Multi-agent neural business control system

Small to medium sized companies require a business control mechanism in order to monitor their modus operandi and analyse whether they are achieving their goals. A tool for the decision support process was developed based on a multi-agent system that incorporates a case-based reasoning system and automates the business control process. The case-based reasoning system automates the organization of cases and the retrieval stage by means of a Maximum Likelihood Hebbian Learning-based method, an extension of the Principal Component Analysis which groups similar cases by automatically identifying clusters in a data set in an unsupervised mode. The multi-agent system was tested with 22 small and medium sized companies in the textile sector located in the northwest of Spain during 29 months, and the results obtained have been very satisfactory

Sustainable Inventory Management Model for High-Volume Material with Limited Storage Space under Stochastic Demand and Supply

Inventory management and control has become an important management function, which is vital in ensuring the efficiency and profitability of a company’s operations. Hence, several research studies attempted to develop models to be used to minimise the quantities of excess inventory, in order to reduce their associated costs without compromising both operational efficiency and customers’ needs. The Economic Order Quantity (EOQ) model is one of the most used of these models; however, this model has a number of limiting assumptions, which led to the development of a number of extensions for this model to increase its applicability to the modern-day business environment. Therefore, in this research study, a sustainable inventory management model is developed based on the EOQ concept to optimise the ordering and storage of large-volume inventory, which deteriorates over time, with limited storage space, such as steel, under stochastic demand, supply and backorders. Two control systems were developed and tested in this research study in order to select the most robust system: an open-loop system, based on direct control through which five different time series for each stochastic variable were generated, before an attempt to optimise the average profit was conducted; and a closed-loop system, which uses a neural network, depicting the different business and economic conditions associated with the steel manufacturing industry, to generate the optimal control parameters for each week across the entire planning horizon. A sensitivity analysis proved that the closed-loop neural network control system was more accurate in depicting real-life business conditions, and more robust in optimising the inventory management process for a large-volume, deteriorating item. Moreover, due to its advantages over other techniques, a meta-heuristic Particle Swarm Optimisation (PSO) algorithm was used to solve this model. This model is implemented throughout the research in the case of a steel manufacturing factory under different operational and extreme economic scenarios. As a result of the case study, the developed model proved its robustness and accuracy in managing the inventory of such a unique industry

Modelling and Designing The Model Predictive Control System of Turbine Angular Speed at Hydropowerplant UBP Saguling PT Indonesia Power

Saguling Generation Business Unit (GBU) is one of hydro powerplants under PT. Indonesia Power which has vital role to produce and distribute electricity in Indonesia. The demand for electricity in Indonesia, which is fluctuative, force the plant to operate in immediate and responsive pattern. Saguling need 2 minute to connect to the transmission system from its non operating state. Plant response is controlled by manipulating guide vane opening so the water entering the turbine chamber can be maintained. 5.6 % maximum overshoot still occurs in start up process due to manual mechanism. This paper provide a design of control system using Model Predictive Control (MPC) to optimize the plant performance which is indicated by faster response time and reduced overshoot. Neural Network with Back Propagation algorithm is used to model the turbine with guide vane opening as input variable and turbine angular speed as output variable. The model is then used in MPC algorithm to compute the optimum control signals.Keywords:  Neural Network, Model Predictive Control, Guide Vane, Cost Function.

The use of artificial neural networks (ANN) in food process engineering

Artificial neural networks (ANN) aim to solve problems of artificial intelligence, by building a system with links that simulate the human brain. This approach includes the learning process by trial and error. The ANN is a system of neurons connected by synaptic connections and divided into incoming neurons, which receive stimulus from the external environment, internal or hidden neurons and output neurons, that communicate with the outside of the system. The ANNs present many advantages, such as good adaptability characteristics, possibility of generalization and high noise tolerance, among others. Neural networks have been successfully used in various areas, for example, business, finance, medicine, and industry, mainly in problems of classification, prediction, pattern recognition and control. In the food industry, food processing, food engineering, food properties or quality control, statistical tools are frequently present, and ANNs can process more efficiently data comprising multiple input and output variables. The objective of this review was to highlight the application of ANN to food processing, and evaluate its range of use and adaptability to different food systems. For that a systematic review was undertaken from the scientific literature and the selection of the information was based on inclusion criteria defined. The results indicated that ANN is widely used for modelling and prediction in food systems, showing good accuracy and applicability to a wide range of situations and processes in food engineering.info:eu-repo/semantics/publishedVersio