Processing time estimations by variable structure TSK rules learned through genetic programming

Accuracy in processing time estimation of different manufacturing operations is fundamental to get more competitive prices and higher profits in an industry. The manufacturing times of a machine depend on several input variables and, for each class or type of product, a regression function for that machine can be defined. Time estimations are used for implementing production plans. These plans are usually supervised and modified by an expert, so information about the dependencies of processing time with the input variables is also very important. Taking into account both premises (accuracy and simplicity in information extraction), a model based on TSK (Takagi–Sugeno–Kang) fuzzy rules has been used. TSK rules fulfill both requisites: the system has a high accuracy, and the knowledge structure makes explicit the dependencies between time estimations and the input variables. We propose a TSK fuzzy rule model in which the rules have a variable structure in the consequent, as the regression functions can be completely distinct for different machines or, even, for different classes of inputs to the same machine. The methodology to learn the TSK knowledge base is based on genetic programming together with a context-free grammar to restrict the valid structures of the regression functions. The system has been tested with real data coming from five different machines of a wood furniture industry.

Real-time supply chain control via multi-agent adjustable autonomy

Real-time supply chain management in a rapidly changing environment requires reactive and dynamic collaboration among participating entities. In this work, we model supply chain as a multi-agent system where agents are subject to an adjustable autonomy. The autonomy of an agent refers to its capability to make and influence decisions within a multi-agent system. Adjustable autonomy means changing the autonomy of the agents during runtime as a response to changes in the environment. In the context of a supply chain, different entities will have different autonomy levels and objective functions as the environment changes, and the goal is to design a real-time control technique to maintain global consistency and optimality. We propose a centralized fuzzy framework for sensing and translating environmental changes to the changes in autonomy levels and objectives of the agents. In response to the changes, a coalition-formation algorithm will be executed to allow agents to negotiate and re-establish global consistency and optimality. We apply our proposed framework to two supply chain control problems with drastic changes in the environment: one in controlling a military hazardous material storage facility under peace-to-war transition, and the other in supply management during a crisis (such as bird-flu or terrorist attacks). Experimental results show that by adjusting autonomy in response to environmental changes, the behavior of the supply chain system can be controlled accordingly.     

Agile Requirements Engineering Practices: An Empirical Study

An analysis of data from 16 software development organizations reveals seven agile RE practices, along with their benefits and challenges. The rapidly changing business environment in which most organizations operate is challenging traditional requirements-engineering (RE) approaches. Software development organizations often must deal with requirements that tend to evolve quickly and become obsolete even before project completion. Rapid changes in competitive threats, stakeholder preferences, development technology, and time-to-market pressures make prespecified requirements inappropriate.     

Binary BAT algorithm and RBFN based hybrid credit scoring model

Credit scoring is a process of calculating the risk associated with an applicant on the basis of applicant’s credentials such as social status, financial status, etc. and it plays a vital role to improve cash flow for financial industry. However, the credit scoring dataset may have a large number of irrelevant or redundant features which leads to poorer classification performances and higher complexity. So, by removing redundant and irrelevant features may overcome the problem with huge number of features. This work emphasized on the role of feature selection and proposed a hybrid model by combining feature selection by utilizing Binary BAT optimization technique with a novel fitness function and aggregated with for Radial Basis Function Neural Network (RBFN) for credit score classification. Further, proposed feature selection approach is aggregated with Support Vector Machine (SVM) & Random Forest (RF), and other optimization approaches namely: Hybrid Particle Swarm Optimization and Gravitational Search Algorithm (PSOGSA), Hybrid Particle Swarm Optimization and Genetic Algorithm (PSOGA), Improved Krill Herd (IKH), Improved Cuckoo Search (ICS), Firefly Algorithm (FF) and Differential Evolution (DE) are also applied for comparative analysis.

     

An Efficient Approach for Enhancing Contrast Level and Segmenting Satellite Images: HNN and FCM Approach

Satellite image segmentation has gotten bunches of consideration of late because of the accessibility of commented on high-goals image informational indexes caught by the last age of satellites. The issue of fragmenting a satellite image can be characterized as ordering (or marking) every pixel of the image as indicated by various classes, for example, structures, streets, water, etc. In this paper centered to build up a satellite image segmenting process by utilizing distinctive optimization methods. The work is prepared dependent on three stages that are RGB change, preprocessing, and division. At first the database images are assembled from the database at that point select the blue band images by performing RGB change. To improve the differentiation and furthermore decreasing the commotion of these chose blue band images, Hopfield neural network (HNN) is utilized. After image upgrade, the images are fragmented dependent on fuzzy C means (FCM) clustering method. The images are clustered and segmented in the way of optimizing the centroid in FCM utilizing oppositional crow search algorithm. The exhibition of the proposed framework is investigated dependent on the presentation measurements, for example, affectability, particularity and accuracy. From the outcomes, the proposed strategy diminished the computational time by expanding the accuracy of 98.3% with HNN system.

     

Synthesis of zeolite Beta from dense system containing a minimum of template

Zeolite Beta has been synthesized, in 24 h at 170°C, from an extremely dense system in which the weight ratio of solid (sodium aluminate and SiO₂) to liquid (tetraethylammonium hydroxide and H₂O) mixtures is 1 1.8; the product has comparable catalytic properties to those samples prepared by previous methods.