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Manufacturing processes may be viewed as operational units in the overall manufacturing/production system. Changes in technology, production patterns, and process settings are typically made at the process (unit) level. Very often, environmental problems are apparent when the manufacturing/production system is viewed as a whole (many units joined together). However, with the units aggregated to form a system, it is often difficult to identify the source of an environmental problem or judge the singular effect of changes to a process unit; the changes become lost in the complexity of the system as a whole. Recent efforts have employed input–output modeling to describe the flow of materials and the environmental consequences associated with manufacturing processes. A method is introduced for aggregating process-level material input–output models to form a combined material input–output model for a manufacturing system. The resulting model serves as a bridge between unit-level changes and broader system behaviors. The model form permits identification of opportunities for reducing environmental impacts at the process level (e.g., reduction of emissions, waste generation, and material use) and driving the system toward zero emissions based on an examination of the aggregated manufacturing system level model. Case studies are used to illustrate the application of the aggregated material input–output model to minimize waste and resource consumptions.

This paper proposes a position tracking control approach of AGV's platform using neural network The control problem of AGV's active suspension is first analysed. Then a neural network control scheme for controlling active suspension is presented. The controller adopts a multilayer back-propagation neural network To obtain the fastest possible speed of convergence and to meet the need of real-time control, a prediction-correction method for adjusting learning parameters is developed. Finally, various manoeuvres of the AGV are simulated to evaluate the performance of the controller. The simulation results indicate that the neural network control scheme is feasible. The neuro-controller can accommodate the variation of operational conditions and environment.