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Weihong Guo

Bio: Weihong Guo is an academic researcher from Rutgers University. The author has contributed to research in topics: Supply chain & Welding. The author has an hindex of 13, co-authored 37 publications receiving 543 citations. Previous affiliations of Weihong Guo include University of Michigan & Tsinghua University.

Papers
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Journal ArticleDOI
TL;DR: In this article, the design and operational principles for reconfigurable manufacturing systems (RMSs) are formulated and a state-of-the-art review of the current design and operations methodologies of RMSs is provided.
Abstract: Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

254 citations

Journal ArticleDOI
TL;DR: This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments.
Abstract: Unmanned surface vehicle (USV) has witnessed a rapid growth in the recent decade and has been applied in various practical applications in both military and civilian domains. USVs can either be deployed as a single unit or multiple vehicles in a fleet to conduct ocean missions. Central to the control of USV and USV formations, path planning is the key technology that ensures the navigation safety by generating collision free trajectories. Compared with conventional path planning algorithms, the deep reinforcement learning (RL) based planning algorithms provides a new resolution by integrating a high-level artificial intelligence. This work investigates the application of deep reinforcement learning algorithms for USV and USV formation path planning with specific focus on a reliable obstacle avoidance in constrained maritime environments. For single USV planning, with the primary aim being to calculate a shortest collision avoiding path, the designed RL path planning algorithm is able to solve other complex issues such as the compliance with vehicle motion constraints. The USV formation maintenance algorithm is capable of calculating suitable paths for the formation and retain the formation shape robustly or vary shapes where necessary, which is promising to assist with the navigation in environments with cluttered obstacles. The developed three sets of algorithms are validated and tested in computer-based simulations and practical maritime environments extracted from real harbour areas in the UK.

81 citations

Journal ArticleDOI
TL;DR: A mixed-integer linear programming model, which aims to minimize the total cost of the “factory-in-a-box” supply chain, is presented in this study and it is demonstrated that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model.
Abstract: The “factory-in-a-box” concept involves assembling production modules (i.e., factories) in containers and transporting the containers to different customer locations. Such a concept could be highly effective during emergencies, when there is an urgent demand for products (e.g., the COVID-19 pandemic). The “factory-in-a-box” planning problem can be divided into two sub-problems. The first sub-problem deals with the assignment of raw materials to suppliers, sub-assembly decomposition, assignment of sub-assembly modules to manufacturers, and assignment of tasks to manufacturers. The second sub-problem focuses on the transport of sub-assembly modules between suppliers and manufacturers by assigning vehicles to locations, deciding the order of visits for suppliers, manufacturers, and customers, and selecting the appropriate routes within the transportation network. This study addresses the second sub-problem, which resembles the vehicle routing problem, by developing an optimization model and solution algorithms in order to optimize the “factory-in-a-box” supply chain. A mixed-integer linear programming model, which aims to minimize the total cost of the “factory-in-a-box” supply chain, is presented in this study. CPLEX is used to solve the model to the global optimality, while four metaheuristic algorithms, including the Evolutionary Algorithm, Variable Neighborhood Search, Tabu Search, and Simulated Annealing, are employed to solve the model for large-scale problem instances. A set of numerical experiments, conducted for a case study of “factory-in-a-box”, demonstrate that the Evolutionary Algorithm outperforms the other metaheuristic algorithms developed for the model. Some managerial insights are outlined in the numerical experiments as well.

73 citations

Journal ArticleDOI
TL;DR: Typical configurations of large-volume manufacturing systems for mechanical products are compared from cost, responsiveness and product quality perspectives, and two practical configurations are discussed – parallel serial lines, and reconfigurable manufacturing systems.
Abstract: When designing a new large manufacturing system with high throughput, the corporation should weigh several factors: the capital investment cost, the system’s responsiveness to future varying market demand, the production losses due to disruptive events, and the product quality. These performance metrics depend heavily on the system configuration. In this paper, typical configurations of large-volume manufacturing systems for mechanical products are compared from cost, responsiveness and product quality perspectives. In addition to traditional serial lines and pure parallel systems, we also discuss two practical configurations – parallel serial lines, and reconfigurable manufacturing systems. The results offer managerial insights for selecting the system configuration that creates the maximum economic value over the lifetime of the system, and fits the corporation needs and culture.

47 citations

Journal ArticleDOI
TL;DR: In this article, a monitoring algorithm targeting near-zero misdetection is developed by integrating univariate control charts and the Mahalanobis distance approach, which is capable of monitoring non-normal multivariate observations with flexible control limits.

41 citations


Cited by
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Journal ArticleDOI
Alan R. Jones1

1,349 citations

Journal ArticleDOI
TL;DR: In this article, the design and operational principles for reconfigurable manufacturing systems (RMSs) are formulated and a state-of-the-art review of the current design and operations methodologies of RMSs is provided.
Abstract: Reconfigurable manufacturing systems (RMSs), which possess the advantages of both dedicated serial lines and flexible manufacturing systems, were introduced in the mid-1990s to address the challenges initiated by globalization. The principal goal of an RMS is to enhance the responsiveness of manufacturing systems to unforeseen changes in product demand. RMSs are costeffective because they boost productivity, and increase the lifetime of the manufacturing system. Because of the many streams in which a product may be produced on an RMS, maintaining product precision in an RMS is a challenge. But the experience with RMS in the last 20 years indicates that product quality can be definitely maintained by inserting in-line inspection stations. In this paper, we formulate the design and operational principles for RMSs, and provide a state-of-the-art review of the design and operations methodologies of RMSs according to these principles. Finally, we propose future research directions, and deliberate on how recent intelligent manufacturing technologies may advance the design and operations of RMSs.

254 citations

Journal ArticleDOI
TL;DR: In this paper, a new paradigm aiming at going beyond traditional six-sigma approaches is proposed, which is extremely relevant in technology intensive and emerging strategic manufacturing sectors, such as aeronautics, automotive, energy, medical technology, micro-manufacturing, electronics and mechatronics.
Abstract: Manufacturing companies are continuously facing the challenge of operating their manufacturing processes and systems in order to deliver the required production rates of high quality products, while minimizing the use of resources. Production quality is proposed in this paper as a new paradigm aiming at going beyond traditional six-sigma approaches. This new paradigm is extremely relevant in technology intensive and emerging strategic manufacturing sectors, such as aeronautics, automotive, energy, medical technology, micro-manufacturing, electronics and mechatronics. Traditional six-sigma techniques show strong limitations in highly changeable production contexts, characterized by small batch productions, customized, or even one-of-a-kind products, and in-line product inspections. Innovative and integrated quality, production logistics and maintenance design, management and control methods as well as advanced technological enablers have a key role to achieve the overall production quality goal. This paper revises problems, methods and tools to support this paradigm and highlights the main challenges and opportunities for manufacturing industries in this context.

238 citations