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In this paper, computer‐aided design optimization approach for hydrostatic bearings is presented. The optimization problem is formulated in multicriteria strategy. The power loss and the temperature rise of oil are considered as performance measures for optimal hydrostatic bearing design. Optimization results indicate the effectiveness of the proposed combined approach which has advantages over the single criteria optimization. Sample results are presented to verify the proposed approach.

This chapter develops a methodology to assist critical facility operators in designing physical protection systems to defend against a single adversary (thief, saboteur, terrorist, etc.) attack. The developed methodology utilizes a multicriteria decision-making approach that balances the competing goals of minimal security system cost and maximum system performance. The methodology utilizes a network-based approach to facility security system design and analysis, which locates physical protection (detection, delay, and response) elements throughout a facility. These elements enable the facility owner to prevent attacks through deterrence and to defeat the adversary if he or she chooses to attack. The developed approach results in the ability for the facility operator to assess relative facility and/or infrastructure safety, and make decisions regarding how to optimally allocate resources for physical protection elements to balance cost and performance. A hypothetical example is discussed which demonstrates the usefulness of the developed methodology.

Discusses a new approach to solving optimal designing and control problems in aircraft gas‐turbine engine components. This approach is a combination of optimal designing problems with optimal control problems, allowing the formation of a single problem of optimal designing of controllable systems. The solving of this problem would involve simultaneous optimization of both design parameters and control laws. Allows the making of technically correct and substantiated decisions, taking into consideration several efficiency criteria for gas‐turbine engine components; a specific feature being the determination of a set of competitive optimal solutions in terms of different efficiency criteria values. Demonstrates the effectiveness of this approach by an example of multicriteria design optimization of a controllable axial flow compressor. Presents the results of a search of compressor blade rows geometrical parameters sets and of compressor stator blades control laws which are Edgeworth‐Pareto optimal for four operating modes. Shows a possibility of increasing compressor efficiency considerably by choosing the most preferable design parameters set and implementing in airborne digital control system a number of control laws optimal for different operating modes.

The purpose of this paper is to present the multicriteria identification method used for solving the microscale heat transfer problem. The thin film exposed to ultrashort laser pulse is modeled using the finite difference method. The parameters of the model are tuned on the basis of experimental data. The multicriteria identification of the numerical model parameters is performed for subsets of experimental data.

The multicriteria identification method is used in the paper. The Pareto front for two criterions is created. The two-temperature model of heat transfer in microscale is used in the numerical model.

The multicriteria identification for two subsets of experimental data leads to different results. The obtained Pareto front allows to choose the most suitable set of numerical model parameters.

The multicriteria identification method was used for the first time to solve the microscale heat transfer problem.

This chapter presents two optimization multicriteria models (bi and triple objective) using a lexicographic approach. Solved models are formulated as assignment of workers to different jobs or services of a real hospital, taking into account the available budget and requirements of each job. Presented problems have been solved using AMPL programming language with solver CPLEX v9.1, with the use of branch and bound method for mixed integer mathematical programming.

The multiple objective decision making problem arises when two or more non‐comparable objective functions are to be simultaneously optimised. There is a definite trend towards utilising interactive techniques for solving the multiple objective decision making problem. Interactive techniques allow the involvement of the DM throughout the decision process. In this paper we first provide a brief overview of multiple objective decision making, and then give a survey of literature dealing with interactive multiple objective decision making from 1965 to 1988.

This chapter presents two multicriteria optimization models with bi and triple objectives solved with weighted-sum approach. Solved problems are allocation of personnel in a health care institution. To deal with these problems, mixed integer programming formulation has been applied. Results have shown the impact of problem parameter change for importance of the different objectives. Presented problems have been solved using AMPL programming language with solver CPLEX v9.1, with the use of branch and bound method.

The multiobjective optimization of an eight‐block actively‐shielded solenoid has been performed. After a study of each objective function for simplified configurations, up to three objectives in mutual contrast and 16 design variables have been considered for the full model. In order to obtain a set of equivalent optimal solutions, an evolution strategy of lowest order with a multistart grid has been linked to a Pareto sorting algorithm. Sparsity in both design and objective space and the improvement of all objectives is a feature of the solutions belonging to the Pareto optimal set.

In continuation of the recent development of Evolutionary Structural Optimisation (ESO) applied to the simultaneous objective to maximise the natural frequency and to minimise the mean compliance, presents the Multicriteria ESO optimisation of two new criteria. This has been done with the use of four different multicriteria methods. Three examples have been used to verify the usefulness and capability of these methods applied to ESO in the context of the aforementioned criteria. Concluded that the ESO weighting method is proficient in presenting the designer with a range of options (of Pareto attribute) taking into account multiple criteria, and the global criterion method has the tendency to produce shapes and topologies that resemble that of the weighted 50 per cent: 50 per cent method. Likewise, the logical OR operator method produced designs that corresponded directly to those of 100 per cent stiffness weighted criteria. No clear resemblance could be concluded with the case of the logical AND operator method.

The purpose of this paper is to introduce a new forecasting approach that involves a multicriteria scoring model, which is enhanced with regression analysis and optimization. We compare regression analysis versus our Enhanced Multicriteria Scoring Model by comparing the Error Sum of the Squares in case studies involving top selling automobiles and top Fortune 500 companies. In both the automobile and Fortune 500 case studies, our Enhanced Multicriteria Scoring was more accurate than regression analysis. In practice, our Enhanced Multicriteria Scoring Model should be compared with regression analysis, and the better of the two techniques should be used to forecast. In short, our Enhanced Multicriteria Scoring model is a “breakthrough” modeling technique that will help companies and organizations improve their forecasting.