This paper presents an application to the multi-objective stochastic transportation problem in fuzzy environment. In this paper, we focus on our attention to multiobjective stochastic transportation problem involving an inequality type of constraints in which all parameters ( supply and demand ) are log-normal random variable and the objectives are non-commensurable and conflicting in nature. A...

This paper deals with fuzzy goal programming approach to multi-objective linear plus linear fractional programming problem based on Taylor series approximation. In the model formulation of the problem, we first construct the membership functions by determining individual optimal solutions of the objective functions subject to the system constraints. The membership functions are then transformed...

In this investigation, a novel approach is presented to nd the optimum locations and capacity of Flexible AC Transmission Systems (FACTS) devices in a power system using a fuzzy multi-objective function. Maximising the fuzzy satisfaction allows the optimization algorithm to simultaneously consider the multiple objectives of the network to obtain active power loss reduction; i.e., new FACTS devi...

Classical deterministic inventory models consider the demand rate to be either constant or time-dependent but independent from the stock status. However, for a certain type of inventory, the demand rate may be in-fluenced by the stock level. Also in many real-life problems, some products such as fruits, vegetables, phar-maceuticals and volatile liquids continuously deteriorate to evaporation, o...

In this paper, a modified TOPSIS (techniques for order preference by similarity to ideal solution) approach for solving bi-level multi-objective programming (BL-MOP) problems with fuzzy parameters is presented. These fuzzy parameters are assumed to be characterized by fuzzy numerical data, reflecting the experts' imprecise or fuzzy understanding of the nature of the parameters in the problem fo...

The paper presents some main aspects regarding multi-parametric quadratic programming (mp-QP) problems. Model Predictive Control (MPC) is considered as a particular mp-QP problem, and this powerful tool is applied for control and simulation through a case study. Since the solutions to mp-QP problems can be expressed as piecewise affine linear functions of the state, a new implementation in term...

This paper presents a new formulation of multi-instance learning as maximum margin problem, which is an extension of the standard C-support vector classification. For linear classification, this extension leads to, instead of a mixed integer quadratic programming, a continuous optimization problem, where the objective function is convex quadratic and the constraints are either linear or bilinea...