generally, an engineering design problem has multiple objective functions. some of these problems can be formulated as multiobjective geometric programming models. on the other hand,often in the real world, coefficients of the objective functions are not known precisely. coefficients may be interpreted as fuzzy numbers, which lead to a multiobjective geometric programming with fuzzy parameters....

fuzzy linear programming models and methods has been one ofthe most and well studied topics inside the broad area of soft computing. itsapplications as well as practical realizations can be found in all the real worldareas. in this paper a basic introduction to the main models and methods infuzzy mathematical programming, with special emphasis on those developedby the authors, is presented. as ...

Recently, fuzzy linear programming problems have been considered by many. In the literature of fuzzy linear programming several models are offered and therefore some various methods have been suggested to solve these problems. One of the most important of these problems that recently has been considered; are Fully Fuzzy Linear Programming (FFLP), which all coefficients and variables of the prob...

In this paper we show semideenite programming (SDP) could be a resource for large dense systems. The preconditioned conjugate gradient method is used to solve fully dense linear systems during each iteration of an optimization algorithm. An incomplete Cholesky factorization is proposed, and some numerical results are obtained. These results generate better lower bounds for the quadratic assignm...

This research studies two computational techniques that improve the practical performance of existing implementations of interior point methods for linear programming. Both are based on the concept of symmetric neighbourhood as the driving tool for the analysis of the good performance of some practical algorithms. The symmetric neighbourhood adds explicit upper bounds on the complementarity pai...

We present a methodology which uses a collection of workstations connected by an Ethernet network as a parallel processor for solving large-scale linear programming problems. On the largest problems we tested, linear and super-linear speedups have been achieved. Using the "branch-and-cut" approach of Hoffman, Padberg and Rinaldi, eight workstations connected in parallel solve problems from the ...

In this paper we propose a new decomposition method for large scale linear programming. This method dualizes an (arbitrary) subset of the constraints and then maximizes the resulting dual functional by dual ascent. The ascent directions are chosen from a finite set and are generated by a truncated version of the painted index algorithm [13]. Central to this method is the novel notion of (e,8)co...

cost reduction is one of the methods applied for improving the productivity of organizations. in productivity literature, particularly in nonparametric methods, cost reduction related methods are regarded as input oriented models. this paper presents a supply chain management (scm) model in which purchasing iron ore and coke from different resources, along with production and distribution of st...

this paper presents a genetic algorithm (ga) for solving a generalized model of single-item resource-constrained aggregate production planning (app) with linear cost functions. app belongs to a class of pro-duction planning problems in which there is a single production variable representing the total production of all products. we linearize a linear mixed-integer model of app subject to hiring...