An Optimization Model for the Design of Additive Manufacturing Supply Chains The continuous adoption of Additive Manufacturing (AM) can enhance Supply Chainxe2x80x99s (SC) effectiveness, adaptability and competitiveness. AM allows for a decentralized SC, bringing production centres nearer to customers, increasing products availability and decreasing inventory level and lead time. However, the integration of SC and AM brings difficulties, leading to the need of a completely new SC design. This paper proposes an optimization model supporting the design of spare parts SCs operating under a MakeToOrder (MTO) strategy. The proposed approach considers the decision of deploying productive resources (3D printers) in locations of a spare parts SC. The problem is represented as a combination of the pmedian and locationallocation optimization models, which are solved using a Mixed Integer Linear Programming (MILP). The approach is tested in two scenarios from a realworld use case of an elevator maintenance service provider. Obtained results demonstrated the promising capabilities of the proposed approach for handling the new design challenges arising from the forthcoming widespread use of 3D printers in manufacturing SCs.

A Robust Optimization Model for ClosedLoop Supply Chain Network Under Uncertain Returns. An effective closedloop supply chain (CLSC) is increasingly important for corporate sustainable development, where used products are returned, remanufactured andor recycled. Optimized planning of CLSC network is required and is influenced by uncertainties of recovery. So, this study seeks to establish a robust optimal model for CLSC network, considering both uncertainties in quantity and quality of returned products. After the general framework of CLSC is discussed, the measurements for uncertain quantity and quality of returned products are formulated mathematically, respectively as a number of discrete scenarios and Quality Index. A mixed integral linear programming (MILP) model for a CLSC network design is established and then translated into a robust optimization model based on regret value, to determine facilitiesxe2x80x99 locations and quantity of flows between facilities in the network. A numerical example is given, and the simulation results show that the operation strategies of the CLSC are relatively stable under different recycling scenarios. Therefore, the optimization model for CLSC network has good robustness.