Optimal building evacuation time considering evacuation routes

Die design is heavily experience based and the die design process is an iterative procedure of trial and error in order to obtain a final die design for the successful manufacture of stampings. Most automotive industries use internal guidelines and past experience for die design. Even though powerful computer-aided design systems are being used in automotive industry, the lack of adequate analysis tools at the initial die geometry design stage hinders the die manufacturing process, and also necessitates lead times of the order of 5-30 weeks [1]. At the concept design stage, and during the initial die development process, the variations in geometry and process conditions are so large that it is prohibitively expensive to use 3D finite element analysis. The complexity of die design heuristic knowledge hinders the development and application of knowledge-based systems. Hybrid intelligent systems are computer programs in which at least one of the constituent models simulates intelligent behaviour [2]. These models could be knowledge-based systems, artificial neural networks, fuzzy logic systems, etc. In this approach both artificial neural networks, knowledge-based systems and finite-element analysis (FEA) for modelling the design process are used. A simulation-based design approach [3] for the die design process is followed. Artificial neural networks (ANNs) are preliminary design tools which indicate the formability of the component geometry, for the selected process and material conditions, The ANN module is trained from FEA results for a generic set of component geometries, process conditions, and material properties. The final die design validation is carried out by FEA. The intelligent framework incorporates rules for material selection, process parameter selection and their modification. Component geometry is a critical parameter which affects the manufacturability of the given part. Hence, an intelligent geometry handling module, which automatically modifies and optimises the geometry of the designed die, is implemented in the present system. Knowledge-based blackboard architecture is used for the integration of various analysis models such as CAD, FEA, and ANN, as an intelligent framework for die design [4]. The hybrid intelligent system provides an integrated decision support environment for simulation and analysis of the forming process, both during the initial die design phase and during the die tryout phase. The hybrid intelligent systems approach supports the capability for automatic evaluation of prospective die design for manufacturability, and performs automatic modification of design inputs. Applications of the hybrid intelligent system for die design are described together with a comparison with shop floor data.

A Hybrid Intelligent Systems Approach for Die Design in Sheet Metal Forming

Resource allocation in state-dependent emergency evacuation networks

Decision making in a semistructured environment often involves the use of quantitative, structured analysis along with the qualitative judgment of an expert. Decision support systems and expert systems are often developed to assist in this judgment process. The hybrid information system described in this paper combines a statistical model with a rule-based expert system in order to integrate the quantitative and qualitative aspects of decision making. The GC Advisor hybrid system is designed for use by auditors to assess the ability of the client firm to continue as a going concern. The guidelines for expert system validation given in previous literature are then applied to the validation of GC Advisor.

The design and validation of a hybrid information system for the auditor's going concern decision

https://works.bepress.com/davood_golmohammadi/1/download/

Estimation of the evacuation time in an emergency situation in hospitals

Many intelligent computing techniques have been developed over the last decade. Some of these include neural networks, fuzzy systems, genetic algorithms, rule-based expert systems, inductive expert systems, and the wide array techniques lumped under the heading of artificial intelligence. The application of these intelligent computing techniques to support planning and control in manufacturing can be done at three levels: organization, coordination, and execution. Because of the different nature of the problems at each level, different types of solutions may be required. Until recently, problem solvers have typically used single-technique-based tools to build these solutions, e.g., an expert system based solution, a neural network based solution, or a linear programming solution. Simple problems that fit the assumptions of a single-technique-based solution may be easily solved by such an approach. However, most real world manufacturing problems are not simple. They may neither fit the assumptions of a single technique nor be effectively solved by the strengths and capabilities of a single technique. One approach to deal with these complex real world problems is to integrate the use of two or more techniques in order to combine their different strengths and overcome each other’s weaknesses to generate hybrid solutions.

Hybrid intelligent systems: Tools for decision making in intelligent manufacturing

A simulation method is presented which enables a modeler to identify dangerous bottlenecks for emergency building evacuation and to test the sensitivity of occupant exit behavior to evacuation improvement strategies. The modeling method is tested against landmark evacuation data of Pauls and Jones, and used to predict problems and solutions for a complex evacuation situation involving a large university building with multiple exit choices. Some concluding remarks are addressed to a future research agenda for this method.

Jay Weinroth

Papers

Hybrid intelligent systems: Tools for decision making in intelligent manufacturing

An adaptable microcomputer model for evacuation management