In this paper, we discuss and extend some recent common fixed point results established by using $varphi-$weakly contractive mappings. A very important step in the development of the fixed point theory was given by A.H. Ansari by the introduction of a $C-$class function. Using $C-$class functions, we generalize some known fixed point results. This type of functions is a very important class of ...

Many exact and approximate solution techniques have been used to solve facility location problems and, more generally, supply chain network design problems. Yet, the Large Neighborhood Search technique (LNS) has almost never been suggested for solving such problems, although it has proven its efficiency and flexibility in solving other complex combinatorial optimization problems. In this paper,...

We consider giant gravitons as probes of a class of ten-dimensional solutions of type IIB supergravity which arise as lifts of solutions of U(1)3 gauged N = 2 supergravity in five-dimensions. Surprisingly it is possible to solve exactly for minimum energy configurations of these spherical D3-brane probes in the compact directions, even in backgrounds which preserve no supersymmetry. The branes ...

Minimum concave Cost Network Flow Problems (MCNFPs) arise naturally in many practical applications such as communication, transportation, distribution, and manufacturing, due to economic considerations. In addition, it has been shown that every MCNFP with general nonlinear cost functions can be transformed into a concave MCNFP on an expanded network. It must also be noted, that multiple source ...

The primary purpose of this paper is to identify recent major accomplishments and prophesy (suggest) future trends in the solution, modeling, and human engineering aspects of network and network-related problems. In particular, the suggested future trends consist of an integrated sequence of modeling, theoretical and computational developments which may allow mathematical programming to attain ...

We present a new algebraic algorithmic scheme to solve convex integer maximization problems of the following form, where c is a convex function on R and w1x, . . . , wdx are linear forms on R, max {c(w1x, . . . , wdx) : Ax = b, x ∈ N} . This method works for arbitrary input data A, b, d, w1, . . . , wd, c. Moreover, for fixed d and several important classes of programs in variable dimension, we...