[in Japanese]

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Mathematical programming has been applied to various fields in everyday life. However for many actual problems, the assumption that the parameters involved in the problem are deterministic known data is often unjustified. These data contain uncertainty and are thus represented as random variables, since they represent information about the future. Decision-making under conditions of uncertainty involves potential risk. In this paper, we consider a stochastic programming problem in which some parameters are defined as random variables. The electric power industry is undergoing restructuring and deregulation, and it is necessary to incorporate uncertainties such as the level of electric power demand and availability of power generators into the optimization problem in the electric power industry. The purpose of this paper is to show algorithms to solve stochastic programming problems and their applications to optimization problems in the electric power industry. These examples clearly show stochastic programming to be very valuable for solving the optimization problem in the electric power industry.

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In this paper, we propose a method to use some kinds of membership functions (MSFs) efficiently to improve an optimization of fuzzy reasoning using a steepest descent method. In fuzzy reasoning, there are many problems, for example, rapid increase of the number of rules and large scale change of fuzzy system as the number of inputs increases. To overcome these problems, we add a technique of genetic algorithm to the optimization of fuzzy reasoning using the steepest descent method. In a technique of genetic algorithm, this new method can select some kinds of MSFs, delete some lengthy rules, and optimize MSFs. In addition, this new method can improve generalization ability as a result of selection of MSFs adapting to the model. The advantages of this new method are demonstrated by numerical examples involving function approximations.

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The modeling of user's behavior pattern for personalized information services in mobile environment has recently become a popular research theme. Most of the researches aim at predicting user's future behavior (and/or location) by extracting frequent patterns from the history of location data sequences. However, sometimes user's behavior changes according to the external information such as date, time, weather etc. and we can not accurately predict it based on the location data sequences only. In this paper, we propose a new prediction method including date and time as external information. First the user's travel history (location, date, time) is stored. Then, from the external information, time/date categories that have correlation to the user's destination based on entropy are selected. Using the time/date categories, a destination which depends on the external information is successfully predicted. An application of the method to a data collected from a car navigation system showed an improved performance comparing to the conventional prediction methods. Higher destination prediction accuracy during the first several minutes after user's departure was reported.

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In this paper, we present a decision making method based on the chance constrained condition programming in the stochastic programming for the case that random variable coefficients are included in two-level linear programming problems where a decision maker exists at the upper level and the other decision maker does at the lower level. To be more specific, first, we reduce a two-level stochastic linear programming problem to an ordinary two-level linear programming problem on the basis of a variance minimization model considering expectations in the chance constrained condition programming. Then, we consider interactive fuzzy programming to derive a satisfactory solution for the decision maker at the upper level through interaction in consideration of the balance between the satisfactory level of the decision maker at the upper level and that of the decision maker at the lower level under the assumption that the decision maker at the upper level and one at the lower level have motivation to cooperate mutually.

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