Pursuing sustain ability, the traditional “take- make-use-dispose” economy model is moving towards a circular approach by introducing the reverse flow for recovery of End- of-Life (EOL) or End-of-Use (EOU) products. This circularity of parts and information between industry and market has generated the Closed-Loop Supply Chain (CLSC). Circularity enhances reuse/recycle of parts and materials and products. However, it increases complexity in the supply as it inputs uncertainty sources regarding the returning and outgoing state of products, the required recovery processes, the expected consumer behavior and the unstable market trends. In this paper, an investigation of the uncertainties affecting CLSC and remanufacturing was selected. Authors aim at mapping, and reporting in connectivity, the sources of uncertainty affecting remanufacturing systems in order to identify the connections and causality relations between them. A holonic representation of the CLSC was elaborated for potential interdependencies assessment. Thus, a map of the uncertainties is then designed in the form of a labyrinth connecting the uncertainty drivers with the corresponding entities/holons: markEt, Consumer, Management, pRoduct and Process. The map is configured as a decision-support tool for application in remanufacturing context. The map allows to control the mutual effects between uncertainty sources in remanufacturing business environments.

The folding and transport of proteins in the Endoplasmic Reticulum (ER) of mammalian cells exhibit similarities to industrial manufacturing processes, in that they are complex systems regulated by control mechanisms. Recently, two such control systems have been identified: the Unfolded Protein Response (UPR) and AutoRegulation of ER eXport (AREX), which allow the ER to adapt to fluctuations and stress. However, the challenges of modeling their activities arise from the lack of data and the complexity of the signaling pathways that activate them. In this study, we utilize a simulation tool commonly employed in manufacturing plants to develop a model that replicates the protein production process in the ER and the actions of the UPR and AREX in mitigating stress conditions. Our simulations provide insights into the behavior of the cell and represent the first attempt to integrate the entire protein production process and the control activity in the ER. The simulation results demonstrate the potential of regarding the ER as a manufacturing process and provide a novel approach to understanding the complex regulation of the ER.

As a disruptive technology, additive manufacturing (AM) is revolutionizing manufacturing supply chains. AM consists of producing 3-dimensional objects through layer-by-layer addition of compound material based on digital models. The scheduling of AM operations differs from traditional (i.e., subtractive and injection molding) manufacturing with a single production run involving several parts/geometries; this makes the jobs heterogeneous. Limited studies have investigated the Additive Manufacturing Scheduling Problems (AMSP). This study extends the Iterated Greedy algorithm to solve the AMSPs considering a single-machine production setting. For this purpose, several computational mechanisms are customized to account for AM-specific characteristics of production scheduling. Numerical analysis shows that the vast majority of the best-found solutions are yielded by the Adjusted Iterated Greedy (AIG) algorithm considering both solution quality and stability; the outperformance becomes more significant with an increase in problem size. Statistical analysis confirms that AIG’s performance is notably better than that of the existing solution algorithm in terms of solution quality and stability. This study is concluded by providing directions for future development of AM and AMSPs to extend the industrial reach of 3D printing technology.