. The cost objective includes investment, operational, as well as emissions costs. The environmental objective captures emission quantities and waste generation at each link of the supply chain. The social objective considers employment opportunities, prioritizing societal community development and improved labor conditions. To solve the proposed model we employ both goal programming and the ε-constraint method to achieve efficient trade-offs among the three objectives. We have successfully applied the proposed model to a large case study of a global manufacturer. The goal programming method results in both economic and environmental cost improvements, while maintaining social costs under control. The ε-constraint method provides the opportunity to regulate the expenditures related to environmental and social strategies. Despite its high complexity, the case study results validate the ability of the proposed model and method to re-design high performing sustainable supply chains.