An analytical model based on variational principles for a thin-walled stiffened plate subjected to axial compression is presented. A system of nonlinear differential and integral equations is derived and solved using numerical continuation. The results show that the system is susceptible to highly unstable local-global mode interaction after an initial instability is triggered. Moreover, snap-backs in the response showing sequential destabilization and restabilization, known as cellular buckling or snaking, arise. The analytical model is compared with static finite element (FE) models for joint conditions between the stiffener and the main plate that have significant rotational restraint. However, it is known from previous studies that the behaviour, where the same joint is insignificantly restrained rotationally, is captured better by an analytical approach than by standard FE methods; the latter being unable to capture cellular buckling behaviour even though the phenomenon is clearly observed in laboratory experiments.

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A recently developed nonlinear analytical model for axially loaded thin-walled stringer-stiffened plates based on variational principles is extended to include local buckling of the main plate. Interaction between the weakly stable global buckling mode and the strongly stable local buckling mode is highlighted. Highly unstable post-buckling behaviour and a progressively changing wavelength in the local buckling mode profile is observed under increasing compressive deformation. The analytical model is compared against both physical experiments from the literature and finite element analysis conducted in the commercial code Abaqus; excellent agreement is found both in terms of the mechanical response and the predicted deflections.

For complex manufacturing systems, the current hybrid Agent-Based Modelling and Discrete Event Simulation (ABM-DES) frameworks are limited to component and system levels of representation and present a degree of static complexity to study optimal resource planning.To address these limitations, a modular hybrid simulation framework for complex manufacturing system design is presented. A manufacturing system with highly regulated and manual handling processes, composed of multiple repeating modules, is considered. In this framework, the concept of modular hybrid ABM-DES technique is introduced to demonstrate a novel simulation method using a dynamic system of parallel multi-agent discrete events. In this context, to create a modular model, the stochastic finite dynamical system is extended to allow the description of discrete event states inside the agent for manufacturing repeating modules (meso level). Moreover, dynamic complexity regarding uncertain processing time and resources is considered. This framework guides the user step-by-step through the system design and modular hybrid model. A real case study in the cell and gene therapy industry is conducted to test the validity of the framework. The simulation results are compared against the data from the studied case; excellent agreement with 1.038% error margin is found in terms of the company performance. The optimal resource planning and the uncertainty of the processing time for manufacturing phases (exo level), in the presence of dynamic complexity is calculated.

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Sustaining and optimising complex systems are often challenging problems as such systems contain numerous variables that are interacting with each other in a nonlinear manner. Application of integrated sustainability principles in a complex system (e.g. the Earth's global climate, social organisations, Boeing's supply chain, automotive products and plants' operations, etc.) is also a challenging process. This is due to the interactions between numerous parameters such as economic, ecological, technological, environmental and social factors being required for the life assessment of such a system. Functionality and flexibility assessment of a complex system is a major factor for anticipating the systems' responses to changes and interruptions. This study outlines generic mathematical and computational approaches to solving the nonlinear dynamical behaviour of complex systems. The goal is to explain the modelling and simulation of system's responses experiencing interaction change or interruption (i.e. interactive disruption). Having this knowledge will allow the optimisation of systems' efficiency and would ultimately reduce the system's total costs. Although, many research works have studied integrated sustainability behaviour of complex systems, this study presents a generic mathematical and computational framework to explain the behaviour of the system

In manufacturing supply chains with labour-intensive operations and processes, individuals perform various types of manual tasks and quality checks. These operations and processes embrace engagement with various forms of paperwork, regulation obligations and external agreements between multiple stakeholders. Such manual activities can increase human error and near misses, which may ultimately lead to a lack of productivity and performance. In this paper, a multi-agent cyber-physical system (CPS) architecture with radio frequency identification (RFID) technology is presented to assist inter-layer interactions between different manufacturing phases on the shop floor and external interactions with other stakeholders within a supply chain. A dynamic simulation model in the AnyLogic software is developed to implement the CPS-RFID solution by using the agent-based technique. A case study from cryogenic warehousing in cell and gene therapy has been chosen to test the validity of the presented CPS-RFID architecture. The analyses of the simulation results show improvement in efficiency and productivity, in terms of resource time-in-system.