In the chapter of improving banking supervision, we analyze the games with complete and incomplete information between supervisors and banks, and their strategies therein during the operating and market exiting stages. We come to the conclusion that in the long run, the supervision of operational risks should progress from direct surveillance to indirect surveillance. In the chapter of incentives and constraints, we first discuss the reform of personnel system (non- monetary rewards and punishments) and salary system in the SOBs, demonstrated with an optimal model. Then we show that it is feasible for senior executives of SOBs to bear a part of operational risk loss through a model of guarantee and compensation. It is a bold attempt in current situation when domestic liability insurance market is still not mature.

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Based on the method of multi-objective fuzzy optimization and the overhead traveling crane design, a mathematical model of fuzzy optimization for main box beam section is established. A fuzzy goal anthology is constructed. The fuzziness of multi-objective function and constraints is defined. The transformation of the fuzzy constraints set to normal constraints set in real number domain is realized by applying optimal level cut set method. The optimal solution and optimal constraint value of sub-objective function in the feasible field are found using the genetic algorithms.

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There exits several MDO algorithms. But they are in our opinion either nonefficient or complicated. So we devised an algorithm called Subspace Approximation Optimization . In the SAO algorithm, the whole system is decomposed into one system-level optimization and several disciplinary optimizations so that a large and complicated problem can be divided into several easy-solving sub-problems. The coupling relationships and the coordination among disciplines are presented by equality constraints and these equality constraints are assigned to relevant disciplines. The optimums of design variables in system level optimization are transferred to discipline level optimization. The optimums of design variables in discipline level correspond to the point that is the nearest to the optimums of design variables in system level. If the optimums of design variables in system level are out of feasible region of discipline 1eve1, linear constraints can be built in the system level optimization using the design variable optimums obtained by the discipline level optimization. The system level optimization would improve the design of the whole system with these linear constraints.

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feasible direction：可行方向;容許方向

可行約束;可行制限 feasible constraints | 可行方向;容許方向 feasible direction | 可行區(qū)域 feasible region