Smart Manufacturing System

Real-time Resilient Fleet Management and Production Planning for Cobot-AMR Systems (February 2021 - February 2024)

Abstract: Mobility, literally speaking, refers to the ability to move from one location to another. It is key to the functioning of a livable and sustainable community. An efficient people-mover system is the backbone of a smart city that has been drawing worldwide attention. In anticipation of population growth and demographic changes, it is vital to develop an integrated and sustainable transport system that meets the diverse needs of the burgeoning population. The key lies in our ability to harness the capabilities of information communication technologies, factor resilience into infrastructure planning and management, retrofit existing infrastructure to promote greener commuting modes and develop innovative technologies in a timely manner to respond to people of all age groups. With all advanced transport related technologies achieved so far, fundamentally we are still facing the main challenge of how to ensure safety, comfort and affordability in terms of travel (time or money) cost, environmental footprint, and social impact. In this project we shall address two key problems from a systems and control perspective, i.e., how to discover and understand people's travel needs and commute patterns at a societal level via traffic analysis and prediction, and how to use traffic signal control, which is essentially a group control mechanism, to enhance safety, comfort and affordability of daily travels in a large complex traffic network.

Resilient Task Planning for Layout Changes in Smart Manufacturing (January 2020 - December 2021)

Abstract: In this project we shall address both modelling and optimization challenges related to low volume high mix manufacturing, and overcome the drawbacks of existing modelling and optimization techniques, i.e., the lack of computationally efficient real-time scheduling tools, which guarantees requirement compliance or behaviour correctness, and the lack of modelling tools that can automatically adapt to new system functionalities and user requirements after system reconfiguration. For this purpose we aim to achieve the following objectives: (1) to develop a hybrid hierarchical modelling framework, which combines both finite-state automata and programming models to ensure expressiveness, verifiability, modularity and computational friendliness for low volume high mix reconfigurable manufacturing. (2) To develop a novel modeling and optimization framework, which facilitates drag-and-play functionalities in smart manufacturing.

Hierarchical Modelling and Real-time Operation Planning for Low Volume High Mix Reconfigurable Manufacturing (July 2016 - June 2021)

Abstract: Industry 4.0 creates what has been called a “smart factory”. Smart factories make use of cyber-physical systems to monitor physical processes, giving a digital representation of the physical world. Smart factories create many new opportunities for industry. The digital representation of the factory allows different production strategies to be simulated and evaluated ahead of time. More sophisticated robotics allow for unprecedented amounts of customization in the production process, and thus greater customization of products to individual customers. New technologies provide new opportunities, but also new challenges. Previous strategies for coordinating production within a factory assume that large production runs of identical items being produced using the same process. New techniques need to optimally produce many small production runs of customized products. Furthermore these techniques must be automated wherever possible. New product recipes must be created quickly, and it is not practical to bring in expert in scheduling every time a new custom product is produced. In this project we investigate how DES can be used to quickly and easily model modern manufacturing systems. DES theory is already used to ensure that systems are both safe, and reliable. We extend DES theory in order to be able to optimally schedule production within the system. We are interested in developing new techniques for optimizing production within a smart factory so as to maximize throughput and machine usage in an environment which requires the production of varied and changing product types. Furthermore we seek to be able to model the system in a flexible way which allows the model to be updated with new product types easily by non experts.

Project Objectives

• To develop a hybrid hierarchical modelling framework, which combines both finite-state automata and programming models to ensure expressiveness, verifiability, modularity and computational friendliness for low volume high mix reconfigurable manufacturing.

• To develop a novel modeling and optimization framework, which facilitates drag-and-play functionalities in smart manufacturing.