Multiobjective Programming (MOP) has become famous among many researchers due to more practical and realistic applications. A lot of methods have been proposed especially during the past four decades. In this paper, we develop a new algorithm based on a new approach to solve MOP by starting from a utopian point, which is usually infeasible, and moving towards the feasible region via stepwise movements and a simple continuous interaction with decision maker. We consider the case where all objective functions and constraints are linear. The implementation of the pro-posed algorithm is demonstrated by two numerical examples

Reconstruction and/or modification of an already existing fuzzy model with new data may improve system performances. As new data become available, adjusting the existing fuzzy rule-based model may present a challenging alternative to full model reconstruction. In this paper a fuzzy rule-based control model using a Takagi-Sugeno fuzzy system is presented and a model modification algorithm is developed which improves the performance of the initial model as new data become available. Proposed approach is applied to a flood flow forecasting case example and the results are compared with those forecasted using initially available and reconstructed models. Results show that the modified model outperforms the initial FRB model. Reconstructed model performs slightly better than the modified model; however, the reconstruction may not be justified in a real time flood forecasting system, considering the limitations on the available lead time.

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This paper presents a mathematical model for training scheduling for Small and Medium Enterprises (SMEs) in incubators. Based on the proposed model, each training program is specified to be held in the most suitable time by considering the SMEs' requirements and some other logical constraints. Construction of the proposed model is illustrated in details and its implementation is demonstrated via a real numerical example.

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