We propose a solution to a problem that is encountered in defining a notion of full meet base revision, ensuring that the output can always be re-used as an input. For this purpose, we introduce the concept of a separable belief base and show that separable bases are exactly those for which the full meet revision closure operator satisfies the extended set of A. G. M. postulates. We then define...

This study proposes a new multi-item inventory model with hybrid cost parameters under a fuzzy-stochastic constraint and permissible delay in payment. The price and marketing expenditure dependent stochastic demand and the demand dependent the unit production cost are considered. Shortages are allowed and partially backordered. The main objective of this paper is to determine selling price, mar...

• This work aims at fair cost distribution amongst shale gas companies. An MILP model is proposed using the Nash bargaining game theory. Optimal water management strategies. Results indicate savings between 30% and 50% for participating In this work, we address optimal strategies among various companies placed in same play. A mixed integer non-linear programming (MINLP) formulation with objecti...

The Augmented Lagragian Method (ALM) and Alternating Direction Method of Multiplier (ADMM) have been powerful optimization methods for general convex programming subject to linear constraint. We consider the convex problem whose objective consists of a smooth part and a nonsmooth but simple part. We propose the Fast Proximal Augmented Lagragian Method (Fast PALM) which achieves the convergence ...

Abstract—A case study of the generation scheduling optimization of the multi-hydroplants on the Yuan River Basin in China is reported in this paper. Concerning the uncertainty of the inflows, the long/mid-term generation scheduling (LMTGS) problem is solved by a stochastic model in which the inflows are considered as stochastic variables. For the short-term generation scheduling (STGS) problem,...

Support Vector Machine (SVM) theory was originally developed on the basis of a linearly separable binary classification problem, and other approaches have been later introduced for this problem. In this paper it is demonstrated that all these approaches admit the same dual problem formulation in the linearly separable case and that all the solutions are equivalent. For the non-linearly separabl...

Exploiting sparsity is essential to improve the efficiency of solving large optimization problems. We present a method for recognizing the underlying sparsity structure of a nonlinear partially separable problem, and show how the sparsity of the Hessian matrices of the problem’s functions can be improved by performing a nonsingular linear transformation in the space corresponding to the vector ...

In this paper, we integrate goal programming (GP), Taylor Series, Kuhn-Tucker conditions and Penalty Function approaches to solve linear fractional bi-level programming (LFBLP)problems. As we know, the Taylor Series is having the property of transforming fractional functions to a polynomial. In the present article by Taylor Series we obtain polynomial objective functions which are equivalent...