• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.3
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• pp.39-55
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• 1992

In this research, a Three Level Decomposition technique has been developed for configuration design optimization of truss structures. In the first level, as design variables, behavior variables are used and the strain energy has been treated as the cost function to be maximized so that the truss structure can absorb maximum energy. For design constraint of the optimal design problem, allowable stress, buckling stress, and displacement under multi-loading conditions are considered. In the second level, design problem is formulated using the cross-sectional area as the design variable and the weight of the truss structure as the cost function. As for the design constraint, the equilibrium equation with the optimal displacement obtained in the first level is used. In the third level, the nodal point coordinates of the truss structure are used as coordinating variable and the weight has been taken as the cost function. An advantage of the Three Level Decomposition technique is that the first and second level design problems are simple because they are linear programming problems. Moreover, the method is efficient because it is not necessary to carry out time consuming structural analysis and techniques for sensitivity analysis during the design optimization process. By treating the nodal point coordinates as design variables, the third level becomes unconstrained optimal design problems which is easier to solve. Moreover, by using different convergence criteria at each level of design problem, improved convergence can be obtained. The proposed technique has been tested using four different truss structures to yield almost identical optimum designs in the literature with efficient convergence rate regardless of constraint types and configuration of truss structures.

• Journal of Korean Society of Transportation
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• v.18 no.1
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• pp.75-85
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• 2000

In existing models in optimization, the crisp data improve has been used in the objective or constraints to derive the optimal solution, Besides, the subjective environments are eliminated because the complex and uncertain circumstances were regarded as Probable ambiguity, In other words those optimal solutions in the existing models could be the complete satisfactory solutions to the objective functions in the Process of application for industrial engineering methods to minimize risks of decision-making. As a result of those, decision-makers in location Problems couldn't face appropriately with the variation of demand as well as other variables and couldn't Provide the chance of wide selection because of the insufficient information. So under the circumstance. it has been to develop the model for the location and size decision problems of logistics facility in the use of the fuzzy theory in the intention of making the most reasonable decision in the Point of subjective view under ambiguous circumstances, in the foundation of the existing decision-making problems which must satisfy the constraints to optimize the objective function in strictly given conditions in this study. Introducing the Process used in this study after the establishment of a general mixed integer Programming(MIP) model based upon the result of existing studies to decide the location and size simultaneously, a fuzzy mixed integer Programming(FMIP) model has been developed in the use of fuzzy theory. And the general linear Programming software, LINDO 6.01 has been used to simulate, to evaluate the developed model with the examples and to judge of the appropriateness and adaptability of the model(FMIP) in the real world.