The Additive Manufacturing (AM) scheduling problem is becoming a very felt issue not only by the scholars but also by the practitioners who are looking to this new technology as a new integrated part of their traditional production systems. They need new scheduling models to adapt the traditional scheduling rules to the changed ones of the additive manufacturing. This paper deals with the enhancement of a scheduling problem for additive manufacturing just present in literature and the presentation of a new meta-heursitic (adapted to the new requirements of the additive manufacturing technology) based on the tabu-search algorithms.

Fashion and Apparel Supply Chains work in a very fast-changing environment and always demand better quality, higher availability of products, broader assortments and shorter delivery times. An efficient Supply Chain Management can make a difference between success and failure in the market. In this context, the main purposes of the presented work are: (i) to define the physical and informative flows, together with connected cost and revenue items, which characterize a Fashion Supply Chain working with a wide network of direct-operated or franchising mono-brand stores and (ii) to optimize Supply Chain performances through a responsive approach which, during the sales season, analyses actual market demand and adjusts operations plans accordingly. The framework aims at becoming a decision support system for the optimization of the performances of a process that starts from the development of the collection by the Styling Office and ends with the withdrawal of unsold items from the stores. In order to analyze the performances under different scenarios, a set of Key Performance indicators, partially selected from the SCOR Model, is defined.