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Abstract Factory in a box (FiB) is an emerging technology that meets the dynamic and diverse market demand by carrying a factory module on vehicles to perform on-site production near customers’ locations. It is suitable for meeting time-sensitive demands, such as the outbreak of disasters or epidemics/pandemics. Compared to traditional manufacturing, FiB poses a new challenge of frequently reconfiguring supply chain networks since the final production location changes as the vehicle carrying the factory travels. Supply chain network reconfiguration involves decisions regarding whether suppliers or manufacturers can be retained in the supply chain or replaced. Such a supply chain reconfiguration problem is coupled with manufacturing process planning, which assigns tasks to each manufacturer that impacts material flow in the supply chain network. Considering the supply chain reconfigurability, this article develops a new mathematical model based on nonlinear integer programming to optimize supply chain reconfiguration and assembly planning jointly. An evolutionary algorithm (EA) is developed and customized to the joint optimization of process planning and supplier/manufacturer selection. The performance of EA is verified with a nonlinear solver for a relaxed version of the problem. A case study on producing a medical product demonstrates the methodology in guiding supply chain reconfiguration and process planning as the final production site relocates in response to local demands. The methodology can be potentially generalized to supply chain and service process planning for a mobile hospital offering on-site medical services. 

Additive manufacturing systems are being deployed on a cloud platform to provide networked manufacturing services. This article explores the value of interconnected printing systems that share process data on the cloud in improving quality control. We employed an example of quality learning for cloud printers by understanding how printing conditions impact printing errors. Traditionally, extensive experiments are necessary to collect data and estimate the relationship between printing conditions vs. quality. This research establishes a multi-printer co-learning methodology to obtain the relationship between the printing conditions and quality using limited data from each printer. Based on multiple interconnected extrusion-based printing systems, the methodology is demonstrated by learning the printing line variations and resultant infill defects induced by extruder kinematics. The method leverages the common covariance structures among printers for the co-learning of kinematics-quality models. This article further proposes a sampling-refined hybrid metaheuristic to reduce the search space for solutions. The results showed significant improvements in quality prediction by leveraging data from data-limited printers, an advantage over traditional transfer learning that transfers knowledge from a data-rich source to a data-limited target. The research establishes algorithms to support quality control for reconfigurable additive manufacturing systems on the cloud. 

Drones are receiving popularity with time due to their advanced mobility. Although they were initially deployed for military purposes, they now have a wide array of applications in various public and private sectors. Further deployment of drones can promote the global economic recovery from the COVID-19 pandemic. Even though drones offer a number of advantages, they have limited flying time and weight carrying capacity. Effective drone schedules may assist with overcoming such limitations. Drone scheduling is associated with optimization of drone flight paths and may include other features, such as determination of arrival time at each node, utilization of drones, battery capacity considerations, and battery recharging considerations. A number of studies on drone scheduling have been published over the past years. However, there is a lack of a systematic literature survey that provides a holistic overview of the drone scheduling problem, existing tendencies, main research limitations, and future research needs. Therefore, this study conducts an extensive survey of the scientific literature that assessed drone scheduling. The collected studies are grouped into different categories, including general drone scheduling, drone scheduling for delivery of goods, drone scheduling for monitoring, and drone scheduling with recharge considerations. A detailed review of the collected studies is presented for each of the categories. Representative mathematical models are provided for each category of studies, accompanied by a summary of findings, existing gaps in the state-of-the-art, and future research needs. The outcomes of this research are expected to assist the relevant stakeholders with an effective drone schedule design.