• Proceedings of the KSME Conference
• /
• 2008.11a
• /
• pp.1187-1192
• /
• 2008

In this paper, a versatile multi-objective optimization concept for fatigue life prediction is introduced. Multi-objective decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.33 no.2
• /
• pp.114-126
• /
• 2009

In this paper, a versatile multi-criteria optimization concept for fatigue life prediction is introduced. Multi-criteria decision making in engineering design refers to obtaining a preferred optimal solution in the context of conflicting design objectives. Compromise decision support problems are used to model engineering decisions involving multiple trade-offs. These methods typically rely on a summation of weighted attributes to accomplish trade-offs among competing objectives. This paper gives an interpretation of the decision parameters as governing both the relative importance of the attributes and the degree of compensation between them. The approach utilizes a response surface model, the compromise decision support problem, which is a multi-objective formulation based on goal programming. Examples illustrate the concepts and demonstrate their applicability.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.4
• /
• pp.347-352
• /
• 2002

This paper propose the Fuzzy AHP(Analytic Hierarchical Process)-based negotiation support (FAHP-NEGO) mechanism to support the dynamic negotiation process in Electronic Commerce(EC). Negotiation is a form of decision-making with two or more actively involved agents who can not make decisions independently, and therefore must make concessions to achieve a compromise. Having concerned that point, the theoretical framework of FAHP-NEGO mechanism is presented by means of fuzzy membership functions and AHP. This mechanism encompasses both qualitative and quantitative conditions, and the use of multiple negotiation procedures for solving the electronic negotiation problem, adjusting the fuzzy membership function, and restructuring the problem representation. A hypothetical example of a healthcare products purchase is given to illustrate the quality of the proposed mechanism. The results showed that the Fuzzy AHP-based negotiation support mechanism could reflect both qualitative and quantitative conditions in EC. The implications of the study for future directions of research on electronic negotiation support modeling and systems are presented.

• Journal of Mechanical Science and Technology
• /
• v.20 no.3
• /
• pp.366-375
• /
• 2006

In this paper, it is intended to introduce a method to solve multi-objective optimization problems and to evaluate its performance. In order to verify the performance of this method it is applied for a vertical roller mill for Portland cement. A design process is defined with the compromise decision support problem concept and a design process consists of two steps: the design of experiments and mathematical programming. In this process, a designer decides an object that the objective function is going to pursuit and a non-linear optimization is performed composing objective constraints with practical constraints. In this method, response surfaces are used to model objectives (stress, deflection and weight) and the optimization is performed for each of the objectives while handling the remaining ones as constraints. The response surfaces are constructed using orthogonal polynomials, and orthogonal array as design of experiment, with analysis of variance for variable selection. In addition, it establishes the relative influence of the design variables in the objectives variability. The constrained optimization problems are solved using sequential quadratic programming. From the results, it is found that the method in this paper is a very effective and powerful for the multi-objective optimization of various practical design problems. It provides, moreover, a reference of design to judge the amount of excess or shortage from the final object.